Table of Contents

1. Cover

2. Foreword

3. PREFACE: Is There A Bubble In Boom-Bust Books?

4. Acknowledgments

5. INTRODUCTION: The Study of Financial Extremes

1. Secrets vs. Mysteries

2. Different Problems Necessitate Different Approaches

3. Uncovering a Mystery

4. Notes

6. PART I: Five Lenses

1. CHAPTER 1: Microeconomic Perspectives

1. “Random Walks” and Accurate Prices: The Efficient Market Hypothesis

2. Unstable and Inefficient: The Theory of Reflexivity

3. Consolidating Two Factors of Detection

4. Notes

2. CHAPTER 2: Macroeconomic Perspectives

1. The Magnifying Power of Leverage

2. Collateral Rates and Debt Dynamics

3. Hyman Minsky's Financial Instability Hypothesis

4. Debt Deflation and Asset Prices

5. Quantitative Easing

6. The Austrian Business Cycle Theory

7. Integrating the Macro Lenses

8. Notes

3. CHAPTER 3: The Psychology Lens

1. The Study of Irrationality Is Born

2. Heuristics Gone Wild: How Rules of Thumb Lead Us Astray

3. Our Flawed Brains: Other Cognitive Issues

4. The Certainty of Uncertainty

5. Notes

4. CHAPTER 4: Political Foundations

1. Can Anyone Own Anything?

2. Venezuelan and Soviet Nationalization: Communism at Work

3. Prices: To Guide or Be Guided?

4. Political Distortions of Property and Price

5. Tariffs and Trade Wars

6. Notes

5. CHAPTER 5: Biological Frameworks

1. Revealing the Maturity of an Unsustainable Boom

2. How Micro Simplicity Drives Macro Complexity

3. Emergent Behavior in Human Swarms

4. The Blind Leading the Blind

5. Notes

7. PART II: Historical Case Studies

1. CHAPTER 6: Tulipomania

1. The Uniqueness of Tulips

2. Fertile Soil for Bubble Formation

3. The Boombustology of Tulipomania

4. The Multilens Look

5. Notes

2. CHAPTER 7: The Great Depression

1. Castles in the Sand

2. From Booming Twenties to Busted Thirties

3. The Boombustology of the Great Depression

4. The Multilens Look

5. Notes

3. CHAPTER 8: The Japanese Boom and Bust

1. Japan(ese) as Different

2. An Overview of the Bubble Economy

3. The Boombustology of the Japanese Boom and Bust

4. The Multilens Look

5. Notes

4. CHAPTER 9: The Asian Financial Crisis

1. Boom Times in East Asia

2. Thailand Catches the Flu

3. The Boombustology of the Asian Financial Crisis

4. The Multilens Look

5. Notes

5. CHAPTER 10: The U.S. Housing Boom and Bust

1. “Safe as Houses”

2. The Music Stops

3. The Boombustology of the U.S. Housing Boom and Bust

4. The Multilens Look

5. Notes

6. CHAPTER 11: China's Credit-Fueled Investment Boom

1. Tendencies toward Equilibrium

2. Leverage, Cheap Money, and Potential Deflation

3. Conspicuous Consumption and Overconfidence

4. Rights, Moral Hazard, and Political Distortion

5. Consensus, Silent Leadership, and Epidemics

6. The Unsustainable Chinese Story

7. The Boombustology of China's Credit-Fueled Investment Boom and Bust

8. The Multilens Look

9. Notes

8. PART III: Looking Ahead

1. CHAPTER 12: Spotting Bubbles Before They Burst

1. Reflexivity and Self-Fulfilling Dynamics

2. Leverage, Financial Innovation, and Cheap Money

3. Overconfidence

4. Policy-Driven Distortions

5. Epidemics and Emergence

6. Conclusions

7. Notes

2. CHAPTER 13: Boombustology in Action

1. Distinguishing Potential and Reality

2. Capital Markets Considerations

3. Population, Debt, and Investment

4. India's Versailles

5. Culture, Vocation, and Underdevelopment

6. Automation: Why It's Too Late for India

7. “Make in India”: The Wrong Playbook for Development

8. India’s Elusive Middle Class: Disappointment Ahead?

9. Notes

9. CONCLUSION: Hedgehogs, Foxes, and the Dangers of Making Predictions

1. Blinded by Focus

2. Notes

10. ADDENDUMA: Passive Investing Bubble?

1. Feedback Loops and Reflexivity

2. Distortion of the Market

3. Oversimplification as Overconfidence

4. Investment and Interest Rates

5. Herd Mentality and Silent Leadership

6. Too Much of a Good Thing

7. Notes

11. About the Author

12. Index

13. End User License Agreement

List of Tables

1. Chapter 1

1. Table 1.1 The Three Forms of Market Efficiency

2. Chapter 2

1. Table 2.1 Debt's Amplification Power

2. Table 2.2 Local Bank ABC Loan Rates

3. Table 2.3 Comparing the Safe Smiths, Optimistic Osbornes, and Carefree Ca...

4. Table 2.4 Happy Times and Sad Times

5. Table 2.5 Very Happy Times and Very Sad Times

6. Table 2.6 Minsky's Debt Descriptions

3. Chapter 3

1. Table 3.1 A Simple Knowledge Test

2. Table 3.2 Answers to the Simple Knowledge Test

3. Table 3.3 Human Irrationality and Financial Booms and Busts

4. Chapter 4

1. Table 4.1 Selected Nationalizations since 2000

2. Table 4.2 Annual World Trade Volumes, 1929–1933

5. Chapter 6

1. Table 6.1 Total Mint Output in Seventeenth-Century Southern Netherlands (Guilder...

2. Table 6.2 The Bank of Amsterdam's Balance Sheet Balloons (Currency: Flori...

3. Table 6.3 The Five-Lens Approach to Tulipomania

6. Chapter 7

1. Table 7.1 Miami Bank Clearings

2. Table 7.2 The Big Bull Market

3. Table 7.3 The Rapid Fall of Glamour Stocks

4. Table 7.4 The Five-Lens Approach to the Great Depression

7. Chapter 8

1. Table 8.1 Selected Sector Valuations

2. Table 8.2 The Five-Lens Approach to the Japanese Boom and Bust

8. Chapter 9

1. Table 9.1 The Five-Lens Approach to the Asian Financial Crisis

9. Chapter 10

1. Table 10.1 The Five-Lens Approach to the U.S. Housing Boom and Bust

10. Chapter 11

1. Table 11.1 The Five-Lens Approach to China's Property Boom and Bust

11. Chapter 12

1. Table 12.1 Signs Revealing Reflexive Dynamics Toward Disequilibrium

2. Table 12.2 Signs Revealing Leverage Excesses

3. Table 12.3 Overconfidence Indicators

4. Table 12.4 Policy Distortion Indicators

5. Table 12.5 Indicators of Emergence and Epidemics

6. Table 12.6 Boombustology Road Map

12. Conclusion

1. Table C.1 Unicorn Dairy Inc. Hypothetical Performance

13. Addenduma

1. Table A.1 The World's 10 Largest ETFs and Their Ownership of Apple

List of Illustrations

1. Chapter 1

1. Figure 1.1 Equilibrium in Pictures

2. Figure 1.2 Natural vs. Social Science: Key Observational Distinctions

3. Figure 1.3 Reconciling Efficiency and Reflexivity

2. Chapter 2

1. Figure 2.1 The Hayekian Triangle and Stages of Production

2. Figure 2.2 How Interest Rates Drive Investment and Consumption Decisions

3. Figure 2.3 Overconsumption and Overinvestment Driven by Inappropriately Low I...

4. Figure 2.4 Credit Cycles Drive Constant Instability

3. Chapter 5

1. Figure 5.1 Infection Rate > Removal Rate = 0%

2. Figure 5.2 Infection Rate > Removal Rate > 0%

4. Chapter 7

1. Figure 7.1 Real S&P Composite Index as a Multiple of 10-Year Moving Average E...

2. Figure 7.2 Total Money Supply During the 1920s

5. Chapter 8

1. Figure 8.1 Japan's Nikkei 225 Index in the 1980s

2. Figure 8.2 Bursting of the Bubble Economy

6. Chapter 9

1. Figure 9.1 The Performance of Selected Asian Stock Markets in 1993

2. Figure 9.2 Indonesian Stock Market, 1995–1999

3. Figure 9.3 The Five Cs

7. Chapter 10

1. Figure 10.1 Shiller's Home Price Index vs. Building Cost Index

2. Figure 10.2 Shiller's Home Price Index vs. Population

3. Figure 10.3 Shiller's Home Price Index, 1890–2018

4. Figure 10.4 Telling People What They Want to Hear: Popular Sentiment Exemplif...

5. Figure 10.5 Rapid Growth in the Number of Subprime and Alt-A Loans

6. Figure 10.6 Exponential Growth in Risky Loans

7. Figure 10.7 Membership in the National Association of Realtors, 1987–2007...

8. Figure 10.8 Membership in the National Association of Realtors, 1916–1936...

8. Chapter 11

1. Figure 11.1 Outstanding Loans in China, 2000–2010

2. Figure 11.2 Price-to-Income Ratios for Several Chinese Cities

3. Figure 11.3 Chinese Steel Production (MM Tons) and Global Market Share

9. Chapter 13

1. Figure 13.1 Foreign Direct Investment into India, by Month

“Boombustology offers a compelling and pragmatic framework for understanding the forces that propel markets to excessive levels in boom times only to see them bust when those forces no longer apply. In this very enjoyable book, Vikram Mansharamani takes the reader on a tour through some of the most notable booms/busts in history, illustrating how one can view them through the application of his microeconomic, macroeconomic, psychological, political, and biological lenses. The analysis is robust.”

—SANDRA URIE, Chairman Emeritus & former Chief Executive Officer, Cambridge Associates LLC

“Far too few people take bubbles seriously until it's too late. Vikram Mansharamani's book should encourage investors and borrowers alike to question whether they're just riding a wave they don't understand. And whether, at the back of their minds, they are relying on being part of a big enough crowd to get bailed out by a kindly taxpayer if things turn sour… . Either way, Boombustology should provoke thought about the context in which some investments and loans are made.”

—SIR PAUL TUCKER, Chair, Systemic Risk Council; author of Unelected Power: The Quest for Legitimacy in Central Banking and the Regulatory State; former Deputy Governor, Bank of England

“Vikram Mansharamani has written a book that makes economic theory vividly accessible—while providing innovative and nuanced analysis that will challenge the assumptions of even the most seasoned experts. He forces readers to think about how booms and subsequent busts move across the globe and across markets, becoming more exaggerated as more capital chases fewer opportunities. Highly recommended!”

—KIM Y. LEW, Chief Investment Officer, Carnegie Corporation of New York

“Boombustology presents a practical approach to thinking about a world that is characterized by massive global economic uncertainty. While many academics conduct thoughtful analysis of market dynamics, Mansharamani provides a useful framework for investors, allocators, and their advisors to better appreciate the context within which they make their decisions. A must-read.”

—LORI VAN DUSEN, Founder & Chief Executive Officer, LVW Advisors

“By employing an unusually diverse set of perspectives to increase understanding of the character of financial crises, Mansharamani gives his readers a valuable set of guideposts to help them find a safe path through future market disruptions.”

—DAVID F. SWENSEN, Chief Investment Officer, Yale University

“Asset class bubbles are by far the most important events in the life of both stock markets and economies. Boombustology is a comprehensive, informative, and entertaining look into how bubbles form, what really constitutes a bubble, and, critically, the causes and circumstances of bubbles breaking. It is essential reading for any bubbliophile.”

—JEREMY GRANTHAM, Co-founder & Chief Investment Strategist, GMO

“If there's one thing that cleaves Wall Street's winners from its losers, it's the ability to say, ‘I've seen this movie, and I know how it ends.' Vikram Mansharamani's Boombustology will entertain readers with an entire film festival of past, present, and future investment disaster flicks that will make them laugh, make them cry, and save their bacon.”

—WILLIAM J. BERNSTEIN, bestselling author of The Investor's Manifesto: Preparing for Prosperity, Armageddon, and Everything in Between

“Investors are attracted to bubbles. To paraphrase Orwell, they find themselves playing with fire without even knowing that fire is hot. Vikram Mansharamani's highly readable book will set them right. It provides a sound intellectual framework for identifying bubbles before they burst. If you buy only one investment book this year, make it Boombustology.”

—EDWARD CHANCELLOR, author of Devil Take the Hindmost: A History of Financial Speculation

“Through an artful and accessible melding of theoretical frameworks and historical cases, Boombustology provides the essential tools to identify and navigate the financial booms and busts that can be so hazardous to one's portfolio. Required reading for all investors.”

—PAUL A. REEDER, President, PAR Capital Management

“Who is the better investor: the generalist or the expert specialist? In this spirited book, Vikram Mansharamani brings to life five famous boom-bust episodes and argues for a multidisciplinary, generalist approach. Boombustology is an engaging read for specialists and generalists alike.”

—JOHN GEANAKOPLOS, James Tobin Professor of Economics at Yale University; External Professor, the Santa Fe Institute; and Partner, Ellington Capital Management

BOOM BUSTOLOGY

SPOTTING FINANCIAL BUBBLES BEFORE THEY BURST

SECOND EDITION

Vikram Mansharamani, PhD

Copyright © 2019 by Vikram Mansharamani. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

Published simultaneously in Canada.

No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750–8400, fax (978) 646–8600, or on the Web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748–6011, fax (201) 748–6008, or online at www.wiley.com/go/permissions.

Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

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Library of Congress Cataloging-in-Publication Data

Names: Mansharamani, Vikram, author.

Title: Boombustology : spotting financial bubbles before they burst / Vikram Mansharamani, PhD.

Description: Second edition. | Hoboken, New Jersey : Wiley, [2019] | Includes bibliographical references and index. |

Identifiers: LCCN 2019005873 (print) | LCCN 2019007457 (ebook) | ISBN 9781119575597 (ePub) | ISBN 9781119575627 (ePDF) | ISBN 9781119575603 (Hardcover)

Subjects: LCSH: Business cycles. | Financial crises. | Business forecasting.

Classification: LCC HB3711 (ebook) | LCC HB3711 .M354 2019 (print) | DDC 338.5/42—dc23

LC record available at https://lccn.loc.gov/2019005873

Cover Design: Wiley

Cover Images: © akinshin/iStock.com, © BrianAJackson/iStock.com

Foreword

Money is not humanity's best subject, a proposition whose truth I can easily demonstrate. Imagine if, upon Cleopatra's death in 30 BC, some public-spirited heir to the Egyptian ruler had made a gift to posterity of a $100 deposit in the Bank of Perpetuity. “Just reinvest the interest at 2% forever and ever,” the donor would have told the teller, “and don't allow a single withdrawal.” So it was done.

This benefaction, seemingly a trifle on the day of deposit, would by now have grown to $41,034,747,782,825,800,000.00. Expressed more manageably, it would represent 58.5 doublings of the original $100, or $5,351,505,546.32 for each of the citizens of the world, of whom, at last count, there were 7,667,888,490. Imagine it: everyone on the face of the earth a billionaire five times over.

But, of course, there was not then, and is not now, any Bank of Perpetuity. Leveraged financial institutions are usually as mortal the people who manage them. Besides, as Vikram Mansharamani illustrates throughout this grand tour of financial thought and financial history, human progress is not continuous but cyclical. Humanity builds up only to tear down. Booms give way to busts, and vice versa. Compound interest is, indeed, a marvel, but we earthlings have to eat and pay the landlord. So it is that wealth, rather than piling higher and higher from generation to generation, is most often consumed by the generation that produces it (or its immediate, temporarily grateful, heirs).

Some may object that with, first, the Industrial Revolution, and, second, today's digital revolution, progress has indeed become cumulative. And so it may be in science and technology. But with regard to investing, central banking, and economic policy-making generally, progress still appears illusory. Buying high and selling low, chasing the hot new mutual fund, piling on leverage, putting in place legal incentives to risky and counterproductive action—we humans seem to keep stepping on the same rakes.

In academic circles, there is a certain scorn for the notion of cyclicality, whether in the stock market averages, the credit markets, or real estate. Asset prices move randomly, one strain of financial thought has it, and markets are quick to absorb and reflect all available information. The upshot is that you, whoever you are and whatever you think you may know, have no real chance of outperforming the S&P 500 or the corresponding bond or real estate index. This being the case, you should abandon security analysis—indeed, even the reading of this informative volume—and send your money to the index fund of your choice.

Mansharamani does some of his best work on the red-hot topic of indexation and on the related question of the “efficiency” of markets. Reading these accounts of boom and bust, whether in contemporary China and Japan or in seventeenth-century Holland or in 1930s America, I am reinforced in my conviction that markets are just as efficient as the people who operate in them—just as unflappable, clear-sighted, rational, and coolly calculating. Which is only to say that markets, like people, are sometimes off their rockers. How to identify these flights of fancy is the principal theme of this volume.

Boombustology may also be read as a persuasive brief for the liberal arts. For anyone who has chosen not to disappear down the rabbit hole of hyper-specialization, Mansharamani has words of hope and encouragement. The more varied the knowledge and experience that one can bring to bear on a problem, he observes, the better one's chances of solving it.

Those odds against success in investment are, in any case, daunting, if only because of the biases we bring to simple acts of perception. Seeing clearly is hard enough even when considerations of money aren't fogging one's mental spectacles. To support this contention, allow me to refer you to the story of the discovery, in 1991, of a 5,300-year-old corpse in a melting glacier in the Italian Alps. Teams of forensic doctors performed repeated X-rays and CAT scans on the Copper Age remains. They could see no signs of external injury, though signs of broken ribs led them to conjecture that Iceman, as they called the specimen, was the victim of foul play.

Only belatedly did one of the examining physicians notice the flint arrowhead lodged less than an inch from Iceman's left lung. The discovery chagrined the many examiners who had missed it. “This is just absolutely astounding to me,” said one, “that this little thing is truly there after all the scrutinizing we've done over these images. We've pulled our hair out and fussed and fretted and tried to drag all these details out, and yet there's probably an arrowhead in there. It makes a person very humble.”

How much humbler must be the seeker of financial evidence. At least, in the case of a forensic examination, no stock options hang in the balance, and the examiners are neither long nor short the body on the examining table. It's a very different proposition in the emotionally charged business of buying low and selling high.

The somewhat downcasting truth of the matter, as Mansharamani observes, is that people in markets bear an alarming resemblance to social insects: bees or locusts or ants. They cluster and swarm because independent thought (given the financial stakes) is too frightening to attempt.

Just after the Armistice that ended World War I, Hugh S. Johnson, known to history as the chief of Franklin Roosevelt's ill-fated National Recovery Administration, was conversing with a federal prisoner. Johnson at the time was a brigadier general in the Army. The prisoner was a well-educated associate of the communist Leon Trotsky.

Johnson asked the captive how the communist movement could possibly take hold in the United States:

“Did you ever see in a sunbeam, in a wood on a still and sultry day, millions of midges hovering in a swarm?” the prisoner asked (as Johnson related in his 1935 memoir, The Blue Eagle: From Egg to Earth”).

“Yes,” Johnson replied.

Did you ever see them suddenly move over to one side or the other, say three feet—all preserving the same distance and relative position?”

“Yes.”

“What made them do that?”

“Well, a breeze perhaps.”

The prisoner corrected his captor: “I said, a still day—but did you ever see them move back after just a few minutes?”

Johnson indicated that he had.

“Well, what made them do that? I'll tell you. There are mass movements in human psychology—just as there are inexplicable mass movements in all sorts of life.”

Mansharamani's sentiments, exactly.

—James Grant

Editor

Grant’s Interest Rate Observer

New York, New York

December 4, 2018

PREFACE

Is There A Bubble In Boom-Bust Books?

While I sincerely hope that Boombustology becomes a timeless classic for students, academics, policymakers, and investors alike, my current goal is considerably more modest. I have written this book because I believe it useful. The world is in the midst of an accelerating sequence of boom and bust cycles, and despite these developments, no organized, multidisciplinary framework exists for thinking about them. This book hopes to provide that framework. Lacking such a framework, we are destined to a world of massive unintended consequences and the continual escalation of extremes—the ultimate outcome of which may be quite destructive to society and the socioeconomic-political world as we know it.

Might it be possible that our attempts to deal with apparent Japanese economic dominance resulted in the Japanese bust, which drove the Asian financial crisis, which drove the dotcom bubble, which resulted in the U.S. housing boom and bust, which is currently creating unsustainable debt loads at the government level around the world? Might it have been possible to identify these booms before they busted to prevent the numerous unintended consequences that follow in the wake of our attempts to address each bust? This book will address these topics.

The market for books about financial booms and busts has itself boomed over the past several years, accelerated in no small part by the recent financial crisis. Why then does it make sense to add to the noise with another treatise on financial bubbles and crashes? Surely all previously written work has addressed any pertinent issues.

The mere fact that this book exists and that you are reading it answers these somewhat rhetorical questions. This book, a written version of a course that I taught at Yale for years, provides a different perspective on financial booms and busts. The fact that I chose to design and teach the course at an undergraduate liberal arts college (rather than a business school) is a telling statement about my perspective. Social occurrences are difficult to categorize as solely economic, psychological, political, or biological—they are, in fact, a complex concoction of all such phenomena. Why, then, should one limit oneself to a simple uni-disciplinary lens when studying financial markets, perhaps the most complicated of social phenomena?

Financial markets are extremely complex developments; competing within them with the handicap of a single lens seems in many ways illogical. Unfortunately, our entire society and educational infrastructure is designed toward specialization and single-discipline analysis. Even among the leading liberal arts schools, virtually all college students are eventually channeled toward a disciplinary major such as economics, political science, psychology, history, literature, biology, or chemistry. While there are meaningful benefits in developing expertise, few multidisciplinary options are offered, let alone pursued. This is exacerbated in graduate and professional schools, and although such specialization is necessary and beneficial in most scientific pursuits, it has the potential to be counterproductive in navigating complex and emergent social phenomena.

Since I entered Yale University as a college freshman (in the pre-cell phone, pre-email era!), I have resisted the tendency of the establishment to channel me into a particular discipline or “box.” Rather than merely study economics or political science, I majored in Ethics, Politics, and Economics—a multidisciplinary major offered at Yale and modeled after the program in Philosophy, Politics, and Economics at the University of Oxford. Incidentally, I double-majored with East Asian Studies, another multidisciplinary major.

Resisting the channel toward a specialization was tougher while pursuing a doctorate, but even here I think I managed to evade the “you must be a single-discipline expert” police who have permeated almost every corner of academia. I sought out PhD programs in the study of innovation and entrepreneurship, inherently multi-/interdisciplinary topics, and was accepted into one such program at the Massachusetts Institute of Technology. The degree I pursued was housed at the Sloan School of Management and was offered by a program called the Management of Technological Innovation and Entrepreneurship. My coursework included economics, psychology, political science, sociology, history, and law.

Even after completing my education and seeking positions in the money-management business, the tendency for immediate specialization was ubiquitous. Virtually every firm with which I interviewed wanted me to become an industry analyst focused on one or two industries. Several firms suggested that it would be best to also focus on a singular geography as well. I soon determined that the established system was based on a strong and widely held view that specialization in the financial markets was a source of advantage. In effect, the industry had produced a pervasive culture of “siloed” thinking in which most practitioners focused on geographies and industries. It was, in the language of Isaiah Berlin, an industry of hedgehogs—people who knew “one big thing.” I opted to become a fox.

In the course of forming my own investment philosophy and approach to thinking about the financial world, I developed a strong belief that a generalist approach (i.e. being a fox) was superior and that competitive insights were found not by competing against other experts but rather by looking between and across the silos. The saying “To a man with a hammer, many things look like nails” is particularly pertinent to the money-management industry. There are times when the worst energy idea may be better than the best consumer idea, yet such insights get lost with expert-oriented approaches.

Before describing what the book is, let me begin by describing what it is not. It is not a book about making day-to-day investment decisions or about the proper investment approach for a particular market. It is not about market timing. Nor is it a book that presents a unique investment philosophy. Many fine books have been written about these topics. Rather, this book is about the context in which these decisions and philosophies are implemented. It is about deciphering the needle-moving extremes that have the potential to render many traditional investment approaches useless. Rather than providing you with a map of how markets may move, Boombustology hopes to provide you a seismograph that can help identify forthcoming quakes.

This book differs from other treatments of financial extremes in three primary ways: (i) it develops and utilizes a multidisciplinary perspective, based on the findings of economics, psychology, and other disciplines; (ii) it utilizes historical case studies to illustrate the power of multiple lenses; and (iii) it summarizes these findings into a forward-looking framework useful in understanding and identifying future financial extremes. After completing this book, you will be left with a robust understanding of the dynamics that precede, fuel, and ultimately reverse financial market extremes. It is also hoped that you will be well versed in the numerous indicators that telegraph the existence of a bubble.

The first section of the book focuses on the five lenses that I consider to be most useful in the study of booms and busts: microeconomics, macroeconomics, psychology, politics, and biology. Why did I choose these lenses? Both micro- and macroeconomic lenses are too obvious to exclude, and the recent emphasis on behavioral approaches necessitates its inclusion. Given the role of politics in developing the very foundation on which booms and busts develop, I included it as well. Space constraints limited me to five lenses, and I chose biology as the fifth to illustrate the power of a perspective external to the social sciences. I chose biology over physics because the economic emphasis on equilibrium is itself derived from physics. It would be eminently reasonable to include sociology and the culture/power dynamics that affect the context as well, and you'll notice that many of the case studies discuss these issues. The lenses utilized are not exhaustive and should be thought of as illustrative. I encourage you to add more lenses as you analyze bubbles.

Booms and busts that affect entire asset classes (versus those that might affect a particular industry or sector) are relatively rare. As such, the second part of the book applies the five lenses to several case studies to generate a “bubble-spotting” theory. The cases chosen were selected to represent variation in geography and time. The list of cases could be much longer, for sure. Again, space constraints limit a more comprehensive approach.

The third and final part of the book takes the lessons learned from Parts I and II and develops a framework for proactively thinking about and identifying financial bubbles before they burst. The theory generated in the book is summarized in a framework presented in Part III; I encourage researchers to test the importance of each indicator.

Topics often associated with the study of bubbles but not included in the book are the benefits of booms and busts and the coincidence of frauds and swindles with busts. Both are excluded here because they are not explicitly about the topic of identifying bubbles. Frauds, swindles, and scams are not-infrequent occurrences in boom times, but because they are unfortunately not revealed until after a bust is well developed, they are often a lagging (and therefore less useful) indicator.

Chapter 1 focuses on the microeconomics of booms and busts, paying special attention to the tendency of prices under various circumstances. Given the dominant microeconomic ideas of market efficiency and supply and demand–driven equilibrium, the chapter describes them and various alternatives. The theory of reflexivity, developed by George Soros, is presented as a viable alternative to the equilibrium-seeking world of traditional microeconomics. The chapter concludes with a reconciliation of the disequilibrium suggested by reflexivity and the equilibrium assumed by microeconomics.

Chapter 2 focuses on credit cycles and financial instability. Three primary theories serve as the focus of the chapter: Irving Fisher's debt-deflation theory of depressions, Hyman Minsky's financial instability hypothesis, and the Austrian business cycle theory. The chapter concludes with a framework for thinking about credit cycles and their impact on asset prices.

Chapter 3 is about the cognitive biases found in most human decision-making. The behavioral lens presented in this chapter focuses on the representativeness and availability heuristics that have historically guided human decision-making toward appropriate answers, but that, in today's increasingly complex, uncertain, and interconnected world, have great potential to lead us astray. Other findings from the research on decision-making are considered and presented, including biases caused by anchoring and insufficient adjustment, mental accounting, fairness, and existing endowments.

Chapter 4 focuses on the politics of property rights and the means through which a society determines the relative value of its goods (i.e. prices). The logic and ramifications of politically motivated price floors and price ceilings are considered, and the chapter concludes with a short discussion of tax policies and how they have the ability to impact asset prices by motivating (or disincentivizing) particular investment decisions by investors.

Chapter 5 attempts to take an emergence perspective from the study of biology and apply it to financial markets. Epidemics, herd behavior, and swarm logic/intelligence are the focus. The chapter focuses on two key lessons: how the study of epidemics and the diffusion of diseases can inform our study of booms and busts—with specific value in helping us understand the relative maturity of a bubble—and how group behavior can have a profoundly conforming impact on its seemingly individualistic members.

Part II of the book presents six historical cases and utilizes the five lenses from Part I to evaluate them. Specifically, Chapter 6 evaluates the Tulipomania of the 1630s; Chapter 7 applies the lenses to the Florida land boom of the mid-1920s and the Great Depression; Chapter 8 is about the Japanese boom and bust; Chapter 9 presents the Asian financial crisis, with special attention paid to Thailand as the epicenter of the events that unfolded; and Chapter 10 evaluates the U.S. housing boom and bust of the 2000s. Chapter 11 takes the Chinese investment bubble (originally profiled as a potential bubble in the first edition of the book) and evaluates the 2010–2015 period through the five lenses.

Chapter 12 summarizes the five lenses and the six cases in a matrix-style analysis that attempts to generate a generalized framework for identifying bubbles before they burst. Key indicators or signposts of a financial bubble are formulated, and a checklist-style evaluation emerges as a means to gauge the likelihood of an unsustainable boom.

Chapter 13 applies the framework of Chapter 12 to one of the most controversial investment considerations in the world today: The potential for India to be the next big growth story. While India has emerged to be one of the best economic growth stories in the recent past, there are reasons to pause and think this may not continue. At the risk of giving away the punch line, Chapter 13 concludes that many indicators are highlighting an elevated probability that the Indian growth story is unsustainable and that investors have been viewing the country's prospects through rose-colored lenses. The Boombustology seismograph is picking up increased pre-quake rumbles.

I'm also pleased to include an addendum to the book with my thoughts on the highly distortive impact of passive investing. While this is not strictly an asset bubble, per se, passive investment is creating bubbly—and therefore unsustainable—dynamics in a host of asset classes. The fundamental logic behind passive investing is that prices are correct. Yet as this strategy (effectively one that buys and sells securities independent of price) gains in popularity, it is increasingly distorting the very foundation of its promise. Passive investors have effectively converted from price-takers (assuming prices are correct) into price-makers (based on the relative inflow and outflow of capital) that are unaware of their new role. The bursting of this passive “bubble” is highly likely to have dire consequences for many investing strategies.

The framework developed over the following pages has helped me navigate through financial booms and busts. I hope it will help you do the same, for in the wise words of Mark Twain, “Although history rarely repeats itself, it often does rhyme.”

Vikram Mansharamani

Lexington, MA

August 2018

Acknowledgments

The ultimate origin of this book lies on a squash court in New Haven. After an exhausting and gruelling squash match against a formidable competitor, I sought his advice. “I'd like to put my PhD to work and perhaps teach a course here at Yale. What do you think?” His response set the wheels in motion: “I think it's a great idea! See if you can teach it as a college seminar.” So it is that I must begin by thanking David Swensen for his encouragement and support in teaching a class at Yale. David is a fierce competitor, a loyal Yalie, a caring mentor, and overall class act. I feel extraordinarily lucky to have him as a friend. A course does not, however, a book make. Charley Ellis encouraged me to convert the course into a book and provided numerous introductions to facilitate its publication. Without his guidance and help, this book would not have been written.

I thank the many students I have had the pleasure of teaching. Over the course of my graduate education and subsequent years of teaching, I have met no group of students more motivated, insightful, intelligent, and analytical than the undergraduates at both Yale and Harvard. They are, simply put, an absolute pleasure to teach because they exhibit natural curiosity, analytical rigor, and intellectual honesty. They have challenged me to think about this material more deeply and have helped refine my thinking.

My graduate education at MIT was an amazing experience that opened my eyes to a new way of thinking. I am particularly thankful to Michael Cusumano, my dissertation committee chair, for his patience as I wandered between academic and non-academic pursuits. Professor Harvey Sapolsky of the Security Studies Program was a constant friend and mentor.

From a professional perspective, I have had the pleasure of working with many great people over the past 25 years. Several have left major imprints on my way of thinking and have indirectly influenced the work presented here. There are way too many to mention specifically. I want to explicitly thank Hank Blaustein for rapidly and creatively capturing the spirit of the “naked emperor” identified by the Boombustology analyst in a fabulous cartoon. Brendan Coffey deserves special mention as a collaborator who assisted me with many of the updates captured in this second edition, and I’m grateful for the research support provided by my colleague Lily Jampol-Auerbach. My parents, Shobha and Vishnu Mansharamani, deserve special thanks. Without their sacrifices (financial and otherwise), I likely would not have had the opportunities in life that I have had.

Any working professional with a young family knows that time is scarce. It should therefore come as no surprise that my greatest debt of gratitude is to my family for their support in providing the time to write this book. Special acknowledgment is owed to my wife, Kristen Hanisch Mansharamani, who has tirelessly read every word. Her editorial capabilities have been tested repeatedly, initially through the writing of three graduate theses, and now through a book. Her dedication and commitment were steadfast.

Finally, I want to thank the editorial staff at John Wiley & Sons for their persevering attention to detail and their unwavering commitment to my efforts, inconsistent as they may have been.

INTRODUCTION

The Study of Financial Extremes: ONE-ARMED ANALYSTS, SECRETS, AND MYSTERIES

We are drowning in information, while starving for wisdom. The world henceforth will be run by synthesizers, people able to put together the right information at the right time, think critically about it, and make important choices wisely.

—E.O. Wilson

Among the many noteworthy comments made by U.S. presidents over the years, perhaps one of the most pertinent with respect to the study of financial booms and busts was made by President Harry Truman: “Someone give me a one-armed economist!” The statement, made in response to the constant “on the one hand … on the other hand” presentation of analysis to him by his advisors, captured the discomfort most decision-makers have with ambiguity and uncertainty. For better or worse, the world in which we now find ourselves is plagued with ambiguity and uncertainty. Globalization, economic interconnectedness, global warming, and international financial linkages are the reality of our sociopolitical-economic existence.

Boombustology takes President Truman's memorable phrase and flips it on its head. “Someone give me a multi-armed analyst!” is my mantra. It is no longer enough for us to evaluate the bipolar possibilities suggested by a two-armed individual. The world is more complex, more uncertain, more dynamic, and more volatile than ever before. It is no longer enough to evaluate developments via a single perspective. Ambiguities rule the day, and as reflected by the title of Robert Rubin's insightful coauthored book,[1] we live “in an uncertain world.”

The fall of the Soviet Union was a defining moment of late twentieth-century world history, but it drove an existential reevaluation for government organizations like the U.S. Central Intelligence Agency. The Agency literally had its entire existence questioned, with many calling for its immediate abolishment.[2]

Rather than succumb to such pressure, the intelligence community instead rigorously reevaluated its purpose in a new world facing new threats and plagued with innumerable uncertainties. Although much of this thinking has broad applicability to the world in which we live today, very little has surfaced in a manner applicable to the dynamic financial and economic uncertainties that have recently dominated popular attention.

Secrets vs. Mysteries

One of the primary insights from this intelligence community introspective analysis is that an inherent and profound difference exists between problems for which an answer exists and must be found versus problems for which no answer (yet) exists. The former case has been labeled a “puzzle” or “secret,” whereas the latter case is considered a “mystery.” Two leading scholars on these distinctions in the U.S. intelligence community are Gregory Treverton and Joseph Nye. Both have highlighted the significant ramifications of this seemingly simple distinction on the approach to generating intelligence.

Treverton eloquently explains the difference between secrets and mysteries in a Smithsonian Magazine article titled “Risks and Riddles.” In it, he says:

There's a reason millions of people try to solve crossword puzzles each day. Amid the well-ordered combat between a puzzler's mind and the blank boxes waiting to be filled, there is satisfaction along with frustration. Even when you can't find the right answer, you know it exists. Puzzles can be solved; they have answers.

But a mystery offers no such comfort. It poses a question that has no definitive answer because the answer is contingent; it depends on a future interaction of many factors, known and unknown. A mystery cannot be answered; it can only be framed, by identifying the critical factors and applying some sense of how they have interacted in the past and might interact in the future. A mystery is an attempt to define ambiguities.[3]

Nye goes on to clarify the distinction in more explicit intelligence community terminology:

A secret is something concrete that can be stolen by a spy or discerned by a technical sensor, such as the number of SS-18 missiles in the Soviet Union or the size of their warheads. A mystery is an abstract puzzle to which no one can be sure of the answer. For example, will Boris Yeltsin be able to control inflation in Russia a year from now? No one can steal that secret from Yeltsin. He does not know the answer. He may not even be in office a year from now.[4]

The distinction these intelligence community scholars make between puzzles and mysteries has broad pertinence to financial markets. Consider the early 2007 New Yorker article “Open Secrets,” written by Malcolm Gladwell. In it, Gladwell highlights the “perils of too much information” and how understanding the difference between puzzles and mysteries leads to a radical reinterpretation of the Enron scandal. Gladwell notes that the truth about Enron's transactions was openly revealed in public filings and all it took was a diligent Wall Street Journal reporter to unveil the issues at hand. The needed capability was not the ability to find particular information, but rather the skill to assemble disparate data points into a clear image of the whole.[5] The problem is not one of inadequate information, but instead one of too much information overwhelming the processing capabilities of “one-armed” analysts.

Different Problems Necessitate Different Approaches

Given that puzzles (i.e. problems for which there are indeed knowable answers) and mysteries (i.e. problems for which there are not knowable answers) are fundamentally different, it should come as no surprise that they necessitate radically different approaches. Consider the relative importance of information and data gathering in each problem.

In the case of a puzzle, the problem is simple: a lack of specific information (i.e. the answer) drives the need for more and more data. More information may contain the answer, so the best approach to addressing a puzzle is to get more information. As mentioned by Treverton, the Soviet Union was a puzzle. The American intelligence community simply needed to gather more data (via satellites, aerial photography, and human intelligence, for starters) to seek the answer.

Mysteries, on the other hand, are less clear. Information is plentiful, and additional data is unlikely to enhance understanding. In addressing mysteries, more information is likely to make the problem more difficult to understand. There is no answer per se in the form of specific data. Rather, insight exists in how the data comes together. In describing the role of the intelligence analysts in the post-Soviet era, Nye noted that they are “people assembling a jigsaw puzzle who have some nifty nuggets inside a box but need to see the picture on the cover to see how they fit.”[6] Finding the pieces is “puzzle work.” Forming the cover image is “mystery work.”

To understand mysteries, we need sophisticated analysis that looks across differing sources of data and evaluates existing information through multiple lenses to uncover a probabilistic answer of how best to understand the mystery. It is only through the use of multiple lenses that we might be able to get a sense of the picture on the jigsaw puzzle box. One lens might only consider color; another might consider the shapes of the pieces, noting the existence of straight edges; a third might focus on anticipated images that are being formed from the other lenses; and so forth.

Nye notes the problem of what I call single-lens analysis in highlighting how the perspectives of the State Department might materially differ from those of the Department of Defense:

In policy circles, the old adage is that where you stand depends on where you sit. In intelligence, what you foresee is often affected by where you work. The primary duty of departmental analysts is to respond to the needs of their organizations. Diplomats are supposed to negotiate solutions. Even in apparently hopeless situations, they tend to press departmental analysts for the one chance in a hundred that might permit success. Generals are supposed to win battles. Even in hopeful situations, they tend to press their intelligence analysts for estimates of what they will have to face if worst comes to worst … The best solution to such human and bureaucratic problems is multiple points of view that are brought together in one place …[7]

Given insights that help elucidate mysteries exist within the mountains of already-available information and data, the key to understanding mysteries lies in filtering and data analysis. Further, as described by Nye, any one filter is necessarily going to be biased—a reality that necessitates the need for multiple lenses. More information (i.e. solving puzzles) will only exacerbate these biases, whereas multiple perspectives will help filter and extract insight from information (i.e. understanding mysteries).

Balancing the general's desire for a worst-case scenario with the diplomat's desire for a best-case scenario will lead to a more calibrated, reasoned, and balanced perspective on the reality of a situation. Likewise, as shall be argued in the next section, financial booms and busts can be best understood when we balance an economist's focus on efficiency with a psychologist's focus on cognitive biases with the insights gained via the use of other lenses.

Uncovering a Mystery

Financial booms and busts are mysteries; they are, particularly from an a priori perspective, probabilistic events for which multidisciplinary analysis is essential. Addressing financial booms and busts as a puzzle may not only prove to be without value, but also have negative impacts and lead to gross misunderstandings.

Thinking of booms and busts as puzzles will lead to a greater emphasis on singular perspectives. It leads to an emphasis on depth of data versus breadth of information. It leads to deeper and more thorough understanding of particular information, but it misses the point that information is not the essential element. There are plenty of “dots,” but connections between them are lacking. We need a framework for connecting the dots in a manner that helps extract insight from the tremendous amounts of information and data that are already available.

As noted by Gladwell, “A puzzle grows simpler with the addition of each new piece of information,” while “mysteries require judgment and the assessment of uncertainty.”[8] Conceiving of financial booms and busts as a mystery necessitates the application of different lenses to develop a probabilistic interpretation of the facts to better understand the situation.

This book provides a framework through which the application of five key disciplines results in a more robust understanding of boom and bust mysteries. The five lenses are microeconomics, macroeconomics, psychology, politics, and biology. Almost by definition, each lens is based on the underlying worldview and beliefs that each discipline is based on. By melding insights from and across these fields, Boombustology will help you become a five-armed analyst. While the one-armed analyst sought by Truman might make for easier decisions, the five-armed analyst is likely to guide leaders toward better decisions.

Notes

1. Robert Rubin, In an Uncertain World (New York: Random House Trade Paperbacks, 2004).

2. Some U.S. senators suggested that the fact that the CIA had missed the implosion of the Soviet Union had cost U.S. taxpayers hundreds of millions of dollars in unnecessary military expenditures that could have been used for other purposes. This was reason enough, they claimed, to fold the CIA into the State Department.

3. Gregory F. Treverton, “Risks and Riddles,” Smithsonian Magazine (June 2007).

4. Joseph Nye, “Peering into the Future,” Foreign Affairs (July/August 1994).

5. Malcolm Gladwell, “Open Secrets: Enron, Intelligence, and the Perils of Too Much Information,” The New Yorker (January 2007).

6. Nye, “Peering into the Future.”

7. Ibid.

8. Gladwell, “Open Secrets.”

PART I

Five Lenses

Part I surveys five disciplines: microeconomics, macroeconomics, psychology, politics, and biology. Each discipline, or lens, is presented as a useful tool in deciphering the mysteries of bubbles before they burst. Specific topics emerging from these five lenses include equilibrium tendencies, reflexivity, credit dynamics, overconfidence, anchoring and adjustment, price mechanisms, property rights, epidemics, and emergence.

CHAPTER 1

Microeconomic Perspectives: TO EQUILIBRIUM OR NOT?

The most interesting, and profitable, times to be involved in investment management are when Mr. Smith's invisible hand is visibly broken.

—Paul A. McCulley

In this opening chapter, we begin our discussion of the various lenses that prove useful in the study of booms and busts by focusing upon a critically important and far-reaching element of traditional microeconomic theory: supply and demand–driven equilibrium. Two competing and seemingly contradictory theories are presented and discussed: the efficient market hypothesis and the theory of reflexivity.

There are many ways in which to illustrate the concept of equilibrium, but it is perhaps best analogized with a ball on a curved shape (see Figure 1.1). A situation in which equilibrium is possible is one in which over time, if left to its own devices, the ball will find one unique location. Overshooting or undershooting this spot is self-correcting. A situation of disequilibrium, however, is one in which the ball is unable to find a unique location. A ball in such a state does not generate self-correcting moves that dampen its moves toward a theoretical “equilibrium” or resting spot; rather, disequilibrium generates motion that is self-reinforcing and accelerates the ball's move away from any stable state.

Figure 1.1 Equilibrium in Pictures

The application of these concepts to the financial arena is very straightforward. The concept of a stable point is best analogized with a price or valuation level in the financial arena. The general idea behind price equilibrium stems from the powerful forces of supply and demand. Inherent in most equilibrium-oriented approaches is a belief that higher prices generate new supply that tends to push prices down. Likewise, it is believed that lower prices generate new demand that tends to push prices up. In this way, deviations from an appropriate price level are self-correcting.

We begin with the traditional economic lens that adopts an equilibrium-oriented view of the financial world. In addition to being based on intuitive supply (the higher the price, the more will be produced) and demand (the higher the price, the lower the demand) logic, the argument in favor of equilibrium is seductively simple. Following a discussion of the efficient market hypothesis and its implications for financial equilibrium, the chapter then turns to the theory of reflexivity. Developed by billionaire George Soros, the theory states that misperceptions about reality may become self-fulfilling, driving prices to ever-greater distances from any supposed stability point.

The careful reader will complete this chapter with the tools to consider financial developments as being equilibrium-oriented or not—which in and of itself should prove valuable in the study of and participation in financial markets. The chapter concludes with a plausible framework for combining the usefulness of both equilibrium and reflexivity lenses.

“Random Walks” and Accurate Prices: The Efficient Market Hypothesis

Adam Smith observed in 1776 that individual, selfish pursuits are able to achieve optimal group outcomes better than if individuals selflessly pursued what they each deemed best for the group. It was as if the self-interested individual is “led by an invisible hand to promote an end which was no part of his intention … [B]y pursuing his own interest, he frequently promotes that of society more effectually than when he really intends to promote it.”[1]

Economic thinking has been profoundly influenced by this early idea that selfish pursuits allocate scarce resources more efficiently than any individual might, despite the noblest of intentions. The laws of supply and demand drive the most efficient allocation of resources, and prices provide accurate signals for the increasing (or decreasing) of supply, with demand rising as prices fall or falling as prices rise.[2] An analogous construct in finance is the efficient market hypothesis, a theory that posits prices of financial securities embody all known information and therefore only move randomly.

The early origins of the efficient market hypothesis can be traced back to George Rutledge Gibson, who in 1889 asserted that the prices of shares that were well known in an open market embodied “the judgment of the best intelligence concerning them.”[3] The statement captures one of the two key building blocks upon which the efficient market hypothesis was built: that prices “contain” or “embody” all available public information. This assertion, which was later developed with greater rigor and precision in the twentieth century, was combined with early econometric work asserting that security prices move in a random manner. This latter claim, developed primarily by MIT economist Paul Samuelson and University of Chicago economist Eugene Fama, essentially stated that stock prices were not predictable based on their prior movements. The idea is often explained at its most basic by discussing the flipping of a coin. Each flip is independent, meaning it isn't influenced by the flips that came before it. No matter how many heads or tails have come up before, that result has no influence over whether heads or tails comes up on the next flip—even if you just had 100 flips that were all heads or all tails.

Fama and Samuelson—who were both building upon an unpublished dissertation by Louis Bachelier written in 1900, titled “The Theory of Speculation”—provided a compelling framework for understanding the behavior of stock prices through further conceptual development of the efficient market hypothesis.[4] In particular, Fama extended, refined, and further developed the theory by articulating three forms of efficiency that exist in the financial markets: weak, semi-strong, and strong.

Before describing each of these three forms of efficiency in greater detail, it is useful to consider the market conditions (i.e. assumptions) on which the theories of efficiency are based. In particular, the efficient market hypothesis requires that participants (in the aggregate) are on average correct and that information is immediately and without friction incorporated by these participants. There is no explicit requirement that individuals be rational or even correct. The only requirement is that individual participants not systematically be irrational in the same manner. This means any irrationality exhibited at the individual level must be offset by contrary irrationality among other individuals such that the “average” individual is not irrational.

Now, let us turn to the three forms of efficiency. The weak form of market efficiency describes a situation in which prior security prices provide no predictive value with respect to future security prices. According to weak-form efficiency, evaluating historical stock price movements proves futile in the search for outperformance in the long run. In the language of today's financial analysts, weak-form efficiency is a theory that technical analysis—the study of prior price movements as the basis to predict future price movements, independent of any fundamental developments—does not work. Weak-form efficiency does not imply that fundamental analysis is not fruitful, just that price movements exhibit no predictable pattern. Stock prices stumble through time in what Fama describes as a “random walk.”[5]

The semi-strong form of market efficiency describes a scenario in which publicly available information fails to provide any predictive value with respect to future security prices. Thus, semi-strong efficiency is really a statement about the rapidity of information incorporation. It implies that share prices instantaneously adjust to new information such that consistently profitable trading on the revelation of such information is not possible. Semi-strong efficiency builds on the weak form's belief that technical analysis is useless and implies that fundamental analysis is also useless.[6]

Finally, the strong form of the efficient market hypothesis suggests that all information (private, public, and otherwise) is fully reflected within the prices of securities, and as such, prices are always correct in that there is no information that provides predictive value with respect to future security prices. Table 1.1 summarizes these three forms of efficiency.[7]

Table 1.1 The Three Forms of Market Efficiency

Weak

Semi-strong

Strong

Price Information

Prices move in random ways.

Prices incorporate all publicly available information.

Prices fully reflect all public, private, and other information.

Role of Technical Analysis

Not useful

Not useful

Not useful

Role of Fundamental Analysis

Useful

Not useful

Not useful

Role of “Inside”/Nonpublic Information

Useful

Useful

Not useful

Method for generating outperformance

Traditional fundamental research

Seeking a nonpublic “edge”

None

In the case of strong form efficiency, insider traders have no advantage over the market. One example that University of Virginia economist Edwin T. Burton cites in his 2013 book Behavioral Finance in support of this argument is that those investing with Ivan Boesky, the infamous inside trader, would have achieved better investment performance with an index fund than they did with Boesky.[8]

In the aggregate, according to the efficient market hypothesis, the current price is the best estimate of value of all market participants. There is no cheap or expensive stock in this view, because the price is always “right.” By definition, if the price isn't right at any moment, it will immediately be forced back to the “correct price” because market participants will exploit the mispricing to generate a profit.

As will be questioned in this chapter and again in Chapter 3, one of the root underlying assumptions in efficient pricing is that all information is interpreted by all participants in exactly the same way. In reality, however, each individual participant will likely interpret data differently or come to unique conclusions about its importance. To say that all individuals have the same information does not necessarily imply that all individuals come to the same conclusion about the value of that information. In fact, as described later, misinterpretations can compound upon themselves, just as differing conclusions from common data might reinforce themselves.

Although the theory of market efficiency was clearly intended to be a simplifying construct and a model useful in helping to explain reality, it was extremely well received by those seeking to understand the volatile (and range-bound) equity markets of the 1960s and 1970s. Specifically, corporate America, Wall Street, and regulators greeted the efficient market hypothesis with a warm reception—resulting in its deep burrowing into the fabric of finance.

By 1978, Michael Jensen, an American economist and emeritus professor at Harvard today, represented the then-entrenched academic position when he proclaimed, “There is no other proposition in economics which has more solid empirical evidence supporting it than the efficient-markets hypothesis.” Financier Fischer Black, in a 1985 speech, dismissed investors buying based on TV news or price-trend analysis, for instance, as “noise traders,” a term that is used by Wall Street to this day to describe people who buy or sell based on recent news.

In academic circles, arguing that financial markets weren't efficient became a sure-fire way to inspire ridicule. The efficient market hypothesis was brought to Main Street by Burton Malkiel's A Random Walk Down Wall Street, which has sold millions of copies since first being published in 1973.[9] As Malkiel has written and said innumerable times since then, “A blindfolded chimpanzee throwing darts at The Wall Street Journal could select a portfolio that would do as well as the experts.”

Implications of Financial Efficiency

The rapid acceptance of the efficient market hypothesis in financial and regulatory communities was a result of its elegance. The seemingly simple idea that security prices fully reflect all available information meant that markets were “right,” and that price moves were accurate reflections of changing fundamentals. Although this logic imbued many facets of the financial industry, the most important manifestations of it are found in the realm of regulatory philosophy and money-management practices.

Bloomberg columnist Justin Fox, in The Myth of the Rational Market, succinctly summarizes the impact of this warm reception:

It was a powerful idea, helping to inspire the first index funds, the investment approach called modern portfolio theory, the risk-adjusted performance measures that shape the money management business, the corporate creed of shareholder value, the rise of derivatives, and the hands-off approach to financial regulation that prevailed in the United States from the 1970s on.[10]

If we believe that security prices are right and completely reflect all available information and expectations, then there is absolutely no room for the consideration that prices are excessively depressed or overly ebullient. Bubbles do not exist in this world, and as such, asset prices are not and should not be a consideration for policymakers. Further, securities regulations need only focus on the creation of a level playing field vis-à-vis insider information, the protection of the public from untrue and unscrupulous marketing, and the prevention of illegal manipulation. Matters such as margin rates, counterparty risk, disclosure requirements, and other mechanical considerations become secondary. This overarching philosophy that “markets know best” has been the guiding light behind much of western-style democratic capitalism since the 1970s, due largely to the broad intellectual appeal and elegant simplicity of the efficient market hypothesis.

Another dramatic implication was that efficiency obviates the need for active portfolio management, whereby investors attempt to outperform the market. After all, if security prices already reflect all available information and there is no predictive value in any information, what value is there in conducting analysis using available information? Better instead to simply “buy the whole market” in a passive manner and not pay the fees associated with active management. The growth of passive money management (primarily manifested in the rapid rise of index funds) has been enormous since the 1970s and at least partially reflects the mass appeal of this efficiency argument.

The Boom and Bust of Efficiency

The commonsense critique of the efficient market hypothesis is best captured by the often-quoted joke about the economist and a friend walking down the street. After stumbling upon a $100 bill lying on the ground, the economist's friend reaches down to pick it up, marveling at her good fortune. Before she actually picks it up, however, the economist says, “Don't waste your efforts. If it were a genuine $100 bill, it would already have been picked up by someone else.”

The absolute domination of the efficient market hypothesis over economic affairs around the world cannot be overstated. In fact, the 1997 Nobel Prize in Economic Sciences was awarded to Robert C. Merton and Myron Scholes for “a new method to determine the value of derivatives.”[11] At the very root of their contribution was a belief that rational, efficient pricing of assets was inevitable and prices would tend toward identifiable equilibria. These two academics were also moonlighting as financiers helping to manage what at the time appeared to be one of the most successful hedge funds of all time, Long-Term Capital Management (LTCM).

LTCM employed a strategy of identifying small inefficiencies in which prices had deviated from their model-derived equilibrium price. The fund then used massive amounts of borrowed money to magnify its bets. In some instances, those bets were magnified through the use of more than $100 of borrowed money for each $1 of invested capital.[12] Although complicated, Nobel-prize winning, mathematically advanced formulas were behind the fund's strategies, one basic underlying premise served as the foundation of the worldview on which the firm's entire approach was based: prices tended toward identifiable equilibria. The firm had billions and billions of dollars (some argue almost a trillion dollars) at risk behind a belief that prices operated according to the right-hand picture in Figure 1.1.

Despite the enormous leverage involved, LTCM had never had a monthly loss of greater than 3% prior to 1998.[13] Global economic uncertainty driven by the Asian financial crisis and the Russian debt default, however, were enough to cause not one, but two days (August 21, 1998 and September 21, 1998) of more than $500,000,000 in losses.[14] According to the LTCM equilibrium-oriented financial models, the likelihood of having one such day was 1 in 50 million. The likelihood of two such days was incalculably small. Needless to say, the revelation of the flawed framework, as well as the huge monetary losses, were shocking to the LTCM financial wizards in Greenwich. Not surprisingly, author Michael Lewis described August 21, 1998 as “the worst day in the young history of scientific finance.”[15]

The most ironic element of the story, of course, is that a team of efficiency-committed, Nobel prize–winning “equilibriumists” effectively undermined their intellectual position by first demonstrating it was possible to generate excess returns (i.e. that markets were not in fact efficient) and then blowing up their intellectual position as well as their business due to something they would probably call a massive market inefficiency. So much for efficiency in the real world!

More recently, Alan Greenspan, “a card-carrying member of the free market brigade”[16] and Ayn Rand devotee, testified to the US Congress that “I do have an ideology … that free, competitive markets are by far the unrivaled way to organize economies.”[17] In response to later questioning about how his philosophy might reconcile with regulation, his response was straightforward: “We've tried regulations. None meaningfully worked.”[18]

As a result of this philosophy, the Chairman of the Federal Reserve took an extraordinarily (but not entirely) hands-off approach to the financial markets. I say “not entirely” because the very existence of a central bank is in fact contradictory to a free market perspective. (In fact, I've often been puzzled by the fact that many free-market economists aspire to be central bankers. Hmmm …)

By basically setting the short-term interest rate in America, the Federal Reserve is effectively a planner that dismisses supply-and-demand fundamentals in the money market and chooses a price for money that it deems appropriate. As we will see in Chapter 2, a small group of economists (collectively known as the Austrian school of economics) have argued that such meddling in the money markets is perhaps the root cause of booms and busts.

Nevertheless, by the fall of 2008, amid one of the most severe economic downturns since the Great Depression, Greenspan indicated to Congress that he had found a flaw in his model of how the financial world works. He went on to describe the greater impact of the credit crunch on the philosophy of market efficiency, saying, “The whole intellectual edifice collapsed in the summer of last year.”[19] For this devout free-marketeer and devotee of market efficiency, such a statement was equivalent to serving pork for lunch during Ramadan in the holy mosques of both Medina and Mecca.

Unstable and Inefficient: The Theory of Reflexivity

George Soros, the hedge fund manager famous for speculating on the dynamics of financial markets, is more known for his financial wizardry than his philosophical musings. Nevertheless, his theory of reflexivity provides a tremendously powerful lens through which to (re)consider market efficiency. Using a “reflexive” lens to view booms and busts proves quite useful, and this section will explain the theory of reflexivity and its primary implications for financial markets.

At its roots, Soros' theory of reflexivity is a theory on the limits of human knowledge. If this is not a grandiose enough topic, the underlying focus of the theory is on the determination of reality and truth in complex social phenomena (like financial markets). The theory is a product of his intellectual devotion to Karl Popper, the early twentieth-century Austrian philosopher and author of Open Society and Its Enemies.

Popper's primary philosophical contributions relate to the asymmetry in the development and falsification of theory. The fundamental problem of science, claimed Popper, is that it cannot prove anything. Rather, virtually all efforts to produce knowledge are based on induction, and induction is inherently problematic. The “problem of induction” relates to the fact that while all confirmatory evidence cannot prove a fact, one contrary piece of evidence can falsify a supposed fact. Thus, although the sun has risen and set every day (thereby leading to a theory that the sun is on a cycle of rising and falling every 24 hours), the mere fact that it has occurred does not mean that it will continue to occur. However, if the sun were to ever not rise and set in a 24-hour period, the theory would be proven false.[20]

Soros goes on to identify two primary “functions” in the social arena. The first, called the cognitive function, is the act through which participants observe the social situation in which they find themselves. The second, the participating function, is the act by which participants participate in (and therefore affect) the social situation. The operation by which these two functions interact is reflexivity. He summarizes: “Reflexivity is, in effect, a two-way feedback mechanism in which reality helps shape the participants' thinking and the participants' thinking helps shape reality in an unending process in which thinking and reality may come to approach each other but can never become identical.”[21] This delta between reality and thinking is known as the participants' bias.

A key implication of this two-way feedback mechanism is that social phenomena have an indeterminacy not present in the natural sciences. There is, according to Soros, no objective truth, and perceptions affect reality as much as reality affects perceptions. While many have suggested that observers might affect the reality they seek to observe, Soros is unique in suggesting that observers actually change the reality they are diligently trying to observe, and that this changed reality in turn affects their perception, creating a self-reinforcing cycle that compounds misperceptions.

Rather than suggesting an additional or alternative lens through which to view reality, Soros goes further to suggest that the scientific method and the basis on which supposed knowledge has been generated are not applicable to the social sciences. Given that economics is the social science that most emulates the natural sciences (thought by many to be the result of “physics envy”), Soros is effectively attacking the supposed rigor of the economic approach. The theory of reflexivity suggests that when it comes to events that have thinking participants, there is no such thing as objective knowledge. Participants act based on their beliefs, which are derived from observing the actions of participants, which are based on their own beliefs, and so on.

Soros bluntly stated his conclusion in a 1994 speech to the MIT Department of Economics: “Thinking participants cannot act on the basis of knowledge. Knowledge presupposes facts which occur independently of the statements which refer to them, but being a participant implies that one's decisions influence the outcome.”[22] Soros fully admits that reflexivity does not occur in every case, but that when it does occur, the dynamics of the situation make traditional scientific approaches (meaning those based on observation) less useful. Figure 1.2 demonstrates how reflexivity is not merely a different way of observing, but rather a radically different process through which reality unfolds.

Figure 1.2 Natural vs. Social Science: Key Observational Distinctions

The key point, then, is that reflexivity is significant because it describes a situation in which misperceptions can be self-reinforced into reality. For this to occur, a strong interaction between the participant and the event must exist to transform the misperception into reality: “What renders reflexivity significant is that it occurs only intermittently. If it were present in all situations all the time, it would merely constitute a different way of looking at events and not a different way for events to evolve.”[23]

The Bitcoin Boom

One of the most recent examples of reflexivity in action took place during the fall of 2017, as cryptocurrencies such as Bitcoin found themselves spiraling higher. As prices rose, so did demand, overwhelming the rise in supply of the cryptocoins. Unlike dynamics in which higher prices depress demand, the bubbly dynamics of the Bitcoin boom led to higher prices drawing in more demand.

Reflexive Dynamics in Action: The Siren's Song of the Bitcoin Bonanza

Source:Bitcoinity.com.

Implications of Reflexivity

For Soros, a Hungarian-born Jew whose family had avoided Nazi persecution through wily tactics such as name changing and secretive movements, it was virtually impossible to believe that truth was knowable or even useful as a concept. The implications of reflexivity on financial markets are quite profound, particularly with regard to the existence of an equilibrium price. Soros describes these implications succinctly:

Instead of a tendency towards some kind of theoretical equilibrium, the participants' views and actual state of affairs enter into a process of dynamic disequilibrium, which may be self-reinforcing at first, moving both thinking and reality in a certain direction, but is bound to become unsustainable in the long run and engender a move in the opposite direction.[24]

His 1994 testimony to the House Banking Committee eloquently summarizes his theory of reflexivity and how it can occasionally rear its head in financial markets:

I must state at the outset that I am in fundamental disagreement with the prevailing wisdom. The generally accepted theory is that markets tend towards equilibrium and on the whole discount the future correctly. I operate using a different theory, according to which financial markets cannot possibly discount the future correctly because they do not merely discount the future, they help to shape it. In certain circumstances, financial markets can affect the so-called fundamentals which they are supposed to reflect. When that happens, markets enter into a state of dynamic disequilibrium and behave quite differently than what would be considered normal by the theory of efficient markets. Such boom/bust sequences do not arise very often, but when they do, they can be very disruptive, precisely because they affect the fundamentals of the economy.[25]

Soros goes on to claim that financial extremes are characterized by two primary components: a prevailing trend that exists in reality and a misconception relating to it. He often uses real estate as an example to illustrate this point. The prevailing trend in reality is that there is an increased willingness to lend and a corresponding rise in prices. The misconception relating to this trend is that the prices of real estate are independent of the willingness to lend.[26] Further, as more banks become willing to lend, and the number of buyers therefore rises, the prices of real estate rise—thereby making the banks feel more secure (given higher collateral values) and driving more lending.

The Reluctant Recognition of Reflexivity

George Soros has used his theory of reflexivity to make billions of dollars for his investors and himself. This does not imply, however, that he has been completely accurate in his predictions. Rather, Soros has been good at managing risk. At the very root of his philosophy is an understanding that he does not know (actually, that he cannot know) anything with 100% certainty.

Soros has been glaringly wrong (or perhaps just very early) in some of his predictions. In his 1998 book The Crisis of Global Capitalism, Soros boldly predicted “the imminent disintegration of the global capitalist system.”[27] In 2001, he admitted during a seminar in New York that he “got carried away” and that he now has “egg on his face.”[28] Despite the bold (and at the time wrong) prediction about the implosion of capitalism, recent events have turned in favor of his arguments. His 2008 testimony to Congress captures the essence of his updated thinking:

The salient feature of the current crisis is that it was not caused by some external shock like OPEC raising the price of oil or a particular country or financial institution defaulting. The crisis was generated by the financial system itself. This fact—that the defect was inherent in the system—contradicts the prevailing theory, which holds that financial markets tend toward equilibrium and that deviations from the equilibrium either occur in a random manner or are caused by some sudden external event to which markets have difficulty adjusting. The severity and amplitude of the crisis provides convincing evidence that there is something fundamentally wrong with this prevailing theory … Usually markets correct their own mistakes, but occasionally there is a misconception or misperception that finds a way to reinforce a trend that is already present in reality and by doing so reinforces itself. Such self-reinforcing processes may carry markets into far-from-equilibrium territory. Unless something happens to abort the reflexive interaction sooner, it may persist until the misconception becomes so glaring that it has to be recognized as such …[29]

The fact that the theory of reflexivity has been so effective at explaining the most impactful events in financial markets (i.e. the extremes) has been the basis of its recent, albeit reluctant, recognition by practitioners and academics. While it has yet to be fully accepted, the glaring failures of the efficiency arguments over the past 20 years have resulted in an increasing openness to alternatives.

Simply put, the existing efficiency framework is not always accurate, and reflexivity helps fill in the holes when it fails. Stability is not ensured by simple supply and demand dynamics. Rather, there are times in asset markets when higher prices generate more demand (not more supply) and when lower prices generate more supply (not more demand). As noted by Soros, these self-reinforcing processes can generate instability from within the system.[30]

Bubbly Gold Dynamics

Gold, one of the longest-valued assets in human history, shows the dynamics of reflexivity quite well. The expansion of the United States, Canada, and Australia were based on very real gold rushes. In short time frames, populations surged as fortune seekers literally rushed into the market. Consider the fact that the non-Native American population of California grew seven-fold, from 14,000 to 100,000, in 1849. The European population of the Australian state of Victoria grew five-fold in a decade after gold was discovered in 1851. Sizeable gold rushes have also been seen in Russia and southern Africa.

Gold still has a way of capturing the popular imagination, particularly at times of high prices. In the spring of 2011, when gold was at a then-record $1,500 an ounce, I was on the streets of Manhattan walking to a television interview with Tom Keene of Bloomberg. In the couple of blocks between the subway stop and the studios, three people offered to buy any gold I might have, two people tried to sell me gold, and one, if I hadn't been careful, probably would have taken my gold. While Tom and I chuckled on air about the dynamics, it was clear that gold was on the minds of lots of people.

More instructive for our purposes is the history of gold since the discarding of the gold standard for currencies in the early twentieth century. In 1933, President Franklin Roosevelt took the United States off the gold standard; and in 1973, Richard Nixon ended the ability to exchange dollars for gold. That unleashed gold into a boom-bust behavior that has continued ever since, with the high of $1,825 an ounce being reach in August 2011. Certainly, gold that year exhibited many of the characteristics to be discussed throughout this book, but for now let's focus on the element of reflexivity.

Most mainstream economic theories utilize a supply and demand–driven price-determination model that generally results in prices tending toward equilibrium. I say “tending” because most serious scholars admit that behavioral and informational issues can distort the price at any point in time, but there exists an overarching philosophical belief that such distortions are rapidly ironed out. Gold prices are, according to this perspective, efficient. Higher prices dampen demand, and lower prices disincentivize supply.

Let us suppose for a minute, however, that higher prices increase demand. Such a dynamic might arise for many reasons, but one eloquent explanation for this outcome in asset markets is reflexivity and the underlying assertion that prices can and do tend away from equilibrium. In this case, boom and bust dynamics appear highly likely.

Has gold exhibited this boom-bust tendency? Do prices tend toward or away from an equilibrium level? Evidence from the GLD gold exchange-traded fund (ETF) suggests that gold prices tend to correlate with demand, meaning higher prices generate more demand. The following chart shows the number of shares outstanding (a reasonable proxy for demand) and the price per share of GLD.

The Reflexivity of Gold: Demand and Price Moving Together

Source: Bloomberg LP.

Consolidating Two Factors of Detection

By attempting to reconcile the seemingly incompatible approaches of the efficiency and reflexivity lenses, this chapter concludes by suggesting a contingent approach to using those lenses. Even though Soros adamantly opposes the logic of efficiency, he does concede that markets usually correct themselves. Thus, the efficiency argument for a stable equilibrium that results in events tending toward it seems viable—most of the time. However, it also seems likely that events that occur far from equilibrium or are reinforced beyond a certain distance from that equilibrium are unlikely to return to it.

Figure 1.3 attempts to capture these two distinct phases using the balls-and-hills logic utilized earlier in the chapter. In the figure, notice that events transpiring in the efficiency band will tend toward an equilibrium point. The same is not true, however, for the balls that have entered the realm of far-from-equilibrium reflexive developments. These balls are unlikely to stabilize in any specific condition unless assisted by an external force.

Figure 1.3 Reconciling Efficiency and Reflexivity

Most of the time, efficiency logic works, and deviations from equilibria tend to self-correct. However, there are instances in which reflexive dynamics are able to overcome the self-correcting force and create self-fulfilling extremes. The investment implications of this efficiency/reflexivity duality are that Adam Smith's invisible hand, which normally drives an appropriate price-discovery process, occasionally breaks down. It is precisely when such dynamics take over that extremes become increasingly likely.

Notes

1. Adam Smith, The Wealth of Nations, book IV, chapter II, paragraph IX, http://www.econlib.org/library/smith/smWN.html.

2. Paul Samuelson, Economics: An Introductory Analysis (New York: McGraw Hill, 1967).

3. Justin Fox, The Myth of the Rational Market (New York: HarperCollins, 2009), xiii.

4. The dissertation was eventually published; see Davis and Etheridge, Louis Bachelier's Theory of $peculation: The Origins of Modern Finance (Princeton, NJ: Princeton University Press, 2006).

5. Eugene Fama, “Efficient Capital Markets: A Review of Theory and Empirical Work” (paper presented at the 28th annual meeting of the American Finance Association in New York City, December 28–30, 1969). The paper was later published in the Journal of Finance 25, no. 2 (May 1970).

6. Fama, “Efficient Capital Markets: A Review of Theory and Empirical Work,” Journal of Finance 25, no. 2 (May 1970).

7. Ibid.

8. Edwin Burton and Sunit Shah, Behavioral Finance: Understanding the Social, Cognitive & Economic Debates (Hoboken, NJ: Wiley, 2013).

9. Burton Malkiel, A Random Walk Down Wall Street (New York: Norton, 2016).

10. Fox, Myth of the Rational Market.

11. The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel, http://nobelprize.org/nobel_prizes/economics/laureates/1997/.

12. Roger Lowenstein, When Genius Failed: The Rise and Fall of Long-Term Capital Management (New York: Random House, 2000); Robert Rubin et al., “Hedge Funds, Leverage, and the Lessons of Long-Term Capital Management: Report of The President's Working Group on Financial Markets” (April 28, 1999), https://cftc.gov/sites/default/files/tm/tmhedgefundreport.htm.

13. Peter Coy and Suzanne Woolley, “Failed Wizards of Wall Street,” BusinessWeek (September 21, 1998).

14. Michael Lewis, “How the Eggheads Cracked,” New York Times Magazine (January 24, 1999).

15. Ibid.

16. Raj Patel, The Value of Nothing: How to Reshape Market Society and Redefine Democracy (New York: Picador, 2009), 4.

17. As quoted by Representative Henry Waxman during Alan Greenspan's testimony to the Government Oversight Committee, October 23, 2008.

18. Greenspan, testimony to the Government Oversight Committee.

19. Ibid.

20. Karl Popper, The Open Society and Its Enemies (Princeton, NJ: Princeton University Press, 1971).

21. George Soros, speech given to the MIT Department of Economics World Economy Laboratory Conference in Washington, DC (April 26, 1994).

22. Ibid.

23. George Soros, The Soros Lectures at the Central European University (New York: Public Affairs, 2010).

24. Soros, World Economy Laboratory Conference.

25. George Soros, testimony before the U.S. House Banking Committee (April 13, 1994).

26. George Soros, testimony before the U.S. House of Representatives Committee on Oversight and Government Reform (November 13, 2008).

27. George Soros, The Crisis of Global Capitalism: Open Society Endangered (New York: Public Affairs, 1998).

28. Andrew Cave, “‘I got it wrong' on collapse of global capitalism, says Soros,” The Telegraph (February 23, 2001).

29. Soros, testimony before the U.S. House Committee on Oversight and Government Reform.

30. In the language of professional economists, such instability is endogenously generated and does not require any exogenous shock to be created.

CHAPTER 2

Macroeconomic Perspectives: THE IMPACT OF DEBT, DEFLATION, AND MISPRICED MONEY ON ASSET MARKETS

It may sometimes be expedient for a man to heat the stove with his furniture. But he should not delude himself by believing that he has discovered a wonderful new method of heating his premises.

—Ludwig von Mises

This chapter makes a leap to macro thinking from the micro considerations of the last chapter. Although not shocking, it seems that what makes sense for the individual may not always make sense for the group. Highlighting how this paradox of aggregation operates, Paul McCulley notes:

Anybody who's ever been a spectator at a crowded ball game has witnessed the difference between microeconomics and macroeconomics: from a micro perspective, it is rational for each individual to stand up to get a better view; but from a macro perspective, each individual acting rationally will produce the irrational outcome of everybody standing, but nobody having a better view.[1]

In economic spheres, this fallacy of composition can be more extreme and have more dramatic impacts on society. Specifically, analysis conducted at an individual level may not apply to groups.[2] Consider home finance. Although it may be reasonable for an individual bank to believe that it can foreclose and sell a house for a value in excess of the mortgage amount, this conclusion is very suspect when considering millions of homes simultaneously being sold. The latter case will crash the market for homes and result in significantly lower prices.

The underlying premise evaluated in this chapter is that debt and the amplification of debt by deflation can have deleterious impacts on individuals, companies, and, in aggregate, markets. Before diving into the conditions under which these effects can snowball in dramatic fashion, we first review the mechanics of debt. More specifically, understanding leverage and how it impacts equity values is essential to grasping how debt exacerbates ups and downs into booms and busts. After describing the mechanisms through which debt amplifies returns, the chapter turns to a somewhat less traditional perspective on the role of money and credit in the creation of booms and busts: Hyman Minsky's Financial Instability Hypothesis. We then explore Irving Fisher's debt-deflation theory, as well as the Austrian Business Cycle Theory, and conclude the chapter by integrating these perspectives into a single macroeconomic framework.

The Magnifying Power of Leverage

Suppose you purchased a house for $100 (a real steal!). Because you don't have all the money needed to buy the house, you decide to take out a loan for a portion of the purchase price. The amount you put down will be your equity in the home, and the mortgage value will be your debt. Now, let's consider two separate scenarios. The first, called “Happy Times,” is one in which the value of your newly purchased home rises by 10%. The second, “Sad Times,” is a situation in which the price of your newly purchased home falls by 10%.

What happens to your investment in each of these scenarios? Table 2.1 summarizes the impact on your investment for a range of down payments under each case. Four rows are highlighted to demonstrate the impact of debt: 10% down, 20% down, 50% down, and 100% payment in full.

Table 2.1 Debt's Amplification Power

Initial Value

Initial

Happy Times

Sad Times

% down

Equity

Value

Debt

Equity

Return

Value

Debt

Equity

Return

$100

0%

$0

$110

$100

$10

Infinite

$90

$100

($10)

Infinite

$100

10%

$10

$110

$90

$20

100%

$90

$90

0

(100%)

$100

20%

$20

$110

$80

$30

50%

$90

$80

$10

(50%)

$100

30%

$30

$110

$70

$40

33%

$90

$70

$20

(33%)

$100

40%

$40

$110

$60

$50

25%

$90

$60

$30

(25%)

$100

50%

$50

$110

$50

$60

20%

$90

$50

$40

(20%)

$100

60%

$60

$110

$40

$70

17%

$90

$40

$50

(17%)

$100

70%

$70

$110

$30

$80

14%

$90

$30

$60

(14%)

$100

80%

$80

$110

$20

$90

13%

$90

$20

$70

(13%)

$100

90%

$90

$110

$10

$100

11%

$90

$10

$80

(11%)

$100

100%

$100

$110

$0

$110

10%

$90

$0

$90

(10%)

In the scenario in which you do not borrow any money and pay for your house with $100 (i.e. 100% down), your investment return in each case is equal to the change in the asset value (i.e. +10% in home value = +10% in equity value). As the amount of debt utilized increases, however, the return on your invested capital is a multiple of the return. For instance, if you purchased your home with $50 down and a $50 mortgage, then a 10% move in the house price equates to a 20% move in your equity value. Likewise, with 20% down, a 10% move in the house price equates to a 50% move in your equity value. In each of these cases, the return on your equity is equal to a multiple of the return on the house. More precisely, the equity return is equal to

Return of asset × (1 / % Down)

In sum, debt amplifies your returns—you make more money when asset values rise, and you lose more money when asset values fall. Given the powerful amplification feature of debt, it should come as no surprise that some of the most persuasive arguments explaining boom and bust cycles are based on debt and debt cycles.

Collateral Rates and Debt Dynamics

To understand debt dynamics in a more granular way, let's look at three different families: the Safe Smiths, the Optimistic Osbornes, and the Carefree Carrolls.[3] As you meet each of these families, pay attention to their risk profiles and how each fares under Happy Times and Sad Times. Here are some facts that apply to all three families:

- All houses cost $100.

- Each family earns $25 per year before taxes ($15 after taxes).

- Each family has nonmortgage expenses of $10 per year.

- Money available for mortgage payments each year is therefore $5.

- All mortgages are obtained from Local Bank ABC, must be refinanced or paid off at the end of five years, and are available with the terms shown in Table 2.2 thanks to a government program guaranteeing access to home purchase finance.

Table 2.2 Local Bank ABC Loan Rates

Loan Amount

Down Payment

$100

$100 +

0%

7.00%

7.50%

10%

6.25%

6.75%

20%

5.50%

6.00%

30%

4.75%

5.25%

40%

4.00%

4.50%

50%

3.25%

3.75%

The Safe Smiths are a conservative bunch. Mr. and Mrs. Smith both work, and the family lives within its means. Like all other families in the neighborhood, the Safe Smiths purchased a home this year for $100. Although they have savings in excess of $100, they decided to purchase the home with $40 down and a $60 mortgage. Because they put 40% down and their mortgage amount is less than $100, the rate they obtained for their mortgage was 4%. Their yearly interest payments to the bank are therefore $2.40, and if they are interested in paying off their mortgage in 30 years, their annual payment (i.e. principal and interest) is equal to $3.44. Given they have $5 available for mortgage payments, the Safe Smiths sleep well at night and are not worried about their budget.

The Optimistic Osbornes are a bit more aggressive. They believe the future is bright, and they are willing to plan on a better tomorrow. They haven't saved as much as the Safe Smiths, but they have enough capital for a 20% down payment. Thus, they take out an $80 mortgage, and the bank gives them a mortgage rate of 5.5%. Their yearly interest payments to the bank are $4.40, and if they are interested in paying off their mortgage in 30 years, their monthly payment is equal to $5.45. Like all other families in the neighborhood, the Optimistic Osbornes have only $5 available for mortgage payments, so they opt for an “interest only” mortgage and agree to pay the $4.40 per year. The family is optimistic that Mr. Osborne may get a promotion (with an accompanying increase in salary) or that the house will appreciate in the near future. Even if they cannot begin paying the full $5.45 per year, the Osborne family is confident that they can refinance the house once it has appreciated and get a lower interest rate on their mortgage.

Finally, the Carefree Carrolls are an optimistic lot who live solely in the present. They don't think about the future and believe tomorrow is always better than today, that house prices always go up, and that budgeting is a worthless task. The Carefree Carrolls love to consume and have not saved very much over the years. As a result, they have no money for a down payment and must seek $100 of financing. Thanks to government programs encouraging homeownership, they are able to get the mortgage at a rate of 7.5%, with a flexible payment schedule. For the first three years of ownership, they are allowed to pay what they are able, and any unpaid interest will be added to the principal of the loan. Their yearly interest payments to the bank are $7.50, and if they are interested in paying off their mortgage in 30 years, their annual payment is equal to $8.39. Given that they have only $5 available for mortgage payments, the bank has arranged to let the Carefree Carrolls pay $5 and let $2.50 be added to the balance of the mortgage at the end of the year. The family is ecstatic they're able to buy their new home and, given the stories they've heard of people making money in real estate, are looking forward to real estate riches.

Table 2.3 summarizes the financial obligation structures for these three families.

Table 2.3 Comparing the Safe Smiths, Optimistic Osbornes, and Carefree Carrolls

Safe Smiths

Optimistic Osbornes

Carefree Carrolls

Purchase Price

$100

$100

$100

Down Payment

$40

$20

$0

Mortgage

$60

$80

$100

Interest Rate

4.0%

5.5%

7.5%

Payments

Interest

$2.40

$4.40

$7.50

Principal + Interest

$3.44

$5.45

$8.39

To see how each family does in Happy Times and Sad Times, look at their financial conditions at the end of year one. To simplify the analysis, let's assume that each family pays the entire $5 to the bank in year one, meaning the Safe Smiths pay $2.40 in interest and the remaining $2.60 as principal repayment to the bank. Likewise, the Optimistic Osbornes pay $4.40 in interest and $0.60 as principal. The Carefree Carrolls will pay $5 toward interest and borrow an additional $2.50 to pay interest. Table 2.4 summarizes each family's equity return after one year in both Happy Times and Sad Times.

Table 2.4 Happy Times and Sad Times

Safe Smiths

Optimistic Osbornes

Carefree Carrolls

Happy Times

House Value

$110.00

$110.00

$110.00

Mortgage

$57.40

$79.60

$102.50

Equity Value

$52.60

$30.40

$7.50

Original Equity

$40

$20

$0

Return

31.5%

52.0%

infinite

Sad Times

House Value

$90.00

$90.00

$90.00

Mortgage

$57.40

$79.60

$102.50

Equity Value

$32.60

$10.40

($12.50)

Original Equity

$40

$20

$0

Return

218.5%

248.0%

infinite

Let's now consider two new scenarios—Very Happy Times and Very Sad Times—in which house prices rise and fall, respectively, by 25% over five years. How do the families fare? Again, for simplicity, let's assume that each family pays only interest for the five years and applies all saved money (i.e. annual savings = $5 minus the interest payment) to principal at the end of the five years. Table 2.5 summarizes the resulting outcomes for the families in both Very Happy Times and Very Sad Times.

Table 2.5 Very Happy Times and Very Sad Times

Safe Smiths

Optimistic Osbornes

Carefree Carrolls

Very Happy Times

House Value

$125.00

$125.00

$125.00

Mortgage

$47.00

$77.00

$112.50

Equity Value

$78.00

$48.00

$12.50

Original Equity

$40

$20

$0

Return

95.0%

140.0%

infinite

Very Sad Times

House Value

$75.00

$75.00

$75.00

Mortgage

$47.00

$77.00

$112.50

Equity Value

$28.00

($2.00)

($37.50)

Original Equity

$40

$20

$0

Return

–30.0%

–10.0%

infinite

Given that each party has a mortgage balance at the end of the five years and must refinance the loan, let's examine how each family's down payment (i.e. the equity value) changed as a percentage of the home value, and the new resulting interest rate.

For the Safe Smiths, neither Very Happy Times nor Very Sad Times greatly affect their refinancing terms. In Very Happy Times, they can refinance at a rate of 3.25% because their down payment is now effectively .50%. In Very Sad Times, they can refinance for 4.75% with their yearly interest payment due as $2.23, still comfortably below their $5 budget.

The Optimistic Osbornes, however, are greatly affected by the difference between Very Happy Times and Very Sad Times. In Very Happy Times, they're able to refinance to 4.75% (because their down payment is effectively 38%), leaving them with an interest payment of $3.88 and an interest + principal payment scheme of $4.80 for a 30-year payoff (both of which are within their $5 budget). In Very Sad Times, however, they must refinance at a rate of 7%, leaving them with interest payments of $5.39—meaning they must borrow more each year simply to make the payments.

Interest Rates and Asset Prices: Affordability-Based Valuation

Although it may seem obvious that lower interest rates make assets such as homes more affordable, they also have the potential to inflate asset prices. Consider the following example, in which a house is originally purchased for $100 when the cost of money (i.e. the interest rate) is 5%. For ease of calculation, let's assume that the house is purchased with 100% financing and no money down.

In this case, the buyer's annual interest payments would be equal to $100 × 5% or $5. Now let's suppose that interest rates fall by 1%. One impact may be that a buyer might again pay $100 and have an annual carrying cost of $4, but another very possible impact is that the buyer's budget of $5 is fixed, and they're willing to now pay up to $5/4% or $125 for the home. Similarly, if interest rates rise by 1%, the buyer might be willing to pay $100 and have a higher carrying cost of $6 per year—or perhaps the buyer retains the $5 budget and is willing to pay only $5/6% or $83.

Clearly, interest rates can meaningfully affect asset valuations, particularly when buyers may be budget constrained.

For the Carefree Carrolls, Very Happy Times allow them to continue their lives effectively “as is.” They're able to refinance at a lower rate of 6.75%, but due to the extra money they've borrowed over the years, their yearly interest payment ($7.59) remains in excess of their $5 budget. Very Sad Times, however, are very sad indeed for the Carefree Carrolls. Because they have a loan amount that is far in excess of the house's value, the bank is not willing to refinance the property and instead forecloses.

As these examples have shown, the relationship between debt, collateral (i.e. down payment or equity amount), and asset prices has the potential to create a toxic cocktail that can greatly improve or deteriorate an individual's financial condition quite rapidly. The next section turns to a theory—the financial instability hypothesis—that suggests these relationships result in a cyclical flow of credit that results in continuous financial instability.

Interest Rates and Corporate Investing

Corporations regularly make decisions regarding the projects in which to invest. As part of their decision-making processes, most businesses attempt to understand the financial returns likely to be generated by investing in a project. A key input in this analysis is the cost of funding, a variable directly influenced by interest rates. All else being equal, corporations will have many more profitable and financially worthwhile projects to take on when interest rates are lower than when they are higher.

Thus, interest rates affect the likelihood of investments taking place, and lower interest rates make lower-return projects viable. But once projects are under way, rising rates can have disruptive impacts on many elements of a corporation and, in aggregate, on industries in which similar decision-making processes may exist.

Consider a steel mill that decides a $1 million investment in expansion makes sense because it generates a 10% return (based on current steel prices, etc.), while its cost of capital is 8%. However, if interest rates rise and the company's cost of capital is now 11%, the project is no longer profitable. In short, higher interest rates raise the hurdle for corporate investment decisions.

Hyman Minsky's Financial Instability Hypothesis

Hyman Minsky was an American economist and professor of economics at Washington University in St. Louis, Missouri. A graduate of the University of Chicago and Harvard University (where he studied under Joseph Schumpeter and Wassily Leontief), Minsky was a relatively unacknowledged economist until after his death in 1996. “With long, wild, white hair, Minsky was closer to counterculture than to mainstream economics.”[4] Although his initial research was focused on poverty, he was taken by a seemingly simple question: could the Great Depression happen again?[5] It was from this line of research that Minsky delved into the topic of financial crises and debt dynamics preceding, during, and subsequent to them. The outcome of this effort was the financial instability hypothesis.

Minsky's theories have been resurfacing with increasing frequency among practicing financiers. In fact, after the financial crisis, UBS held a conference call titled “Minsky for Beginners” for its institutional clients. During the call, George Magnus, former UBS chief economist, eloquently summarized the financial instability hypothesis:

Minsky's big contribution was the proposition that after long periods of economic stability, endogenous destabilizing forces in the economy begin to develop, forces that eventually lead to financial instability … [H]e argued that this happens through the progressively more interesting but then progressively more dangerous use of leverage.[6]

The roots of this internally produced instability, Minsky argued, are found in the three primary forms of debt structures that exist in a capitalist society, and their relative predominance in the system at various points in time. His three distinct income-debt relationships were labeled—according to their respective ability (or inability) to pay interest and principal from normal cash flows—as hedge, speculative, and Ponzi.[7]

Hedge financing takes place when you are able to pay back both the interest owed as well as the principal due via normal cash flows. This approach is not particularly risky and is not subject to changing market conditions. The Safe Smiths in the preceding examples can be classified as hedge financiers.

Speculative financing is a bit riskier because it is an approach in which interest expenses are paid, but the principal must be refinanced upon maturity. According to Minsky, “the speculation is that refinancing will be available when needed.”[8] Greater risk is borne, because debt for refinancing may be available at materially different prices than originally envisioned. The Optimistic Osbornes began as speculative financiers.

Finally, Ponzi financing takes place when you are dependent on the availability of additional debt in order to pay interest on existing debt. Given the inability to pay interest expense out of cash flows, the possibility of principal paydown is nonexistent in Ponzi financing structures. This structure is based on an operating assumption that values will continually rise, allowing for easier and more advantageous refinancing terms. The Carefree Carrolls are Ponzi financiers. Table 2.6 summarizes these three financing structures.

Table 2.6 Minsky's Debt Descriptions

Hedge

Speculative

Ponzi

Cash Flow Adequate to Pay Interest?

Yes

Yes

No; interest must be paid for with new debt

Cash Flow Adequate to Pay Principal?

Yes

No; principal must be refinanced with new debt

No; principal must be refinanced with new debt

The financial instability hypothesis that Minsky proposed is based on a shift in the mix of financing structures present in a society. In a working paper presented at Bard College, Minsky eloquently summarized his theory, utilizing the language of equilibrium presented in the prior chapter:

It can be shown that if hedge financing dominates, then the economy may well be an equilibrium-seeking and -containing system. In contrast, the greater the weight of Ponzi finance, the greater the likelihood that the economy is a deviation-amplifying system.

Shifting economic conditions further complicate the distinctions of these financing structures, as hedge units could become speculative units or Ponzi units, and speculative units may well turn into Ponzi units in environments of degrading profitability.

Earlier, we observed a shift in the type of financing structure utilized by the Optimistic Osbornes depending on the state of asset prices. In Very Happy Times, the Optimistic Osbornes migrated from speculative financiers to hedge financiers. However, in Very Sad Times, they morphed from speculative to Ponzi financiers.

Minsky's argument about constant instability is straightforward: “Over a protracted period of good times, capitalist economies tend to move from a financial structure dominated by hedge finance units to a structure in which there is a large weight of units engaged in speculative and Ponzi finance.”[9] Thus, in a self-fulfilling, reflexive manner, financing units get more and more aggressive as the lack of failure justifies this tendency. Eventually, however, the weight becomes unbearable, and the structure implodes. This procyclical tendency of credit to grow in riskiness during good times is very destabilizing, and it is something I call the Minsky Migration.

McCulley eloquently summarizes Minsky's underlying point: “Put differently, stability can never be a destination, only a journey to instability.”[10] McCulley coined the term Minsky Moment, representing that moment in time when the credit structure switches from getting more aggressive to less aggressive. After the Minsky Migration crosses the Minsky Moment, bad things happen. Speculative and Ponzi units begin to implode, and hedge units become vulnerable as the entire economy wobbles. Asset prices plunge as those units unable to obtain refinancing are forced to sell assets. This dynamic, which causes broad and great pain in an economy, is known as debt deflation.

Debt Deflation and Asset Prices

Irving Fisher[11] is most known for his unfortunately timed statement in 1929, days before the stock market crash, that “stock prices have reached what looks like a permanently high plateau.” His insistence immediately after the crash and up until the economic contraction had acquired significant momentum that stock prices were destined to go higher, combined with the failure of a firm he started, led most people to dismiss him and his ideas entirely. His debt-deflation theory, in which he argued that deflation increased the real value of debts, did not receive serious attention until well after his death.

Although Fisher was a devout believer in general equilibrium theory prior to the Great Depression, he quickly rejected the idea of a stable equilibrium by 1933, noting that “there may be an equilibrium which, though stable, is so delicately poised that, after departure from it beyond certain limits, instability ensues, just as, at first, a stick may bend under strain, ready all the time to bend back, until a certain point is reached, when it breaks.”[12] In fact, Fisher stated that he believed the concept of equilibrium to be “absurd” and that “at most times there must be over- or under-production, over- or under-consumption, over- or under-spending, over- or under-saving, over- or under-investment, and over- or under- everything else.”[13]

Once the Great Depression was in full force, Fisher developed a cycle theory of booms and busts: the debt-deflation theory of great depressions. Embodied in his book Booms and Depressions and succinctly summarized in a 1933 Econometrica journal article, the theory is based on the premise that overindebtedness and deflation are a toxic combination, regardless of other factors that may be present. Fisher admitted that overinvestment, overconfidence, and overspeculation were important considerations, but further noted that “they would have far less serious results were they not conducted with borrowed money.”[14]

At the root of the debt-deflation theory is an understanding that falling prices effectively increase the real value of debts, further burdening already stressed borrowers like the Osbornes and the Carrolls. This process usually results in forced and uneconomic selling of assets by overindebted companies and individuals, which results in further falling prices that results in an effective increase in real debt. Fisher summarizes the results of this process: “when overindebtedness is so great as to depress prices faster than liquidation, the mass effort to get out of debt sinks us more deeply into debt.”[15]

Many scholars today argue that the twentieth century experienced two serious bouts of debt deflation: the Great Depression and Japan's Lost Decade(s). Because data is significantly more available for the Japan case, recent (and ongoing) research on it has proven additive. In particular, Richard Koo's formulation of a two-stage business cycle is worth considering. In the normal course, notes Koo, businesses focus on profit maximization, as suggested by traditional economic theories. Following a highly leveraged boom, however, the power of debt-deflation dynamics drives companies to focus on deleveraging—even if at the expense of profit maximization. The result is a lack of demand for credit and, as he observed in Japan, constant deleveraging despite interest rates of close to 0%.[16] This part of the business cycle is one that Koo labels a “balance sheet” recession.

The debt-deflation theory to some extent mixes the dynamics of leverage with reflexive tendencies. That is, selling begets more selling, and the self-fulfilling fear of lower prices is amplified by leverage. Debt-deflation theories emphasize what occurs after a bust—inadequate demand driving deflation, which becomes particularly toxic when compounded with debt.

Quantitative Easing

As I mentioned earlier, there is irony that the pinnacle of the economics profession in the United States is Federal Reserve Chair. Many economists believe markets are definitionally efficient, and to think otherwise is to compete with Malkiel's chimpanzee. Yet the height of the profession—what economists in the halls of Princeton, MIT, Harvard, Chicago, and Stanford aspire to—is a central banker, a person tasked with setting the price of money.

Leverage, Interest Rates, and the 2017 Crypto-Craze

There's evidence that the availability of credit helped fuel Bitcoin's meteoric rise in late 2017. Joseph Borg, the president of the North American Securities Administrators Association, an investor-protection organization, said in December 2017 that the group had seen people take out home mortgages and use credit card advances to buy Bitcoin and possibly other cryptocurrencies.[17]

Like the housing bust of the late 2000s, it appears that the environment of low rates encouraged speculative buying. Consider the idea of a risk-free return—the return you can get on your money without risk of principal loss. For most investors, this is usually the rate of return on U.S. government bonds, considered the safest investment in the world because they are backed by the faith and credit of the U.S. government. In 2017, the annual yield on a 30-year Treasury bond averaged under 3%. If you wanted a higher return, you were forced to take higher risks.

Individuals, by and large, were the predominant buyers of crypto-assets. And for them, it is likely their perceived risk-free return was the interest rate offered on high-balance deposit accounts. In that case, the “riskless” return is even less: around 1% (or possibly lower). And since the Consumer Price Index—the measure of the cost of goods and services across the economy—rose 2.1% in 2017, according to the Bureau of Labor Statistics,[18] many individuals were justified in thinking they were losing purchasing power if they did nothing. If you perceive that you're actually losing money by keeping it in the bank, you're going to be more inclined to put your money in something that offers the promise of very high rewards.

I continue to find it a bit odd that folks who believe markets are best at setting prices feel they are the best at setting the price of money. To pursue their dual mandate to support employment and keep prices stable, they are given control of the money supply (and, relatedly, the price of money). The money supply is the total of all the physical currency in circulation: the deposits at institutions that primarily serve the public, like commercial banks, credit unions, and similar institutions, plus savings deposits, money market mutual funds, and others instruments. Roger Lowenstein, writing for the New York Times, explained the Fed's position this way:

You can think of the Fed as the banker in a national game of Monopoly. Normally, everyone gets $200 when they pass “Go,” but when business conditions slump, the Fed can give the economy a boost—much like hiking the “Go” rate to $300. Or, if the prices of the little green houses and red hotels are rising too swiftly, it can hand out less money.[19]

Over time, there has been a strong correlation between interest rates (which effectively are the price of money), which are influenced by the Fed in this manner (although the mechanism was different in the past), and stock market returns. That is, there is evidence that the central bank influences asset prices. We'll explore this in depth in the chapter on the Great Depression, but for now it's sufficient to understand that many economists—including Ben Bernanke, Fed chairman during the Great Recession—believe that raising interest rates (i.e. tightening the money supply) was a major reason the 1929 market crash led to the Great Depression. It created the nasty debt deflation that proved so hard to shake. The key was to avoid deflationary dynamics by doing whatever was necessary to generate inflation.

When the market crashed in 2008, therefore, Bernanke's response was to avoid the mistakes of the Great Depression. He quickly led the Fed to lower interest rates. Rates dropped from 5% in September 2008 to a goal of 0.25% to 0% by the end of that year. It's worth noting that interest rates were already pretty low (historically speaking) before the crash … because they had been lowered (and kept low) to offset the impact of the dotcom crash.

But once the price of money is zero, what else can a central banker do to stimulate the economy? In a 2002 speech to the National Economists Club, Bernanke famously cited an analogy originated by economist Milton Friedman when he said in such an environment the government can undertake a “helicopter drop” of money by cutting taxes and creating a program of open market purchases by the central bank to stimulate consumption and raise prices:

U.S. dollars have value only to the extent that they are strictly limited in supply. But the U.S. government has a technology, called a printing press (or, today, its electronic equivalent), that allows it to produce as many U.S. dollars as it wishes at essentially no cost. By increasing the number of U.S. dollars in circulation, or even by credibly threatening to do so, the U.S. government can also reduce the value of a dollar in terms of goods and services, which is equivalent to raising the prices in dollars of those goods and services. We conclude that, under a paper-money system, a determined government can always generate higher spending and hence positive inflation.[20]

By the end of the decade, the Fed chairman had his chance to put theory into practice. It was called quantitative easing (QE). Starting in 2008, the Fed bought $1.25 trillion of mortgage-backed securities in the first 16 months of QE. Including more mortgage-backed debt purchases later, along with the buying of agency securities and various Treasury bonds, the Fed's balance sheet ballooned five-fold from $882 billion in December 2008 to $4.47 trillion in May 2017—a level almost one quarter the rate of U.S. GDP. In the middle of this program of buying, in 2010, Bernanke wrote a Washington Post op-ed declaring the program was working:

Stock prices rose and long-term interest rates fell when investors began to anticipate this additional action [of purchasing assets]. Easier financial conditions will promote economic growth…. Critics have, for example, worried that it will lead to excessive increases in the money supply and ultimately to significant increases in inflation. Our earlier use of this policy approach had little effect on the amount of currency in circulation or on other broad measures of the money supply, such as bank deposits. Nor did it result in higher inflation.”[21]

It seems that Bernanke's goal was to increase the value of some assets. Whether you agree with it or not, quantitative easing such as the world's central banks have pursued has far-reaching effects on the desire of investors to prefer some assets over others. The potential impact of QE isn't completely known yet. As of this writing, the Fed is working to “normalize” its balance sheet—that is, selling assets it has and not buying new assets. If the effect of QE was to raise asset prices, will the end of it (known by some as quantitative tightening [QT]) mean a decrease in asset prices?

We've seen hints of what may come. The problem is that once the Fed started “accommodating” the market by buying assets to the tune of $85 billion per month, investors became addicted. Consider the market reaction in the spring of 2013 when Chairman Bernanke made a seemingly innocuous statement while answering questions from politicians: “In the next few meetings, we could take a step down in our pace of purchases.”

Investors, accustomed to the supportive actions of the Fed, reacted to the hint of the Fed tapering its purchases in the future like children having their iPads taken away by their parents. The “taper tantrum” of 2013 led to 10-year Treasury bond yields jumping almost 40 basis points in two days from June 19–21—the sharpest rise in long-term interest rates as determined by the market in recent U.S. monetary history—as investors pulled $7.5 billion from bond funds in one week.[22] The dollar spiked, U.S. exports became less competitive, and asset prices across the board fell, hitting emerging markets especially hard.

In a 2014 review of the taper tantrum, Christopher J. Neely of the St. Louis Fed bank suggested the reaction was positive: “The lesson from the taper tantrum is that the QE programs have had the desired effect on asset prices, suggesting that the purchases have influenced output, employment, and inflation expectations in the desired directions.”[23]

Yet consider the reality: the Fed's balance sheet actually increased after the 2013 tantrum by an additional $1 trillion by early 2014[24]. The Fed had slowed the pace at which it added assets to its ledger by about 12%, to $75 billion per month at the end of 2013. In 2015, investors threw a second tantrum as the Fed raised interest rates for the first time in more than six years. Bond funds experienced their third-highest outflows in one week. Still, the Fed balance sheet remained basically unchanged.

Given that the Fed balance sheet is only beginning to normalize (whatever that might mean!) as of this writing, and there are stated plans to continue normalizing it, how many more tantrums might we expect? As of September 2018, the Federal Reserve still had $4.2 trillion of assets on its balance sheet, more than $3.2 trillion greater than a decade earlier. Unconventional policy has produced unconventional risks: expect more investor tantrums.

The next section describes the Austrian business cycle theory, which suggests that overinvestment and excess capacity create conditions that become highly unsustainable and eventually result in a bust.

The Austrian Business Cycle Theory

The Austrian School of Economics[25] uses the banking function of connecting savers with borrowers as the basis upon which it builds a theory of boom and bust cycles. At its root, the theory posits that excessive credit growth (driven by government intervention via interest rate policies, etc.) is the root of speculative booms and busts by generating unsustainable growth. The underlying belief is that “there is an economic and moral difference between legitimate ownership that comes from deferred consumption and premature ownership that is subsidized by the monetary system.”[26]

The Austrian business cycle theory is similar in many respects to Minsky's financial instability hypothesis. According to the theory, artificially low interest rates result in bad investments and overconsumption, creating excess capacity and motivating businesses and individuals to under-save. Because central banks monopolize money creation (see the following box) and therefore affect the credit cycle, Austrian economists suggest that central banks lie at the origin of financial bubbles.

Fractional Reserve Banking and Money Creation

Banking institutions serve many roles, the most important of which is the deployment of funds from savings into productive investments. To do this, many banks collect deposits from individuals and institutions (the savers) before lending those same funds to other individuals and institutions (the borrowers).

Because savers may demand their money back from a bank at any time, banks need to keep cash on hand to meet this potential need. If the banks kept $100 on reserve for every $100 deposited with them, there would be no excess capital to lend out to the borrowers. Further, such a full-reserve approach would be inefficient in that very few savers ask for their capital to be returned in a short time. Fractional-reserve banking is a solution to these problems and is the dominant form of banking practiced today. In it, banks keep only a fraction of deposits on reserve and also maintain the commitment to meet the demands of any saver requesting a return of their capital. This process expands the supply of money in the system, primarily by allowing deposited capital to multiply.

To understand how money is created, let's use a simple example in which $1000 is deposited into Bank 1 and the mandated reserve requirement is 10%. This means Bank 1 will keep $100 on reserve and will lend out $900. But that $900 is going to end up in other banks. Those other banks will keep $90 on reserve and lend out $810 … which ends up in other banks. If we assume this process stops after 10 cycles (it need not), then approximately $6500 will exist in bank deposits. After 25 cycles, the aggregate deposits will be more than $9250. Ultimately, total deposits will reach $10,000, or 10 times the initial deposit. To generalize, simple math will demonstrate that the money multiplier is exactly [1 / (Reserve Requirement)].Thus, if the reserve requirement is 25%, total deposits will eventually reach 4× the initial deposit, or $4000. Similarly, a 50% reserve requirement will result in 2× the initial deposit, or $2000. The accompanying figure graphically demonstrates this process of money multiplication and how it varies by reserve requirement.

Money Multiplication via Fractional Reserve Banking

The use of fractional reserve banking creates a meaningful vulnerability for banks: the risk that many savers simultaneously may ask for their capital. Although in practice this happens quite rarely, a severe shock to confidence can result in bank runs in which the amount of money being demanded by savers exceeds the reserves held by the bank. For this reason, many governments have created a deposit insurance scheme, hoping to create the confidence needed to avoid bank runs.

Note: For a more complete description of the money-creation process via fractional reserve banking, I encourage you to consult Modern Money Mechanics: A Workbook on Bank Reserves and Deposit Expansion, published by the Federal Reserve Bank of Chicago. The publication was originally authored by Dorothy M. Nichols in 1961 and was later revised and updated by Anne Marie Gonczy in 1992.

Underlying Beliefs: Macroeconomics and Capital Structure

The Austrian school has three underlying beliefs that are particularly pertinent to our discussion of booms and busts: (i) equilibrium is a nonsensical construct, (ii) aggregation is not possible, and (iii) interest rates help determine preferences for consumption today vs. consumption in the future.[27] To begin, they reject the idea of equilibrium. Noble-prize winning Austrian economist Friedrich Hayek succinctly captured the perspective that equilibrium is the exception by stating “before we can even ask how things might go wrong, we must first explain how they could ever go right.”[28]

Another Austrian tenet is that aggregates are nonsensical constructs because individuals have unique tastes and time horizons that cannot be summed into singular demand curves. Llewellyn Rockwell, founder of the Ludwig von Mises Institute, eloquently captures the spirit of this second point by noting that “every actor in the economy has a different set of values and preferences, different needs and desires, and different time schedules for the goals he intends to reach.”[29]

Although there are many ways of interpreting this belief, the most popularized manner[30] of presenting the heterogeneity of capital decisions (the most relevant for our boom–bust focus) is the stages-of-production triangle, also known as the Hayekian triangle in honor of Hayek. Figure 2.1 presents a simplified version of it with five production stages.

Figure 2.1 The Hayekian Triangle and Stages of Production

Examples of production stages that might be early (i.e. stage 1) include basic research and development and other capital-allocation decisions that might be years from impacting a firm's bottom line. These earlier stages are truly “investment” stages in which today's profits are foregone in return for the expectation of future profits. Likewise, examples of late-stage production functions include inventory and working capital management. These are production functions that are temporally proximate to consumption.

Because the dynamics that affect capital-allocation decisions in early stages differ from the dynamics affecting later stages, Austrians believe that a simple aggregation is inappropriate. The slope of the triangle's hypotenuse can be thought of as an indicator of the interest rate. Steep triangles imply high interest rates and a corresponding minimization of long-run (i.e. stage 1) investments. Flat triangles imply low interest rates with corresponding emphasis on long-run investments. In many ways, the slope can be thought of as a proxy for the time value of money and therefore the hurdle rate for investment decisions.

The third Austrian belief of relevance to us is that consumption and investment trade off against each other (unlike the traditional economic interpretation that economic output is the result of consumption plus investment plus government spending plus net exports). Thus, investment is defined as foregone consumption, and likewise, consumption is foregone investment. Given that this consumption vs. investment framework necessitates a trade-off between the suppliers of funds and the demanders of funds, we are effectively talking about the market for money. The clearing price of the money is the interest rate.

Malinvestment and Overconsumption: Central Banks and Money Creation

Market-clearing interest rates, note the Austrians, allow for the optimal allocation of resources between consumption and investment. Figure 2.2 summarizes how the interest rate (i.e. the price of money that allows the supply of saving and the demand for investment capital to be matched) drives the trade-off on the consumption/investment frontier. The various stages of production then align with the consumption for an appropriate allocation of corporate resources.

Figure 2.2 How Interest Rates Drive Investment and Consumption Decisions

The involvement of central banks in setting the price of money is confusing and causes problems, assert the Austrians. (Just imagine how disturbed they must be by QE!) Central banks in democratic capitalist societies[31] are motivated to keep interest rates below their appropriate level, and the result on investment decisions is an inappropriate increase in long-run investments. By long-run, I am describing capital investments that do not provide a payback for a significant period of time. Research and development, plant expansions, and the like would be good examples of long-run investments. Increasing a sales force might be considered a short-run investment.

From the perspective of the corporate entity making investment decisions, it appears that the savings are greater than they in fact are. This distorted perspective arises due to the primary mechanism through which central banks manipulate interest rates—management of the money supply. To reduce interest rates, central banks can “manufacture” money by either lowering the interest rate by mandate or lowering required reserve ratios.

Likewise, inappropriately low interest rates cause consumption to be higher than would be the case with an appropriate rate. From the perspective of the saver, it appears that there is less demand for capital than is actually the case. With a lower opportunity cost for consuming, entities choose to consume at a level above one that would naturally occur at an appropriate interest rate. The result of this higher consumption is that corporate investment decisions are shifted increasingly toward short-run production stages. Because demand is robust, inventories are built, and so on. Figure 2.3 summarizes how inappropriately low rates result in both malinvestment (top half) and overconsumption (bottom half).[32]

Figure 2.3 Overconsumption and Overinvestment Driven by Inappropriately Low Interest Rates

Eventually, bad investments and/or overconsumption must be addressed, and an overly “consumed” society is found with too much debt (due to the low cost of money) and a need to increase savings. At this point, the bust portion of the cycle begins as savings increase (either via actual savings or via debt repayment), consumption slows, and profit maximization is deemed subservient to balance sheet repair typical of Koo's balance sheet recessions.[33] Significant excess capacity from overinvestment results in deflationary forces, which increase the real value of debt. Austrians believe that this process must be free to run its course, independent of bailouts and other government intervention, in order to purge the system.

As is clear from the diagrams and descriptions of the relationships that result in malinvestment and overconsumption, Austrians believe the root cause of the boom–bust cycle is inappropriately low interest rates. One mechanism through which central banks such as the Federal Reserve control interest rates is via deposits held at their member banks. So, the Federal Reserve creates money and then deposits it into banks. Through the money multiplier effect described earlier, these deposits are multiplied as banks lend capital to those seeking it. Simple supply and demand dynamics (i.e. more supply) drive the cost of money (i.e. the interest rate) down as a result of the money-creation process. The unlimited capability of the Federal Reserve's “printing press,” combined with the multiplicative power of fractional reserve banking, enable the Fed to effectively set short-term interest rates.[34]

Austrians note the inherent contradiction of economists who claim the “market knows best” working at the Fed. In many ways, the Federal Reserve operates as a central planning organization more typically found in communist/socialist societies.[35] Austrians believe knowledge is inherently difficult to obtain (not unlike Soros's belief regarding social science) and that any intervention into markets is inherently distortive. This belief regarding the U.S. central bank is perhaps best captured by U.S. congressman and 2008 presidential candidate Ron Paul, who notes: “After decades of experience in grappling with Fed officials in committee meetings and of lunches and private discussions with Fed chairmen, a lifetime of reading serious economic literature, and a profound awareness of the dangers to liberty in our time, I know there is absolutely no hope for the Fed to conduct responsible monetary policy.”[36]

Integrating the Macro Lenses

Although some of the lenses presented in this chapter are not fully accepted by economists, they provide powerful tools through which to recognize, evaluate, and understand booms and busts. Further, despite their seemingly disparate foci, the Austrian cycle, financial instability hypothesis, and debt-deflation theory can be integrated into one coherent theoretical construct.

Figure 2.4 notes the interaction of these components into an integrated macroeconomic lens for the evaluation of financial extremes. As shown by the circular flow, the cyclical nature of debt is at the heart of the framework, with debt and its magnifying power as the primary drivers of the cycle.

Figure 2.4 Credit Cycles Drive Constant Instability

As we move to the next chapter, the focus of our lens-building efforts turns to psychology and the cognitive biases that often affect human decision-making processes.

Notes

1. Paul A. McCulley, “The Shadow Banking System and Hyman Minsky's Economic Journey,” PIMCO's Global Central Bank Focus (May 2009).

2. George Cooper, The Origin of Financial Crises: Central Banks, Credit Bubbles, and the Efficient Market Fallacy (New York: Vintage Books, 2008).

3. Please note that by naming my aggressive borrowers the “Carefree Carrolls,” I am in no way attempting to diminish the role of aggressive lenders that convince unsuspecting borrowers into assuming more debt than might be prudent. In fact, one could easily replace the “Carefree Carrolls” with the “Duped Daltons” in this chapter.

4. Stephen Mihm, “Why Capitalism Fails,” The Boston Globe (September 13, 2009).

5. In fact, in a collection of his essays was titled Can IT Happen Again? Boston Globe writer Stephen Mihm eloquently noted that “it” was, “like Harry Potter's nemesis Voldemort, the thing that could not be named: The Great Depression.”

6. George Magnus, “Minsky for Beginners (Transcript),” UBS Emerging Economic Focus (October 12, 2009), 2.

7. Hyman Minsky, Can IT Happen Again? Essays on Instability and Finance (Armonk, NY: ME Sharpe, 1982).

8. Ibid.

9. Hyman Minsky, “The Financial Instability Hypothesis” (working paper, Jerome Levy Economics Institute of Bard College, May 1992, no. 74).

10. McCulley, “The Shadow Banking System.”

11. Fisher, a member of the Yale College class of 1888, was awarded the first ever PhD in economics from Yale University in 1891.

12. Irving Fisher, “The Debt-Deflation Theory of Great Depressions,” Econometrica (1933).

13. Ibid.

14. Ibid.

15. Ibid.

16. Richard Koo, The Holy Grail of Macroeconomics: Lessons from Japan's Great Recession (Singapore: John Wiley & Sons, 2009).

17. Michelle Fox, “People Are Taking Out Mortgages to Buy Bitcoin, Says Securities Regulator,” CNBC (December 11, 2017), https://www.cnbc.com/2017/12/11/people-are-taking-out-mortgages-to-buy-bitcoin-says-joseph-borg.html.

18. Bureau of Labor Statistics, U.S. Department of Labor, The Economics Daily, Consumer Price Index: 2017, https://www.bls.gov/opub/ted/2018/consumer-price-index-2017-in-review.htm.

19. Roger Lowenstein, “The Education of Ben Bernanke,” The New York Times Magazine (January 20, 2008).

20. Ben S. Bernanke, “Deflation: Making Sure ‘It' Doesn't Happen Here” (remarks, National Economists Club, Washington, D.C., November 21, 2002), https://www.federalreserve.gov/boarddocs/speeches/2002/20021121.

21. Ben S. Bernanke, “Aiding the Economy: What the Fed Did and Why,” op-ed column for The Washington Post (November 5, 2010), https://www.federalreserve.gov/newsevents/other/o_bernanke20101105a.htm.

22. Shelly Antoniewicz, “The Taper Tantrum—Take II,” ICI (December 13, 2016), https://www.idc.org/viewpoints/ci.view_16_bond_mf_projections.print.

23. Christopher J. Neely, “Lessons from the Taper Tantrum,” Federal Reserve Bank of St. Louis, Economic Synopses, no. 2 (2014), https://research.stlouisfed.org/publications/economic-synopses/2014/01/28/lessons-from-the-taper-tantrum.

24. Board of Governors of the Federal Reserve System, “Recent Balance Sheet Trends,” https://www.federalreserve.gov/monetarypolicy/bst_recenttrends.htm.

25. So named because of the predominantly Austrian economists (Ludwig von Mises, Friedrich Hayek, and others) who pioneered much of the early work.

26. Llewellyn Rockwell, “Why Austrian Economics Matters More Than Ever” (talk delivered to the Mises Institute Supporters Summit, November 1, 2008, Auburn, AL).

27. Roger W. Garrison, Time and Money: The Macroeconomics of Capital Structure (New York: Routledge, 2001).

28. Friedrich von Hayek, “The Pretence of Knowledge” (Nobel Prize Lecture, delivered upon accepting the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel on December 11, 1974), https://www.nobelprize.org/prizes/economic-sciences/1974/hayek/lecture/.

29. Rockwell, “Why Austrian Economics Matters.”

30. The clearest visualizations of Austrian belief are provided by Auburn University professor Roger Garrison in Time and Money. Many of the figures in the section about the Austrian business cycle theory are based on Garrison's frameworks.

31. The one notable exception took place when Paul Volker raised rates quite dramatically to contain inflation. While it is possible to imagine a democratic capitalist society keeps rates at their appropriate level or higher, in practice this has proven quite difficult to do for any extended period of time as it has the potential to result in a significant political backlash against elected officials.

32. Garrison, Time and Money.

33. Koo, The Holy Grail of Macroeconomics.

34. Murray M. Rothbard, The Case against the Fed (Auburn, AL: Ludwig von Mises Institute, 2007).

35. Ron Paul, End the Fed (New York: Grand Central Publishing, 2009).

36. Ibid.

CHAPTER 3

The Psychology Lens: HOMO ECONOMICUS MEETS HOMO SAPIENS

Investing is not a game where the guy with the 160 IQ beats the guy with the 130 IQ … Once you have ordinary intelligence, what you need is the temperament to control the urges that get other people into trouble in investing.

—Warren Buffett

The third discipline through which we evaluate financial booms and busts is psychology. We can gain a fresh perspective by focusing on actual, rather than theoretical, human behavior. One of the key underlying assumptions of most economic lenses is that humans are rational individuals. Although the term rational is one that might be interpreted in many ways, it generally refers to profit-maximizing, self-interested, and optimized decision-making. According to such “rational choice” logic, humans accurately weigh costs and benefits to make the most economically rewarding decisions.

The framework of rational choice and rational action theories has had a dominating influence over most of the social sciences in recent years, stemming in large part from the success it achieved in influencing the economics discipline. According to John Scott at the University of Essex, “what distinguishes rational choice theory is that it denies the existence of any kinds of actions other than the purely rational and calculative.”[1] In fact, rational choice logic goes further to describe individual preferences in terms of utility functions—functions that are transitive (i.e. if A is preferred to B and B is preferred to C, then A is preferred to C), complete (i.e. for any choice between options X and Y, an individual will either prefer X, prefer Y, or be indifferent between the two), and invariable (if L is preferred to M, then M is not preferred to L).[2]

To illustrate completeness, consider the choices made by a woman named Sara. If Sara is presented a choice between apples and oranges, she will choose an apple, choose an orange, or be happy with either. She will not answer, as a three-year-old might, “I want strawberries.” Suppose that Sara chooses the orange. But before she is able to peel the orange, she's given the option of strawberries. She can now choose the orange, choose the strawberries, or be indifferent between the two. She chooses the strawberries.

Sara has now revealed her preferences to us in a manner that means strawberries are preferred to oranges, and oranges are preferred to apples. According to rational choice theory, Sara has also revealed that strawberries are preferred to apples (even though she did not explicitly make such a choice). From an academic research perspective, these simplifying assumptions of completeness and transitivity provide a robust foundation for the application of scientific methods, regardless of their accuracy.[3] By enabling the modeling of human choice into equations and formulas, these assumptions enable physics-like “rigor.”

It is only relatively recently that psychologists have demonstrated that humans may not be “rational” in the sense just described. In fact, according to one behavioral perspective, it seems that “human beings are motivated by cognitive biases of which they are largely unaware (a true invisible hand if ever there was one).”[4] Through hundreds of empirical studies, psychologists have found that people often make suboptimal decisions for a variety of reasons, including incomplete accounting of costs and benefits, partial risk understanding, and flawed assumptions regarding the probabilities of various outcomes. As psychologists and economists began to share ideas, a burgeoning new field of study was born: behavioral economics.

If ever an entire discipline and field can be traced back to the pioneering work of one or two people, then behavioral economics provides a great example. Princeton professor Daniel Kahneman and the late Amos Tversky are the original pioneers of the research that has now become known as behavioral economics. In 2003, addressing the American Economic Association, Kahneman reflected on the original confusion that motivated his research:

My first exposure to the psychological aspects of economics was in a report that Bruno Fey wrote on that subject in the early 1970s. Its first or second sentence stated that the agent of economic theory was rational and selfish, and that his tastes did not change. I found this list quite startling, because I had been professionally trained as a psychologist not to believe a word of it.[5]

This chapter uses findings from recent research in this new field to demonstrate that we are all plagued by predictable and consistent biases that affect our decision-making in a manner that directly conflicts with our supposed rationality. In the study of booms and busts, it is essential to understand why people behave the way they do. Some of the research findings presented in this chapter help us understand why irrationality may be the norm, rather than the exception, making booms and busts more likely than stability. This chapter connects the dots between the observation of booms and busts and the individual actors that create them.

The Study of Irrationality Is Born

Take a moment to complete the test shown in Table 3.1, as originally presented by J. Edward Russo and Paul Shoemaker in Winning Decisions: Getting It Right the First Time.[6] For each question, write your best estimate of the correct answer in column marked Estimate.

Table 3.1 A Simple Knowledge Test

Source: From Winning Decisions by J. Edward Russo and Paul J.H. Schoemaker, copyright © 2002 by J. Edward Russo and Paul J.H. Schoemaker. Used with permission of Doubleday, a division of Random House, Inc.

Question

Estimate

?

1. What is the weight of an empty Airbus A340–600? (kg or tons)

2. In what year did John Steinbeck win the Nobel Prize for Literature?

3. What is the distance from the Earth to the Moon? (km or miles)

4. What is the air distance from Madrid to Baghdad? (km or miles)

5. In what year was the construction of the Roman Colosseum completed?

6. What is the height of the Aswan High Dam? (meters or feet)

7. In what year did Magellan's crew complete the first naval circumnavigation of the globe?

8. In what year was Mohandas K. Gandhi born?

9. What is the surface area of the Mediterranean Sea? (square km or miles)

10. What is the gestation period of the great blue whale? (days)

Upon completion of this exercise, go back and use the next two columns to place a range around your point estimate. Don't modify your original estimate. Try to make the range as narrow as possible, but wide enough that you have 90% confidence that the correct answer lies within the range. To truly appreciate the value of this chapter, complete the exercise before proceeding any further.

Table 3.2 provides the correct answers. Check and see how many of the answers lie within the ranges you provided. Use the ? column to mark an X for those you missed and a ✓ for those you got correct. Count the number of ✓s. Is it less than 9? A number of less than 9 indicates overconfidence, as the instruction was to state a range within which you believed with 90% confidence that the answer lay. This means you should be correct 9 times out of 10.

Table 3.2 Answers to the Simple Knowledge Test

Source: From Winning Decisions by J. Edward Russo and Paul J.H. Schoemaker, copyright © 2002 by J. Edward Russo and Paul J.H. Schoemaker. Used with permission of Doubleday, a division of Random House, Inc.

Question

Answer

1. What is the weight of an empty Airbus A340–600? (kg or tons)

218,000 kg or 240 tons

2. In what year did John Steinbeck win the Nobel Prize for Literature?

1962

3. What is the distance from the Earth to the Moon? (km or miles)

384,400 km or 238,850 miles

4. What is the air distance from Madrid to Baghdad? (km or miles)

4,308 km or 2,677 miles

5. In what year was the construction of the Roman Colosseum completed?

A.D. 80

6. What is the height of the Aswan High Dam? (meters or feet)

114 m or 375 ft

7. In what year did Magellan's crew complete the first naval circumnavigation of the globe?

1522

8. In what year was Mohandas K. Gandhi born?

1869

9. What is the surface area of the Mediterranean Sea? (square km or miles)

2,510,000 sq km or 970,000 sq miles

10. What is the gestation period of the great blue whale? (days)

335 days

Although many might argue that the exact questions are somewhat arbitrary and therefore only test random knowledge, the test has proven robust as a test of metaknowledge[7] and overconfidence. As noted by Russo and Shoemaker,

Of the more than two thousand American, Asian and European managers to whom we have given a ten-question quiz like this one, less than 1 percent met the challenge. Most fail to be even in their range (not the point estimate) for four to seven questions out of ten.[8]

Few people are able to do well at this test. I have personally given this test to hundreds of undergraduates, MBA students, managers, colleagues, and others, both inside and outside the United States. Most score between 3 and 6 correct answers. Although this may seem to be aberrational, it is actually quite reflective of a consistently biased human decision-making process. Humans appear to be routinely overconfident and unaware of their own knowledge limitations. As you might imagine, the inability to estimate accurately and other such behavioral biases throws quite a wrench into the study of “rational” individuals and their behavior during booms and busts. In addition to exploring how we make decisions, the rest of the chapter explores why we consistently produce less-than-optimal decisions.

Heuristics Gone Wild: How Rules of Thumb Lead Us Astray

Beginning in the early 1970s, Amos Tversky and Daniel Kahneman started empirically analyzing how people make decisions in uncertain situations. What they found was interesting and, at first glance, contradictory to the economic perspective of rational actors. Rather than accurately weighing the costs and benefits (including the probabilities of each) of each decision made (like a supercomputer might), humans tended to rely on a handful of heuristics or “rules of thumb” to simplify the complexity of a cost/benefit analysis for each decision. In most circumstances, “these heuristics are quite useful, but sometimes they lead to severe and systematic errors.”[9]

The simple example used by Tversky and Kahneman does an excellent job of illustrating the power of a heuristic and its ability to mislead us in a consistent manner:

The apparent distance of an object is determined in part by its clarity. The more sharply the object is seen, the closer it appears to be. This rule has some validity, because in any given scene the more distant objects are seen less sharply than nearer objects. However, the reliance on this rule leads to systematic errors in the estimation of distances …[10]

By employing a heuristic such as “sharpness = nearness,” we find that we are prone to overestimate distances in conditions of poor visibility and underestimate distances when visibility is good. Thus, a normally useful rule of thumb (clarity as a measure of distance) can systematically mislead us into inaccurate estimation of distances.

Significant research over the past 40 years has focused on what might be deemed the two primary heuristics utilized by most human decision-makers that tend to bias our decisions. Although we humans likely employ dozens, if not hundreds, of other heuristics, these two rules of thumb tend to be consistently problematic in terms of our ability to make good decisions. More specifically, they each result in biases to our decision-making processes that drive suboptimal outcomes due to consistently poor estimation of the probabilities and values needed for rational optimization.

We now turn to a discussion of the representativeness heuristic and the availability heuristic, as well as a brief exploration of the cognitive biases that they each cause. Although the implications of these findings for our study of booms and busts should be obvious, the chapter wraps up by highlighting several ramifications pertinent to our study of booms and busts.

Representativeness

The representativeness heuristic is a rule of thumb we all use to form a conclusion when presented with data that looks like other data that we know to be true. Not dissimilar to the concept of stereotyping, in which we assume one member of a group must be like the image we have of the group, the representativeness rule of thumb is one in which we use identifiable clues to cognitively label an individual observation as representative of a class of observations.

For example, suppose you are walking down a street in Boston and meet a person who is thin, athletic looking, and wearing sneakers, and who asks for directions. Suppose further that it happens to be two days before the Boston Marathon. It is highly likely that you will assume that this person is an athlete visiting Boston to run the marathon. Likewise, if you meet a disheveled-looking, absent-minded, poorly dressed older individual carrying a disorderly stack of papers near a college campus, you might hypothesize, with good reason, that the person is a professor.

Although these rapid conclusions drawn from limited data may be accurate, they need not always be. In fact, let's turn to an example (based upon Tversky and Kahneman's work) in which the representativeness heuristic might lead you astray. Suppose a colleague describes an individual (let's call him Stanley) as being “shy, withdrawn, extremely helpful, detail oriented, meek yet tidy, and extremely interested in structure.”[11] Now suppose you are asked to guess what this person does for a living (farmer, car salesman, secretary, artist, physician, librarian, etc.) and to order the list of possibilities from most to least likely. For lots of people, Stanley is thought to be a librarian.

The process by which most people would order the list is based on the similarity of the description to their stereotyped image of each occupation. Although using this heuristic may be efficient in determining the identification of the person's occupation, it unfortunately creates numerous biases and may therefore generate incorrect conclusions. The biases emanating from this heuristic occur because representativeness allows little room for the necessary probability-affecting variables that should influence our thinking. Five primary cognitive biases[12] emerge from our extensive use of the representativeness rule of thumb. Let us now turn to each and discuss them in turn.

Inattention to base rates The fact that Stanley resembles our image of a librarian absolutely overwhelms our awareness of base rates. Base rates are the frequency with which a type occurs in a population. In the previous example, if we knew nothing about Stanley and were asked to rank his likely profession, we would probably turn to our knowledge of base rates to determine the ranking. So, given that not many individuals are actually librarians, the probability of Stanley being a librarian would likely fall. Likewise, the probability that Stanley is a physician would perhaps rise (given there are more physicians in the world than librarians).

Thus, it seems that our brains overweight representativeness (i.e. similarity of description) and underweight the generic probability of an occurrence. The result of our brain's reliance on the representativeness heuristic is that our decision-making processes suffer from a cognitive bias in which we underweight or even dismiss statistical likelihoods (e.g. the number of physicians is many times the number of librarians in America) that should inform our thinking.

Insensitivity to sample size Tversky and Kahneman[13] asked undergraduates to answer the following question:

A certain town is served by two hospitals. In the larger hospital about 45 babies are born each day and in the smaller hospital about 15 babies are born each day. As you know, about 50% of all babies are boys. However, the exact percentage varies from day to day. Sometimes it may be higher than 50%, sometimes lower.

For a period of 1 year, each hospital recorded the days on which more than 60% of the babies born were boys. Which hospital do you think recorded more such days?

1. The larger hospital

2. The smaller hospital

3. About the same (i.e. within 5%)

More than 50% of respondents indicated C was their choice, with the remaining respondents evenly split between A and B. According to statistical theory, however, the larger the sample, the less likely it is to stray from an average. If this is not obvious, think about the probability of getting 75% heads while flipping a coin. Surely it must be easier to get 3 heads when flipping a coin 4 times than it would be to get 3,000 heads when flipping a coin 4,000 times, right? Tversky and Kahneman note that this “fundamental notion of statistics is evidently not part of people's repertoire of intuitions.”[14]

Again, we find that our cognitive processes are biased in a manner that leads to suboptimal decision-making. We tend to extrapolate our thinking across all scenarios, disregarding the likelihood of deviations that might exist due to limited sample sizes.

Misconceptions of likelihood (the gambler's fallacy) Consider the following question from Bazerman and Moore's spectacular review of the academic literature on behavioral decision-making:[15]

You and your spouse have had three children together, all of them girls. Now that you are expecting your fourth child, you wonder whether the odds favor having a boy this time. What is the best estimate of your probability of having another girl?

1. 6.25% (1 in 16), because the odds of getting four girls in a row is one out of 16

2. 50% (1 in 2), because there is roughly an equal chance of getting each gender

3. A percentage that falls between these two estimates (6.25% and 50%)

Most respondents chose either A or C, believing that the probability of having another girl must be low. Specifically, our brains want us to assume that random deviations will be offset over time. However, as noted by Bazerman and Moore, “the problem with this reasoning is that the gender determination of each new baby is a chance event; the sperm that determines the baby's gender does not know how many other girls the couple has.”

The situation is not dissimilar to the thinking of a gambler at a roulette wheel. Having just witnessed five consecutive red numbers emerge, he is convinced that the probability of the next number being black is higher than it actually is. In fact, depending on the type of roulette wheel, the probability will always be either 18 out of 37 or 18 out of 38. These likelihoods do not change.

This miscalculation of chance is driven by our brain's desire for order, even when there may be randomness. Tversky and Kahneman correctly summarize: “Chance is commonly viewed as a self-correcting process in which a deviation in one direction induces a deviation in the opposite direction to restore the equilibrium. In fact, deviations are not ‘corrected’ as a chance process unfolds, they are merely diluted.”[16]

Dismissing the powers of regression Even though most humans believe that randomness from an underlying statistic is likely to be quickly corrected, for some reason we do not believe that performance regresses. When professional athletes do really well, we are willing to assume that it is due to skill. When one mutual fund does well while others falter, we are willing to assume that it is due to skill. A well-cited example is taken from Kahneman and Tversky's early work on the psychology of prediction in which poor performers were punished and good performers were rewarded. Despite these interventions, the poor performers improved, while the good performers deteriorated. The authors suggest that interventions may have had absolutely nothing to do with performance and that simple regression to the mean drove the outcomes.[17] By relying too greatly on a single data point (and believing it to be highly representative), our brains lead us to overestimate the capabilities of high performers and to underestimate the capabilities of low performers.

Conjunction fallacy One rule of statistics is that the probability of a subset cannot be more than the probability of the whole set. Thus, the probability of being a blond woman from Boston must necessarily be less than or equal to the probability of being a woman, the probability of being blond, and the probability of being from Boston. It is not possible that there is a 50% chance of being a blond woman from Boston and a 5% chance of being from Boston. Even if only 5% of the population is from Boston, then if all Bostonians are female and blond, the probability of being a blond woman from Boston will also equal 5%. However, if some portion of Bostonians is not female, or blond, then the probability of being (i) blond, (ii) female, and (iii) from Boston will be less than 5%.

The representativeness heuristic, however, plays games with our probabilistic assessments. If we went on to describe the woman just mentioned as having a heavy New England accent in which she pronounces words like car as “cah,” our brains would act as if we placed a greater probability of the person being a Bostonian woman than being a woman. Consider the following example from Bazerman and Moore:[18]

Linda is thirty-one years old, single, outspoken, and very smart. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and she participated in antinuclear demonstrations. Rank the following eight descriptions in order of the probability (i.e., likelihood) that they describe Linda.

1. Linda is a school teacher in an elementary school.

2. Linda works in a bookstore and takes yoga classes.

3. Linda is active in the feminist movement.

4. Linda is a psychiatric social worker.

5. Linda is a member of the League of Women Voters.

6. Linda is a bank teller.

7. Linda is an insurance salesperson.

8. Linda is a bank teller who is active in the feminist movement.

Most respondents to this question ranked C as more likely than H and H as more likely than F. This means people in this study believed it more likely that Linda was a feminist bank teller than a bank teller. Given that the feminist descriptor actually narrows the universe (as feminist bank tellers are a subset of feminists and a subset of bank tellers), it is impossible for the probability of being a feminist bank teller to exceed that of being a bank teller (or being a feminist).

Because the description of Linda is more representative of a feminist bank teller than that of a bank teller, use of the representative heuristic leads us to a logical inconsistency in that we place a higher likelihood on a less-likely occurrence.

Availability

The second primary bias-inducing rule of thumb is the availability heuristic. As eloquently summarized by Tversky and Kahneman, “there are situations in which people assess the frequency of a class or the probability of an event by the ease with which instances or occurrences can be brought to mind.”[19] Thus, the availability heuristic is one in which personal experience and personal knowledge of events are more heavily weighted than an objective person might consider appropriate. Because our minds will more easily recall events of greater frequency, the availability heuristic is generally useful in that events with greater frequency are usually more likely. The breakdown occurs because “availability is affected by factors other than frequency and probability”[20] and is more heavily weighted toward events that are easily remembered or more memorable.

Thus, vividness and memorability (which are unrelated to frequency) are overweighted in terms of our assessment of probability. This results in a consistent and predictable set of cognitive biases. As succinctly summarized by Bazerman and Moore, “We too easily assume that our available recollections are truly representative of the larger pool of events that exists outside of our range of experience.”[21] The two primary decision-making biases that emanate from use of the availability heuristic are biases due to the ease of recall and biases due to the retrievability of the image or event. We now turn to each of them.

Ease-of-recall bias Because recent and vivid experiences tend to be more prominent in our cognitive processing, we tend to believe they occur more frequently than they actually do. Consider the following problem, presented by Bazerman and Moore:[22]

Please rank order the following causes of death in the United States between 1990 and 2000, placing a 1 next to the most common cause, a 2 next to the second most common cause, etc.

__Tobacco

__Poor diet/physical inactivity

__Motor vehicle accidents

__Firearms (guns)

__Illicit drug use

Now please estimate the number of death caused by each of these five causes between 1990 and 2000.

According to the Journal of the American Medical Association,[23] the most common cause of death on the list is tobacco, followed by poor diet/physical inactivity. The next three causes of death are motor vehicle accidents, firearms, and illicit drug use. Although few respondents actually correctly ranked the frequency of these causes, even fewer were able to accurately describe the relative frequencies. The following list gives the raw data for actual deaths:

Tobacco

435,000

Poor diet

400,000

Motor vehicles

43,000

Firearms

29,000

Drugs

17,000

Because the last three causes are more vivid and easily recalled (perhaps due to media attention that increases their vividness in our memories), most of us tend to overestimate the frequency of their occurrence, while underestimating the likelihood of the less-vivid causes. The availability of memorable stories about drug, firearms, and motor accident–related deaths is driven by more prominent and vivid stories about them. Thus, we tend to believe our immediately recent experience is more reflective of reality than it may in fact be.

Retrievability-based biases A retrievability bias occurs when we overestimate the probability of an event because our memory structures make it more retrievable. For instance, try to answer the following question relatively rapidly: Are there more words that begin with the letter a or words in which the letter a is the third letter?

Most people believe there are more words beginning with the letter a than words in which the third letter is a. Reality, however, is quite different. Roughly 6% of English words begin with the letter a, whereas almost 9% have the letter a as the third letter. For most people, it is easier to recall words that begin with the letter a. Because our memory structures allow for greater retrievability of words beginning with the letter a, we overestimate their frequency.[24]

An example used by Tversky and Kahneman concerns the relative fame of people:

Subjects heard a list of well-known personalities of both sexes and were subsequently asked to judge whether the list contained more names of men than of women. Different lists were presented to different groups of subjects. In some of the lists, the men were relatively more famous than the women, and in others, the women were relatively more famous than the men. In each of the lists, the subjects erroneously judged that the class (sex) that had the more famous personalities was the more numerous.[25]

Thus, we tend to believe whatever is more retrievable from our experience set is more frequent, when actual frequency has very little to do with our ability to retrieve actual data. Unfortunately, the media tends to exacerbate this problem by highlighting events that either sell newspapers or generate loyal watchers, rather than statistically representative stories. Thus, despite the fact that very few graduate students become billionaires, the story of Google founders Larry Page and Sergey Brin has received tremendous attention—likely leading to a perception that successful graduate student-turned-entrepreneurs are more likely than the data might suggest. Might this inspire graduate students to pursue riskier ventures than they should? Or perhaps venture capitalists are more likely to fund long shots because of such biases?

Our Flawed Brains: Other Cognitive Issues

In addition to these two primary rules of thumb and their numerous accompanying biases, there are a host of other cognitive issues that disrupt our ability to be economically rational in our optimization efforts. Several such issues are discussed in this section, with a brief mention of how they may each affect boom–bust cycles.

The Power of Irrelevance: Anchoring and Adjustment

Recall the 10 questions you answered in Table 3.1, ranging in topic from distances to years to areas. In most cases, you were asked to estimate an unknown number. Chances are high that after you came up with your best point estimate, the number you wrote in the Estimate column, you created an insufficiently wide range around that number. Such insufficient adjustment complicates our assessment of what we think we know. As you might guess, the ramifications of anchoring and adjustment have profound implications for the analysis of individual securities prices.

When attempting to estimate an unknown quantity, most of us usually begin with an initial guesstimate and then adjust the value appropriately to reflect modifications we deem appropriate. In most cases, we tend to “anchor” on our initial guesstimate and subsequently make insufficient or inadequate adjustments. The following example is taken from Tversky and Kahneman:

In a demonstration of the anchoring effect, subjects were asked to estimate various quantities, stated in percentages (for example, the percentage of African countries in the United Nations). For each quantity, a number between 0 and 100 was determined by spinning a wheel of fortune in the subjects’ presence. The subjects were instructed to indicate whether that number was higher or lower than the value of the quantity, and then to estimate the value of the quantity by moving upward or downward from the given number. Different groups were given different numbers for each quantity, and these arbitrary numbers had a marked effect on estimates. For example, the median estimates for the percentage of African countries in the United Nations were 25 and 45 for groups that received 10 and 65, respectively, as starting points. Payoffs for accuracy did not reduce the anchoring effect.[26]

This is quite a profound finding. People are influenced by knowingly random numbers when asked to determine an unknown quantity. This is hardly the stuff of “rational” humans. If a stock were trading for $300 per share, despite what your own analysis might suggest, you're less likely to think it is worth $100 per share than if the stock were currently trading at $150. Why? Because the $300 price provides a powerful anchor that prevents us from placing an appropriately large range around possible values. It also creates a mind-set that focuses on adjustments to current values, rather than the absolute values themselves. Think of how much attention is paid to the relative performance of mutual funds or other asset classes over time. Does it really matter if a stock fund was up 75% in 2009 if it was down 95% in 2008? Might such performance comparisons be counterproductive? In times of excess, such anchoring can be extremely powerful motivation for non-economic behavior.

Framing and Preference Reversal

Is how you ask a question more important than what you ask? Surely what you ask is more important than how you ask it, right? Perhaps not. Original research conducted by Kahneman and Tversky on prospect theory suggests otherwise. Some of their groundbreaking work on the topic demonstrated that how a question is asked can often impact the answer received. Consider the following example, taken from their 1981 article titled “The Framing of Decisions and the Psychology of Choice”:[27]

Imagine that the United States is preparing for the outbreak of an unusual Asian disease, which is expected to kill 600 people. Two alternative programs have been proposed. Assume that the exact scientific estimates of the consequences of the programs are as follows:

If Program A is adopted, 200 people will be saved.

If Program B is adopted, there is a 1/3 probability that 600 people will be saved and a 2/3 probability that no people will be saved.

Which of the two programs would you favor?

For those who responded to this question, more than 70% were risk-averse enough to take the sure thing of saving 200 lives, despite the equivalent probability-weighted value. Tversky and Kahneman then rephrased the descriptions of the program consequences. For this second group, they gave the same preface and then the following options:

If Program 1 is adopted, 400 people will die.

If Program 2 is adopted, there is a 1/3 probability that nobody will die and a 2/3 probability that 600 people will die.

Which of the two programs would you favor?

For this set of options, almost 80% of respondents chose Program 2. Mathematically, Program A and Program 1 are equivalent, while Program B and Program 2 are equivalent. Despite these characteristics, a significant reversal of preference took place simply by changing the manner in which the question was asked. Why?

Cutting straight to the punch line, it turns out that most humans are risk averse when facing choices about gains and risk seeking when facing choices about losses. Basically, we're willing to lock in a sure gain (a win is a win, we don't necessarily need the largest win), but we hate the idea of a sure loss (so we're willing to gamble against larger possible losses).[28] Because Programs A and B were described in terms of gains, most respondents were risk averse and sought to lock in the gains of 200 saved lives. However, when faced with a different framing of the problem, one based on the sure loss of 400 lives, respondents were willing to risk a loss of 600 lives rather than lock in a sure loss of 400.

The fact that people are able to change their preferences between identical options depending on how the question is asked violates a fundamental belief of rational choice theory—the belief that preferences are invariable—because if preferences vary, it becomes extraordinarily difficult to model human behavior. Consider the following example, which notes the power of “free.”

In a recent study, people were offered a choice of buying either a Hershey's Kiss for a penny or a Lindt chocolate truffle for 15 pennies. A large majority of respondents (greater than 70%) chose the Lindt truffle. However, when given the choice was between a free Hershey's Kiss or a 14-cent Lindt truffle, almost 70% of respondents chose the Hershey's Kiss.[29]

How “Fairness” Impacts Decisions

As Hurricane Harvey was coming toward Texas in 2017, there were the usual press reports and images of empty store shelves as consumers stocked up on food and water in anticipation of disaster. Not surprisingly, as the massive storm made landfall, there was a lack of available goods. One profit-minded manager at a Best Buy store started selling cases of bottled water for $42 and cases of Smartwater, an electrolyte-enhanced drink, for $29, helpfully noting there was a “limited supply.” Both were well above their regular prices in the area. The widespread reaction was outrage. Many people (on social media) vented their disgust at this example of a store taking advantage of a natural disaster to raise prices. Best Buy's corporate spokesperson quickly apologized for the store manager's action, noting that their focus should be on helping people and highlighting that also, by the way, Best Buy stores don't normally stock water.

Surprisingly (or maybe not, depending on your experience), a large number of economists argue that such price gouging is a public good and we should applaud it. The essence of the argument is that really desperate people will gladly pay for needed goods, which are available because of their high prices, while low prices encourage people who don't have an immediate need for water to hoard and leave none for the needy. Further, the high prices will incentivize others to bring more needed goods to market. Think of it as the Uber-esque “surge” pricing for goods. Although this makes sense to an economist, an average person might find such behavior incomprehensible.

In a series of questions given to respondents in the mid-1980s, Kahneman, Knetsch, and Thaler demonstrated that fairness affects how we make decisions.[30] They posed a series of questions to various respondents on the topic of price changes. Two categories of questions were presented: one reflecting a need on the part of the companies to protect their profits (i.e. a situation in which costs had risen) and the other in which companies were exploiting increased market power due to a shift in demand. The findings were remarkably consistent in acknowledging the need for a return on capital (i.e. it is acceptable to protect profits) and the nonnecessity of exploitation due to a supply or demand shock (i.e. profiteering).

One of the more interesting types of questions asked in the academic research on fairness is an “ultimatum” question.[31] It goes something like this. You and a random person are greeted by a third person seeking to give away money (let's just say $10,000). The person indicates that your newfound friend will propose a split, and that you will have to agree to the split in order for the two of you to receive the money. If you disagree, then neither of you will receive anything and he will move on to two other random people. You and your “friend” agree to play, and she then proposes the following split: $9,900 for her, $100 for you. Do you accept?

Most people reject the offer. From a strictly economically rational perspective, $100 is more than $0, so you would be better off accepting the offer. Why reject this offer? There are two primary ways to consider this action: you have agreed to pay $100 to punish your “friend” for being unfair, or you are unwilling to accept an unfair deal. If humans exhibit even some desire for fairness (as appears to be the case), the microeconomic assumption of self-interested individuals falls apart. The ramifications of fairness on the formation and subsequent deflation of asset bubbles is likely to be quite high.

Mental Accounting: Why a Dollar Is Not a Dollar

A dollar in your wallet is valued the same as a dollar on your dresser or a dollar in your car, correct? Recent research suggests this may not be the case. A phenomenon called mental accounting is humorously (and accurately) depicted in the following story, taken from Belesky and Gilovich:[32]

By the third day of their honeymoon in Vegas, the newlyweds had lost their $1,000 gambling allowance. That night in bed, the groom noticed a glowing object on the dresser. Upon closer inspection, it was a $5 chip they had saved as a souvenir. Strangely, the number 17 was flashing on the chip's face. Taking this as an omen, he donned his green bathrobe and rushed down to the roulette tables, where he placed the $5 chip on the square marked 17. Sure enough, the ball hit 17 and the 35–1 bet paid $175. He let his winnings ride, and once again the ball landed on 17, paying $6,125. And so it went, until the lucky groom was about to wager $7.5 million. Unfortunately, the floor manager intervened, claiming that the casino didn't have the money to pay should 17 hit again. Undaunted, the groom taxied to a better-financed casino downtown. Once again he bet it all on 17—and once again it hit, paying more than $262 million. Ecstatic, he let his millions ride—only to lose it all when the ball fell on 18. Broke and dejected, the groom walked the several miles back to his hotel.

“Where were you?” asked his bride as he entered their room.

“Playing roulette.”

“How did you do?”

“Not bad. I lost five dollars.”

This story, which highlights the issue of mental accounting better than any other I've heard, illustrates one of the many behavioral biases that affect gamblers. The truly rational person will treat each dollar she has in her pocket as her dollar, not to be trivially disposed of or imprudently risked. Surely then it wouldn't matter how the dollar got into her pocket, would it? As this story illustrates, however, the idea of “house money” contradicts this concept. Economist Richard Thaler has demonstrated several other similar mental accounts that fundamentally violate the concept that money should be thought of as fungible.[33]

The classic and most commonly utilized demonstration of mental accounting is visible in the following questions. Answer them as honestly as possible. Suppose you arrive at a concert and realize that you lost your ticket, which cost $200. Fortunately, similar seats are available for $200. Do you buy another ticket? Now for the second question. Suppose you arrive at a concert to buy a ticket, but realize that you lost $200 in the parking lot on the way to the ticket booth. Fortunately, you happen to have enough money to still buy the ticket. Do you go ahead and buy it?

Most people answering questions like this tend to answer no to the first question and yes to the second question. Why is that? Well, it seems that individuals consider the first scenario (buying two tickets) to be equivalent to spending $400 on entertainment, which may exceed one's budget. The second scenario, however, is treated differently because there is a $200 loss and a $200 entertainment expense. Although related and unfortunate, they fall in separate mental accounts.

Such mental accounts create massive complications in the study of booms and busts if investors have made a great deal of money in the bubble-formation stage. Might investors be less logical/rational if they are playing with “house money”? What if you had purchased a stock for $5 and it was now trading for $50? Would you sell your whole position, or perhaps only 10% of it? By selling 10% of your position, you are now effectively playing with “house money” and might be more willing to let it ride than rational decision-making might suggest.

The Endowment Effect: Why It's Worth More to You if You Already Have It

Another effect that has been repeatedly demonstrated among behavioral studies of decision-making is called the endowment effect. This label is used to describe the impact that ownership has on perceived value, or, more specifically, the difference between our willingness to pay (WTP) and our willingness to accept (WTA) a price for a good. Rational agents do not suffer from a difference between these two prices, something noted by the use of the term indifference to describe the curves that graphically illustrate trade-offs.

In actuality, however, people value what they have more highly than if they did not have it. Consider the following example, taken from Kahneman, Knetsch, and Thaler, in which the professors conducted a now-famous study using coffee mugs.[34] They presented mugs to members of a Cornell undergraduate class and asked them each to evaluate the mug. Then they told some students that the mug was theirs to keep. Finally, they asked everyone in the class to place a price on the mug. Those who owned the mug would decide the price at which they would sell the mug (WTA), and those who did not own the mug would decide the price at which they would buy the mug (WTP). The average WTA was over $5, and the average WTP was slightly more than $2. This difference between the WTA and the WTP illustrates the endowment effect.[35] In numerous studies, the ratio between selling prices and buying prices has been demonstrated to be between 2 and 5 times.[36]

The ramifications of the endowment effect, particularly when combined with other biases and cognitive effects, can have dramatic impacts on the financial markets. If people value what they own at up to 5× the price they would willingly buy the same good, you can imagine how severe the bust phase of a boom–bust cycle might be once forced selling results in prices falling dramatically. How likely are you to part with an asset when willing buyers are offering prices that are well below your perceived value of the asset?

The Congruence Heuristic and the Confirmatory Bias

The congruence heuristic,[37] also known among academic psychologists as the positive hypothesis testing heuristic, is one in which our thinking process is systematically skewed in a manner to validate hypotheses as true, rather than testing them.

Consider the approach most people take to a question asking if something about a group of people is true. The first step (usually) is to recall individuals you may know who are in the group. The second step is to ask if those individuals fit the criteria being asked. Based on your answer to the second question, you conclude you have a reasonable answer. For example, think about how you might answer the question of “Are couples living in rural settings more likely to have bigger families than couples living in urban settings?”

Optimal decision-making processes, however, have to do with evaluating four separate groups: rural couples with big families, rural couples with small families, urban couples with big families, and urban couples with small families. Only after considering all four sets can we accurately assess the frequency of each occurrence relative to each other. However, most people seeking to answer the question will immediately attempt to think about rural families they know and compare them with urban families they know (actually also falling prey to the availability bias in the process).

Again, it is easy to see how this limited “research” process that our brains conduct leads us astray in financial markets. Particularly when combined with other cognitive effects (the endowment effect, anchoring, etc.), we can easily imagine situations in which we are too slow to acknowledge evidence that contradicts our own views regarding a stock or bond, and it is only reluctantly that we would alter our assessments of its value. On an aggregated basis, this effect can have a massive impact when applied to millions of participants simultaneously.

The Certainty of Uncertainty

Given that making decisions in uncertain environments is a certainty of life, it is critical that we understand the ways that our brains miscalculate and misinterpret data. For the purposes of studying booms and busts, however, the primary insights of this chapter are summarized in Table 3.3.

Table 3.3 Human Irrationality and Financial Booms and Busts

Bias/Effect

Investment Implication/Example

Base Rate Insensitivity

Google as representative of startup success

Sample Size Insensitivity

“Couldn't have been random”

Likelihood Misestimation

Patterns must correct; “odds now in my favor”

Regression Dismissal

Belief in trend, “grow to the moon”

Conjunction Fallacy

Misextrapolation of small to big

Recall

College dropout entrepreneurs = Bill Gates, Mark Zuckerberg

Retrievability

False belief in rarity of an occurrence

Anchoring/Adjustment

Stock is $300, surely correct value can't be $100

Framing

Upside/downside confusion

Mental Accounting

“House money” effect; reluctance to sell; “Let it ride”

Fairness

Righteousness versus profit-maximization

Endowment

Reluctance to sell; illiquidity

Congruence

Self-validation of conclusions, misinterpreted as “testing”

As highlighted in the table, the numerous biases that plague human decision-making processes have the potential to severely distort the rationality of individual actors. Although all of these biases (and others) are important in seeking a better understanding of financial extremes, the cases presented in Part II will show a greater emphasis on the concept of overconfidence. In many ways, the idea of “this time is different” (a phrase that often typifies the boom phase of a cycle) is the ultimate manifestation of overconfidence. As we shall see in later chapters, however, the availability bias, anchoring, insufficient adjustment, and other decision-making flaws also rear their ugly heads in ways that consistently lead to nonrational decisions. It is this consistency of the biases that proves problematic, for if humans were inconsistently irrational, individual irrationality would offset other individual irrationality, resulting in a population that was rational.

Let us now turn to the political lens to understand how property rights and the price mechanism can lay the very basic foundations on which booms and busts thrive.

Notes

1. John Scott, “Rational Choice Theory,” in Understanding Contemporary Society: Theories of the Present, eds. Gary Browning, Abigail Halclim, and Frank Webster (London: Sage Publications, 2000).

2. Steven Green, “Rational Choice Theory: An Overview” (working paper, Baylor University, May 2002).

3. Friedrich A. von Hayek, “The Pretence of Knowledge” (Nobel Prize Lecture, December 11, 1974), https://www.nobelprize.org/prizes/economic-sciences/1974/hayek/lecture/.

4. Dan Ariely, “The End of Rational Economics,” Harvard Business Review (July-August 2009): 78–84.

5. Daniel Kahneman, “A Psychological Perspective on Economics,” The American Economic Review 93, no. 2 (May 2003): 162.

6. J. Edward Russo and Paul Shoemaker, Winning Decisions: Getting It Right the First Time (New York: Currency Books, 2002) 79–80.

7. Metaknowledge is knowledge about one's knowledge. In some senses, it's a test of how calibrated an individual may be in knowing what they know and knowing what they don't know.

8. Russo and Shoemaker, Winning Decisions, 80.

9. Amos Tversky and Daniel Kahneman, “Judgment Under Uncertainty: Heuristics & Biases,” Science (1974).

10. Ibid.

11. Example modified from an example provided in Tversky and Kahneman, “Judgment Under Uncertainty.”

12. By bias I mean a predictable and consistent misestimation of either probabilities or values.

13. Tversky and Kahneman, “Judgment Under Uncertainty.”

14. Ibid.

15. Max Bazerman and Don Moore, Judgment in Managerial Decision Making (Hoboken, NJ: John Wiley & Sons, 2008).

16. Tversky and Kahneman, “Judgment Under Uncertainty.”

17. Daniel Kahneman and Amos Tversky, “On the Psychology of Prediction,” Psychological Review 80, no. 4 (July 1973).

18. Bazerman and Moore, Judgment in Managerial Decision Making.

19. Tversky and Kahneman, “Judgment Under Uncertainty.”

20. Ibid.

21. Bazerman and Moore, Judgment in Managerial Decision Making.

22. Ibid.

23. As cited in Bazerman and Moore, Judgment in Managerial Decision Making.

24. Amos Tversky and Daniel Kahneman, “Availability: A Heuristic for Judging Frequency and Probability,” Cognitive Psychology 5, no. 2 (1973): 207–232.

25. Ibid.

26. Tversky and Kahneman, “Judgment Under Uncertainty.”

27. Amos Tversky and Daniel Kahneman, “The Framing of Decisions and the Psychology of Choice,” Science 211, no. 4481 (Jan 1981): 453–458.

28. Daniel Kahneman and Amos Tversky, “Prospect Theory: An Analysis of Decision Under Risk,” Econometrica 47, no. 2 (March 1979): 263–291.

29. Dan Ariely, “The End of Rational Economics,” Harvard Business Review 87, no. 7/8 (July-August 2009): 78–84. As an additional illustration of how free goods create confusing behavior, consider the fact that I witnessed one of my colleagues (a well-paid hedge fund investor) literally dropping everything, walking several blocks, and pretending to be a random person walking by a promotion booth that was handing out free sample boxes of pasta. (Note: The pasta was available for under $2 at any grocery store.)

30. Daniel Kahneman, J. L. Knetsch, and Richard H. Thaler, “Fairness as a Constraint on Profit Seeking: Entitlements and the Market,” American Economic Review 76, no. 4 (1986).

31. Richard H. Thaler, The Winner's Curse: Paradoxes and Anomalies of Economic Life (Princeton, NJ: Princeton University Press, 1992), Chapter3: “The Ultimatum Game.”

32. Gary Belsky and Thomas Gilovich, Why Smart People Make Big Money Mistakes and How to Correct Them: Lessons from the New Science of Behavioral Economics (New York: Fireside, 1999), 31–32.

33. Richard H. Thaler, Quasi-Rational Economics (New York: Russell Sage Foundation, 1994), Part 1: “Mental Accounting and Consumer Choice.”

34. Daniel Kahneman, J. L. Knetsch, and Richard H. Thaler, “Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias,” The Journal of Economic Perspectives 5, no.1 (Winter 1991): 193–206.

35. Peter Ubel, Free Market Madness: Why Human Nature is at Odds with Economics—And Why it Matters (Boston: Harvard Business School Press, 2009).

36. Daniel Kahneman, J. L. Knetsch, and Richard H. Thaler, “Experimental Tests of the Endowment Effect and the Coase Theorem,” Journal of Political Economy 98, no. 6 (December 1990): 1325–1348.

37. So named by Jonathan Baron, Jane Beattie, and John C. Hershey, “Heuristics and Biases in Diagnostic Reasoning: Congruence, Information, and Certainty,” Organizational Behavior and Human Decision Processes, 98, 88–110.

CHAPTER 4

Political Foundations: EVALUATING PROPERTY RIGHTS, PRICE MECHANISMS, AND POLITICAL DISTORTIONS

If you put the government in charge of the Sahara Desert, in five years there'd be a shortage of sand.

—Milton Friedman

When a collection of individuals agrees to form a society, they have many options in determining how to organize themselves. The political philosophy of the group will be manifested in its political-economic systems, and the range of possible solutions is wide. This chapter focuses on two key decisions that relate to a society's vulnerability to boom and bust cycles. First, we evaluate the different philosophies relating to property rights. A society's choice to allow private property to exist and to protect such property with corresponding rights is an essential prerequisite for market-determined prices to be “discovered” via supply and demand dynamics. Further, without private property rights, the idea of booms and busts may be moot, as the state owns everything.[1]

The chapter evaluates the mechanism through which a society chooses to determine the prices of goods, and the roles that those prices play in the allocation of scarce resources. Although significant gradations exist between the extremes, two primary price-determination methodologies are considered: (i) supply and demand–driven price “discovery” processes that take place through the interaction of buyers and sellers; and (ii) central planning–driven price dictation in which the prices of goods and services are set or influenced by government bureaucrats. Again, the political choice of determining a pricing methodology has significant ramifications for the relative fertility of booms and busts. Market-determined prices are inherently more volatile than state-mandated prices; as such, they create the conditions in which economic dislocations have the potential to snowball into extreme price movements. Societies that have state-mandated prices are unlikely to have extreme price volatility; rather, they may suffer from extreme fluctuations in the availability of goods.

Respected property rights and market-determined prices are two essential ingredients for booms and busts to take place. Political processes in societies having these preconditions are likely to exacerbate financial extremes. Specifically, politically determined price floors and price ceilings can confuse price-discovery processes, and tax policies are prone to either inflate or depress the demand (and supply) for certain goods, sometimes quite dramatically. Let us now turn to the issues of property and prices.

Can Anyone Own Anything?

According to the Concise Encyclopedia of Economics, property rights are defined as:

the exclusive authority to determine how a resource is used, whether that resource is owned by the government or by individuals. … Private property rights have two other attributes in addition to determining the use of a resource. One is the exclusive right to services of the resource … [and the other is] the right to delegate, rent, or sell any portion of the rights by exchange or gift at whatever price the owner determines (provided someone is willing to pay that price).[2]

Private property rights thus have three primary characteristics: exclusive rights to determine how the property is used, exclusive rights to the services of such property, and exclusive rights to sell or exchange the property.

The spectrum of possible property rights ranges from complete and total state ownership of all property to complete and total private ownership of all property. Private property rights are a hallmark of capitalism, and the lack of private property rights (i.e. state ownership of all assets) is typified by communism. In fact, Karl Marx succinctly captured the essence of communism in the Communist Manifesto when he wrote “The theory of the Communists may be summed up in the single sentence: Abolition of private property.”[3]

At the root of this objective was a belief that private property rights enabled the accumulation of inequality to compound over time, ultimately leading to disparities of wealth so large as to threaten systemic collapse. Without private property rights, and in a society in which everything was owned by the state, it would be possible to achieve another socialist ideal: a harmonious society in which everyone worked hard to make sure that everyone had what they needed. As noted by Marx and Engels, such an ideal state would be summarized by the slogan, “From each according to his ability, to each according to his needs!”[4]

Although the complete lack of property rights characterizes one extreme of the property rights spectrum, the other extreme is one in which laissez faire capitalism provides for complete private property rights. Property rights are essential for a market mechanism to work. Without property rights, the incentive to drive profits or generate economic returns relies not on economic self-interest, but rather on psychological factors—if it exists at all. If such an economic incentive did not exist, prices would not be determined by market forces—thereby negating the powerful information content they might otherwise contain.

Nationalization also tends to make an asset or market less efficient than it would be under a freely operating private property system. For example, Mexico nationalized its oil industry in 1938,[5] and since then Pemex has chronically underinvested in research and development. This has placed Mexico in the position of having its reserve balances in long-term decline. The disparity is seen along international border oil and gas fields in the Gulf of Mexico. Large, multi-billion-barrel potential reserves were discovered in the Perdido Fold Belt area early in the last decade. On the U.S. side, Shell Oil started production in 2010 and produces about 100,000 barrels of oil and gas per day. Just over the border, Mexico's national oil company just approved a plan to drill in 2017, after overhauling its regulations to allow private partners to bring in expertise Pemex lacks. Although this is not a perfect example, because U.S. law permits transferable leases of offshore resources, not outright ownership, nonetheless it's illustrative of the speed with which a more freely arranged economy operates than the nationalized version.

Lest we think that nationalizations only take place in commodity-rich countries, Table 4.1 highlights a handful of nationalizations that have taken place since the year 2000, excluding those nationalizations that might be better characterized as bailouts in which the company or industry might have gone bust without the bailout.

Table 4.1 Selected Nationalizations since 2000

Country

Year

Target

Bolivia

2006

Natural gas industry

Germany

2008

Federal print office

New Zealand

2001, 2008

Rail networks

United Kingdom

2001

Rail networks

United States

2001

Airport security services

Although nationalization is the most extreme form of a change to property rights once granted, equally problematic approaches might include poorly defined property rights, or property rights that are subject to some limitations. A good example of poorly defined property rights might exist in areas of territorial ambiguity. For instance, the Spratly Islands in Southeast Asia are a collection of islands that were claimed by no fewer than six nations. Vietnam, the Philippines, Brunei, Malaysia, China, and Taiwan all claimed ownership of the territorial waters. In the mid-1990s, when it was believed the islands might be sitting on top of significant oil reserves, gunboats actually exchanged fire. Crestone Energy, a Denver-based energy company, had secured what it believed were legitimate rights to drill for oil from the Chinese government. The Vietnamese government disputed this right, claiming territorial sovereignty over the area. When Vietnam later sent a rig into the area, the Chinese responded with a gunship and a naval blockade of the rig to prevent it from receiving needed supplies. Likewise, fishing rights in the Grand Banks were not clearly delineated, resulting in competitive overfishing by both U.S. and Canadian fishermen that ultimately made fishing in the region commercially unviable.

Property rights with limitations are another case of distortion by government interference. Consider the fate of Unocal, a U.S. oil and gas company that tried to sell itself to CNOOC, one of the Chinese national oil companies. The U.S. government effectively blocked the transaction. Consider also the fate of London-based port operator Peninsular & Oriental Steam Navigation Company (P&O) in the sale[6] of their assets (which included U.S. port operations) to DP World, an investment company controlled by the government of Dubai in the United Arab Emirates. Political forces and public uproar resulted in the Emirates agreeing to divest of the U.S. ports in order to get the transaction completed.

These three property rights “modifiers” (nationalization, ambiguity, and limitation) have a dramatic impact on the likelihood of booms and busts. By mitigating investor desires to participate in markets that lack clearly defined and well-respected property rights, it appears that all modifications to property rights in fact dampen the possibility of booms and busts. Likewise, areas of territorial disputes and property rights ambiguity are likely to deter many investors and limit the risks they are willing to bear. Finally, the ability to exit from investments free and clear of last-minute modification of terms by the government appears a necessary condition for the formation of bubbles. Without this ability, open-ended, believable stories of justified price extremes would meet resistance.

There is also the possibility that financial booms and busts can be enabled via the granting of property rights where they were previously absent. A glance at prices in the privatized housing markets of (former) communist nations (i.e. China, Russia) begins to demonstrate the possibilities.

Venezuelan and Soviet Nationalization: Communism at Work

The argument against market-derived prices is one that fundamentally believes prices are fickle and subject to irrational whims. As such, interpreting information that is supposedly embedded in prices is a fool's game. For this reason, centrally planned economies that generate prices from government decisions do not look to markets or supply–demand dynamics for price generation.

In the former Soviet Union and other socialist economies, prices were set by decree, something like the Federal Reserve sets the price of money in the United States. In some cases, governments attempted to model out what an appropriate market-determined price might be and then dictated that as the price. Stiglitz notes the futility of such an approach:

Even if the government was successful in deciding on an appropriate price, it could do so only after a lengthy bureaucratic process. But economic conditions were changing while the bureaucrats were deliberating, which means the government-announced price was rarely the same as the market price.[7]

By setting prices that were either too high or too low, government interference with market-determined prices resulted in both shortages (when prices were set too low, resulting in excess demand or inadequate supply) and oversupply (when prices were too high, resulting in inadequate demand or excess supply). Not surprisingly, government interference tended to also produce black markets in which illegal transactions were taking place at prices negotiated between sellers and buyers. Despite these problems, the political process of setting prices via central planning was adopted by many countries in the twentieth century.

Let's consider how political decisions to restrict or remove property rights have affected the price mechanism by examining the ongoing nationalization efforts in Venezuela.

Tenaris, the world's leading producer of steel tubes and pipes for the oil and gas industry, had a facility in Venezuela in which it had invested substantial capital. The facility had been performing relatively well economically, and its prospects for future profits were bright. Then, on May 22, 2009, the Venezuelan government informed the management of Tenaris that they would be nationalizing the company's assets and those assets would belong to the state.[8] What impact might nationalization have on the price mechanism for steel assets in Venezuela? By establishing that private property rights were not respected, the government of Venezuela sent a very clear message to the global investment community. Would a reasonable person or company invest in a country where the rights to their investment might not be respected?

Venezuela is one of what is now a handful of nation's exercising Soviet-style nationalism. Under now-deceased president Hugo Chavez, Venezuela nationalized a series of industries in the past decade, including taking majority stakes in major oil and gas operations, forcing ExxonMobil and Conoco out of the country when they refused to acquiesce. Chavez also nationalized agriculture and fertilizer operations of foreign businesses along with glass, steel, gold, cement, telecom businesses.

At first, when Chavez's regime of nationalization began in earnest in 2007, it seemed positive, at least to the masses of Venezuela. Powered by high oil prices that peaked at $160 per barrel, Chavez was able to spread money in the forms of discounted services to many Venezuelans. The country largely weathered embargoes put on it by the United States. Chavez was even able to tweak the American government by offering discounted home heating oil to poor U.S. residents through Citgo, the U.S. arm of the national oil company PDVSA. Even though nationalization of farmland had made agriculture highly unproductive in the country, Venezuela's oil riches allowed it to import the majority of the food it needed.

But as oil and gas prices fell, the ability of PDVSA to subsidize everyday life for its citizens fell with its revenues. This led to a deep economic crisis which left many residents scrambling to secure basic necessities. When they can, tens of thousands stream into Colombia to buy food and other supplies. During a 2017 trip to Bogota, I witnessed Venezuelan businessmen stuffing their suitcases with toilet paper before returning home. Looting is commonplace. A 2016 study[9] found that half of sixth graders attending public schools had gone to bed hungry in the past week. Another study determined that the average Venezuelan lost 19 pounds that year due to the lack of food.

Market forces that could begin to correct the problem – privatizing farming again and allowing fixed prices to fluctuate—haven't been permitted. Instead, in 2017, the government turned to more central planning, including introducing Plan Rabbit, a proposed program of giving cages to poor settlements to raise baby rabbits, breed them, and thereby create a sustainable in-home protein source (since rabbits, well, breed like rabbits). A pilot program found most of the recipients treated the bunnies like pets, naming them, putting bows on them, and even letting them sleep in their beds. Some supposedly shared scarce food with the furry additions to their families.[10]

That this is happening isn't a surprise. While communist, or central command-style economies, can adjust to market forces of supply and demand, they typically have been much slower to do so than free market economies. And because prices are set, they lack the informational value of market-determined prices, which reflect underlying supply and demand dynamics.

The largest and most successful command-style economy, the Soviet Union, collapsed as dictated prices and uses of economic resources weren't agile or accurate enough to reflect the actual economic cost of production. Basically, farmers and producers of other goods and services refused to sell at state-ordered prices because the prices the state fixed were too low to justify production. This lead to a black market economy.[11] The government in essence was trying to control inflation by legislating it out of existence. It didn't work: in just one year (1990), GDP of the Soviet Union fell 8% as the traditional relationships between suppliers and producers collapsed. By the end of 1991, the Soviet Union ceased to exist.

Prices: To Guide or Be Guided?

If property rights are present (and respected), the next logical question for a society might be, “How should we determine the price of an item, and who should be involved in the process?” Although this is a seemingly trivial and innocuous question, it strikes at the heart of different political philosophies ranging from laissez faire capitalism to communism (and everything in between).

Surely it makes sense that an intricate, hand-woven sweater made of cotton that has been spun into yarn, dyed various colors through inks generated by finding and squeezing appropriate fruits and vegetables that have proven to generate such pigments, and finally woven into a wonderfully attractive pattern is worth more than a couple of leaves that have been picked up off the ground and stuck together with the sap of local, readily available trees, right? The intricate sweater has taken a great deal of time to construct, thereby embodying a great deal of labor. Such logic implies the leaf clothing is easier to make, and should therefore have a lower price. The earliest theories of price and value were based on calculations of the amount of labor that went into the good or service. This approach to thinking about prices dates back at least to Adam Smith, who wrote:

The real price of every thing, what every thing costs to the man who wants to acquire it, is the toil and trouble of acquiring it. What every thing is really worth to the man who has acquired it, and who wants to dispose of it or exchange it for something else, is the toil and trouble which it can save to himself, and which it can impose upon other people.[12]

Smith went on to further distinguish between “value in use” and “value in exchange,” noting the seeming paradox between the value of water and the value of a diamond:

Nothing is more useful than water, but it will purchase scarce any thing; scarce any thing can be had in exchange for it. A diamond, on the contrary, has scarce any value in use; but a very great quantity of other goods may frequently be had in exchange for it.[13]

It turns out that supply–demand dynamics do a reasonably good job of explaining this paradox. By focusing on the marginal value (i.e. what would one additional unit be worth?) of each good, it becomes easier to understand why each good is priced the way it is. How much would you consider paying for one additional cup of water in your life each year? Most people would not pay very much because, in most parts of the world, water is plentiful. In commenting on the water–diamond paradox presented in The Wealth of Nations, Nobel prize–winning economist Joseph Stiglitz noted, “water has a low price not because the total value of water is low—it is obviously high, since we could not live without it—but because the marginal value … is not very high.”[14]

There are two fundamentally different methods through which society can generate prices for its goods and services: market mechanisms through which supply-and-demand dynamics determine a price, or central planning in which prices are dictated by government bureaucrats. These two methods generate different perspectives on the role of prices in a society's allocation of scarce resources. The market-oriented approach to price determination is generally utilized in societies that seek to have prices guide investment and consumption decisions. The central planning approach has historically been used by societies in which the government guides prices in a quest to maintain social or economic stability.

The Market-Oriented Approach

Having examined the price-dictation method as implemented in Venezuela and the Soviet Union, let us turn to the market-oriented approach to price determination. In addition to being deemed efficient and effective, an important argument made in favor of market-determined prices is that prices contain tremendous information about the appropriate allocation of scarce property: “Market prices condense, in as objective a form as possible, information on the value of alternative uses of…property.”[15]

Suppose an entire block of central midtown Manhattan suddenly became available for sale today. For simplicity's sake, let's say it's between 53rd and 54th Streets, and between 5th and 6th Avenues. There is no skyscraper on it, no pavement, just some overgrown grass and a rickety old fence. The price at which this block will sell is a reflection of all the alternative uses for the block and is therefore “informationally rich” and useful in guiding the appropriate investment decisions related to the block's use. Should a pig farmer purchase the block and arrange to construct the world's most modern pig-slaughtering facility on it? Perhaps an entrepreneur should buy it and build an underwater basket-weaving training facility on the block.

Although there is nothing wrong with investing to create a pig-slaughtering or underwater basket-weaving facility on this block, we might agree that neither of those uses for the land is the “best” use of the land. Surely a commercial real estate developer will recognize the value they might create through the building of an office tower or residential condominium building and outbid our prospective pig farmer. In fact, it is highly likely that the person or firm that sees the highest value for the land (perhaps a hotelier enters the scene and outbids our residential condo developer) will bid the price up to a point where the land will be used in an economically optimal manner.

If the land sale takes place as an auction, with the opening bid starting at $1.00, it is easy to see how the rising price will eliminate inappropriate investments on the land and effectively select the investment on the land that will maximize its value or use. Ten-year-old Johnnie takes the bus in from New Jersey to attend the auction and believes a lemonade stand would thrive on that block. He bids $9.28, the amount of money in his piggy bank. He is outbid by a young couple from a third-world country thinking that this plot of land might suit them to build a small house for themselves in which they could start a family (with a bid of $25,000). Next comes along an entrepreneurial college student who wants to convert the block into a training center for the homeless. Believing learning is most effective in an environment similar to one's home, he proposes to not build anything (with a bid of $100,000). After him comes a parking lot company that believes a shortage of parking lots would justify high prices to park on this block, and proposes an investment to create a three-story parking garage (it bids $500,000). Our underwater basket-weaving school builder is next, bidding a cool $1 million, beaming with confidence that she will be addressing one of Manhattan's largest unmet needs.

Next come the folks from the pork industry, armed with projections of the sausage revival that is expected to imminently displace chicken breasts on grocery shelves. They limit their bid to $10 million, knowing that the facility will cost a pretty penny to construct in such an inconvenient location. Following our pig farmers are the executives of Cheapo Motel, Inc. They see a future global economic recession driving more and more travelers (business and leisure alike) to seek “clean beds, by the hour if necessary.” Their projections justify a bid of $50 million. The penultimate bidder is a commercial office developer. He thinks he can build a 30-floor tower and fill it with office tenants. After estimating construction and operating costs, he believes he can pay $250 million for the land and still generate a profit. The final bidder is a hotel development company that believes it can build the city's finest six-star hotel and convince Seasons Carlton (one of the world's best hospitality companies) to manage it. At an estimated occupancy rate of 97% and average daily rate assumption of $750 per night, the company justifies a bid of $1 billion. The gavel falls: “Sold.”

As this process demonstrated, the price of the property informed bidders of potential uses. It ensured that a pig-slaughtering facility did not end up in midtown Manhattan, and channeled our underwater basket-weaving school to the suburbs. Ultimately, it was the price of the property that resulted in the allocation of the scarce land to its most “valuable” use. Because market-determined prices are dynamic, it is conceivable that the property may in time find a more valued use.

Suppose for a moment that scientists at the Massachusetts Institute of Technology, in close cooperation with the National Institutes of Health and the Centers for Disease Control and Prevention, determine that natural pork, if cooked within four hours of slaughtering, has meaningful life-extending health qualities by materially reducing cholesterol, lowering blood pressure, improving metabolism, generating muscle mass, and increasing memory and overall brainpower. Upon completion of this research, professors at Johns Hopkins Medical School determine that such pork, when combined with a diet of hummus and red wine, removes the need for any and all pharmaceutical treatments for cholesterol, high blood pressure, diabetes, and a host of other common ailments. Not surprisingly, the price of “local pork” skyrockets from $5 to $1,000 per pound—supported by its qualification for health insurance reimbursements. Most consumers (depending on their medical coverage) are now entitled to three servings of local pork a week for the cost of their medical copayment (typically between $10 and $20).

Given these dynamics, our aforementioned pork-slaughtering executives return to Manhattan and present an offer to our real-estate developer. Perhaps due in part to the lack of local pork offerings in New York City, many businesses had moved to New Jersey to improve employee welfare. As a result, rents in Manhattan are down materially. These new market conditions enable our pig company to buy the block from our real-estate developer, knock down the building, and build a pork-slaughtering facility. Within two years, the facility is recognized as the most profitable pork slaughterhouse in the world by the Global “Local Pork for Health” Slaughterhouse Federation.

The dynamics described here would not have been possible without informationally rich price signals. The higher pork prices provided a valuable incentive for our pork company to invest in Manhattan. The lower rents led our real-estate developer to sell. These decisions were informed and facilitated by the knowledge and information embedded in prices. In a nutshell, this is the basic argument in favor of market-determined prices.

Government Meddling with the Price Mechanism

Representative governments are often beholden to popular sentiment in a manner that makes them highly likely to respond to outcries relating to unfair prices. There are many ways to deal with seemingly unfair prices, the most rapid of which is to simply apply a price ceiling (or floor) on the price of the good or service and require all citizens to adhere to the mandated price. Not unlike the communist approach, such a method is prone to creating excess supply or excess demand and inadequate demand or inadequate supply. If we think of prices as the symptoms and underlying supply and demand fundamentals as the cause, mandating a price is addressing the symptom but not acknowledging its cause.

Price floors and price ceilings Governments tend to utilize price controls and price ceilings because they provide an easy-to-implement method of assuring citizens that they will be able to afford goods and services, or that an important supplier/constituent will be given a fair price for their production. Rather than addressing the issues that may be driving the price to be higher or lower than their constituents might like (i.e. too much production or supply in the case of low prices or too much demand or inadequate supply in the case of high prices), governments find it easier to simply mandate a price via decree. When prices in a society are generally free to move (and interpreted as providing valuable resource allocation information), but certain prices are constrained from moving completely freely, unintended consequences arise with respect to both the supply and demand. In short, as governments interfere with the price mechanism, both producers and consumers react to the artificial price signals. The end result is suboptimal resource allocation and a higher likelihood of booms and busts.

Perhaps the most common example of a price floor that adversely affects price discovery is the policy of mandating minimum wages. Although the labor market is generally one that utilizes supply and demand fundamentals to determine prices (i.e. wages), government intervention constrains the free movement of prices. By setting a price for unskilled labor that is higher than the price justified by supply and demand fundamentals, the policy of minimum wages incentivizes more workers to enter the workforce while simultaneously discouraging companies from hiring as many workers as they may in fact seek at a market price. The result is higher unemployment than might exist without such a policy. This perverse outcome is unfortunately often exacerbated at times of economic hardship when the political expediency of raising the minimum wage generates short-term political gain at the expense of employment.

In terms of price ceilings, the most often-cited example is rent control. Although price ceilings are able to ensure that consumers can afford to rent a home, the unintended consequence of such a policy is a shortage of available rental units. This is the simple result of having more people trying to rent apartments than there are apartment owners willing to rent their units. Owners have no incentive to increase the supply of rental units because they are not compensated for doing so. Rent control is a particularly intractable problem to overcome because the political value in the short run (citizens pay less for housing) is accompanied by a long-term cost (the supply of rental apartments is not increased). Thus, although well intended, rent control actually creates artificial scarcity and magnifies (rather than mitigates) the problem. Might this policy then result in more home buyers than might otherwise be the case as the supply of rental units becomes inadequate? What impact does such a policy have for the likelihood of housing booms and busts?

Tax policies The section of the U.S. Tax Code titled “Election to expense certain depreciable business assets” (Title 26, Subtitle A, Chapter 1, Subchapter B, Part VI, Section 179) effectively paid for a portion of my 2004 purchase of a BMW X5. Why is it that the U.S. government paid for a portion of my vehicle purchase?

As it turns out, Section 179 is commonly known among tax accountants as the “SUV deduction.” Originally intended to help farmers needing to purchase expensive vehicles, the deduction applied to vehicles with a gross weight above 6,000 lbs. Global automobile manufacturers studied these laws and, surprise surprise, my BMW X5 had a gross vehicle weight of 6,005 lbs. I guess the Germans were nervous enough to add a bit of extra weight to accommodate those who might opt not to have a CD player and such. Because I was running my own business at the time, I was able to expense virtually the entire amount of the vehicle against my income for that year. This was equivalent to the federal government paying approximately 40% of my car's cost.

When speaking with the sales representatives at various dealerships during my car search process, I learned of what various salesmen had termed the “mad December dash” in which all sorts of business-owning executives come into the showroom and are willing to pay full price for 6,000lb+ SUVs. Virtually every year, dealerships sell out of these cars during the last week of the year. As you might imagine, such a policy artificially inflates demand for certain cars by effectively subsidizing their purchase. What might these cars cost if such a deduction did not exist? What is the real demand for a $100,000 Cadillac Escalade ESV?

Are Booms and Busts a Capitalist Phenomenon?

Given the fact that asset booms and busts are driven by prices that rise and fall more rapidly than might otherwise be expected, it should come as no surprise that booms and busts appear to be a phenomenon more frequently found in democratic capitalist economies. Alan Greenspan succinctly captured the essence of this distinction in noting the absence of boom and bust sequences in the most socialist of all economic systems in the twentieth century—the Soviet Union: “I do not recall bubbles emerging in the former Soviet Union.”[16]

Does this imply that booms and busts are a solely capitalist event? Is it possible to have booms and busts in socialist economies? Given the apparent necessity of two basic political constructs (property rights and unconstrained market prices) for the enablement of booms and busts, and the fact that fully socialist states prevent these two constructs from existing, it indeed appears that booms and busts are a capitalist phenomenon.

The fact that many capitalist societies have also adopted representative governments such as democracies provides a further complication to our political analysis. Myopic representatives often place short-term political objectives in front of more-prudent longer-term-oriented policies. The result is often a distortion of the basic price mechanism that guides production and consumption decisions. This distortion, in turn, exacerbates the tendency of the system to swing between extreme states.

The case of democratic socialism—and hybrids between the traditional extremes of authoritarian socialism (communism) and democratic capitalism—is more interesting and complicated. As demonstrated by the recent cases of Greece, Italy, Spain, and Portugal, states that fall toward the middle of the spectrum (i.e. democratic socialists) appear equally prone to booms and busts. Might this be because socialist tendencies create entitlements that eventually outstrip the ability of the state and its citizens to generate the resources needed to support those same entitlements?

This example is just one illustration of how government tax policies can dramatically affect supply and demand dynamics. Similar influences can be found with tax breaks for hybrid automobiles, as well as the much-discussed mortgage interest deduction (which we shall discuss in a case study of the housing boom in a later chapter). Hundreds of examples exist (electric vehicles, window replacements, solar energy installations, biofuel facilities, etc.), and unlike property rights interferences, which tend to dampen the likelihood of booms and busts, government interference with the price mechanism tends to amplify the likelihood of booms and busts.

Political Distortions of Property and Price

Political decisions regarding property rights and prices are at the foundation of a market's receptivity to boom and bust cycles. Without private property rights, the incentives to profit are less obvious, thereby tempering—if not eliminating—financial extremes. Just as the removal of property rights (think of Venezuela's nationalizations) dampens investor enthusiasm and decreases, if not eliminates, the likelihood of bubbles forming, the introduction of property rights where they previously did not exist (think of China's housing reform program of the late 1990s) creates a particularly fertile ground on which bubbles may grow. In many ways, private property rights are a measure of a society's willingness to allow successful investors and speculators to keep their winnings.

Modifying or constraining property rights through price ceilings and floors has a ripple-through effect on the market dynamics (i.e. supply and demand) that determine prices. As described previously through the example of minimum wages, price floors tend to keep prices artificially high and therefore incentivize supply and disincentivize demand. Likewise, price ceilings such as rent control tend to keep prices artificially low and incentivize demand and dis-incentivize supply. The result is that politically motivated or mandated price distortions usually exacerbate, rather than mitigate, the problems they seek to address.

It is easy to see how these policies can increase the likelihood of booms and busts occurring. Might consistent underinvestment due to price ceilings result in an eventual supply shortage that is too large to ignore? Could the genuine supply shortage create hoarding mentalities that further exacerbate the problem? How might prices react in such an environment? Likewise, might price floors drive overinvestment that generates excess supply? Might overproduction result in bloated inventory levels that will eventually become too large to ignore? What might happen to prices then?

Just as price ceilings and floors distort supply and demand drivers, so too do taxes have a confounding effect. By effectively subsidizing or penalizing particular consumption and investment behavior, taxes alter underlying demand or supply. One of the most well-known tax policies to do that is the mortgage-interest deduction in the United States. By effectively paying a portion of the interest owed on a mortgage, the U.S. government has lowered the cost of home ownership and increased demand for homes, a dynamic that has the potential to magnify booms (and therefore busts) in the housing market.

The Church (and State) of Gold

Longtime money manager and investing consultant Peter L. Bernstein published The Power of Gold in 2000, in which he traces the course of the human obsession with gold. It starts with Moses coming down from Mount Sinai with the Ten Commandments given to him by God and encountering his followers, who have already abandoned God to worship a calf made of gold. In many ways, today, gold remains the basis of a religion: a comforting sign of seeming certainty in a complex world of fiat currencies, inflation and deflation, forex rates, and quantitative easing. In any book predicting the coming implosion of the Western economic system, the hero, so to speak, is gold, there to pick up the pieces left by the collapse of a seeming house of cards made with paper money.

For thousands of years, gold did indeed play a role in the financial system. As a metal that occurs on every continent on earth but never in sufficient volume to become commonplace, gold was the basis of finance and a motivating force in the exploration of the Western Hemisphere by Europeans. Bolstering gold's case as a store of value is its quality of being a noble metal, meaning it doesn't corrode and is essentially indestructible by virtue of its chemical inertness.

Gold is cumbersome to carry in bulk, so people began to store it with third parties; and paper money came along as a representation of assets (which might be gold) held at a bank or private institution. Governments eventually took control of issuing paper currency, but the concept was the same—you could take your French francs, British pounds, or U.S. dollars, go to a bank, and swap them for a fixed amount of gold.

For various reasons relating to massive deficit spending during wars and the need for governments to print money independent of their precious metal holdings, most governments abandoned the gold standard. Currencies now were truly IOU notes (referred to as fiat currency because they were deemed currency by mandate rather than because they represented some hard asset like gold), backed by the governments issuing them. And while you might argue that a country's ability to tax and the might it could exert through its military meant the IOU was pretty solid, gold's true believers weren't convinced.

To them, this political shift to fiat currencies was a conman's game. The real asset they used to own, through dollars, was gone, replaced by something a government said had value. The politicians were pulling a fast one, and the true believers wouldn't stand for it. They want to be able to see and touch their gold as proof of having wealth. One leading “gold bug,” former Texas congressman Ron Paul, sounds a bit like Calvinist colonial preacher Jonathan Edwards, telling sinners (fiat currency believers) of the horrible fate that awaits them when Judgment Day comes:

Governments, bankers, industrialists, beneficiaries of the military industrial complex, and welfare recipients will never recant in their belief in fiat money … . those ‘heroes of authoritarian government’ will be humbled.[17]

Paul wrote this in 2015, while he was running for president of the United States. Speaking to CNBC in 2013, he said gold “will go to infinity” because the dollar was going to “collapse totally.”[18] Yikes. But if the world economy collapsed totally (something that probably will not happen)? What would you want on hand? Gold can't feed you like a crate of canned food, it can't give you pleasure like a bottle of whiskey, and it can't help you create economic value, like a farming tool might. You're far more likely to be able to trade any of those items for something you need than you are to trade a yellow metal.

A friend of mine in the investment business told me of a very successful trader who lives in a condo in Boston. In his very large apartment, he has a pool table, the four legs of which he has hollowed out so he can store gold coins (in case the economic system collapses). If economic Armageddon arrives and you see someone lumbering across Boston Common with four legs of a pool table weighing him down, you'll at least know why.

A few years ago, Joe Weisenthal, writing for the Business Insider website, came up with a tongue-in-cheek list of why gold is, in fact, a religion. Among his reasons: gold's been popular for thousands of years; people have literally worshipped it; believers expect gold will punish corrupt leaders and profligate governments; gold has different sects (those who buy futures, others who own bullion in storage, and yet others who feel gold should be physically close at hand); and, most important, gold offers a promised land after an apocalypse that will wipe out unbelievers.

Despite the religious-like convictions of true gold believers, the fact that fiat currencies are so easily manipulated by governments does lend credibility to the argument that non-manipulatable currencies should have value. This is an argument that might also resonate with believers of crypto-currencies that are algorithmically limited to a certain supply and cannot be manipulated by leaders to serve their political purposes.

We could argue the appointment of fiat currency skeptic Mick Mulvaney as White House Budget Director by Donald Trump fed into the enthusiasm of Bitcoin evangelists in 2017. Mulvaney has long been a vocal proponent of the gold standard and critic of fiat currencies like the dollar. However, it was widely noted in crypto circles that Mulvaney gave a speech to the John Birch Society, known for its belief that gold and silver are the only legitimate currencies. In this speech, given in 2016, Mulvaney apparently also praised Bitcoin as a currency that is “not manipulatable by any government,” according to Mother Jones magazine, which acquired audio of the speech.[19]

If you saw someone who invested in hard assets, considered hedges against the U.S. dollar, praised Bitcoin, and had the ear of the President and influence over government budgets, might you run out and begin purchasing what some have called “digital gold”?

Tariffs and Trade Wars

A tariff is a type of tax applied to a specific category of goods that are traded between nations. In America, tariffs have been more like a historical novelty than a tool of modern policy, until recently.

Tariffs as we know them in the modern economy grew out of a nation's desire to protect its domestic industry. Colonial European powers used them to protect and support the domestic industry of the home countries. This policy made it more expensive for goods to be imported to the colonies, thereby protecting specific industries. In the case of the British American colonies, for instance, tariffs were levied by London on imports into the colonies of Caribbean sugar and molasses—because they were products of French and Spanish colonies. This helped ensure that British sugar companies had a profitable market for their production.

Later, the United States and other countries tended to use tariffs to protect growing domestic industries from foreign competition. One argument used to justify these tariffs is that they would eventually increase competition, once the domestic industry matured and the tariffs were removed. The result might be more jobs, lower prices, and better products for consumers. The problem with these “infant-industry” tariffs was their execution, as Austrian-American economist Gottfried Haberler described:

Nearly every industrial tariff was first imposed as an infant-industry tariff under the promise that in a few years, when the industry had grown sufficiently to face foreign competition, it would be removed. But, in fact, this moment never arrives. The interested parties are never willing to have the duty removed. Thus temporary infant-industry duties are transformed into permanent duties to preserve the industries they protect.[20]

So why are tariffs harmful? They are another form of political intervention in the markets that inflate prices for some things and deflate them for others. Tariffs are inefficient in that they generally make things more expensive and misdirect resources that might be more productively deployed elsewhere.

Consider why we use specialists for all sorts of domestic projects. If you want a closet built in one of your rooms, it is very reasonable to expect that a carpenter could build a fine-looking functional closet with basic raw materials: a hammer, nails, lumber, sheetrock, and paint. However, unless you're a carpenter, you will have to make a financial trade-off at least indirectly of something of value to you. For instance, rather than studying carpentry, you might add skills to your own career that would in turn boost your earning power. Or perhaps you need to take time off from work, surrendering your income to build your closet. While you may pay a carpenter more in direct dollars to build a closet for you, it is likely cheaper if you include all the direct, indirect, and opportunity costs you would incur if you did the project yourself.

The same is true for countries, and the theory of comparative advantage helps explain how trade produces economic gains. In many ways, some countries have different strengths and capabilities than others, which means it's less expensive overall to have another country sell, for instance, coconuts into the United States than to undergo a large replanting of American farmlands to growing coconuts domestically. The land is likely more valuable being used for other desirable plantings that thrive in the U.S. climate and may sell for more per equivalent unit, such as oranges in Florida or almonds in California. David Ricardo articulated the principle of comparative advantage in 1817: every nation, however inefficient in its overall production structure, can always profitably export some goods to pay for its most desired imports.[21]

Not only do tariffs raise costs and curtail comparative advantage, but they also compound the problem by effectively levying a tax on a country's own citizens. They pay the tariff through the higher prices on the goods and services of the imported item, or they pay a higher price for a domestically produced item that is being protected by tariffs.

But cheapness of goods and services is in the extreme isn't beneficial for an economy, either. There is an inherent, if harder to quantify, value in providing jobs for a country's citizenry, even if that would mean higher costs for some items. This is the very argument that leads to trade wars, where one country's imposition of tariffs results in retaliatory tariffs, which in turn lead to successive rounds of escalation.

Tempting as it is to punish another country to protect domestic jobs, history has shown that tariffs and the trade wars they frequently spawn are on the whole destructive processes in which all parties lose. Consider the trade spat (which at the time of this writing appeared to escalating toward a trade war) between the United States and China, in which America imposed a tariff on steel and aluminum imports from China. The move was welcomed by domestic steelmakers but raised the prospect of shutting down companies that use imported metal by making them uncompetitive against those importing finished goods that competed with domestic production. Regardless of how this specific story plays out, the unintended complications and increased expense of grappling with tariff ramifications are clear.

A significant milestone in recognizing the negative impact of tariffs came about through the United States Tariff Act of 1930, known to everyone as the Smoot-Hawley act (after its sponsors). By imposing the highest tariffs in over 100 years to protect American industries suffering during the economic downturn of 1930, the Act led to a global trade war. The United States imposed taxes ranging from 40% to 100% on some goods. In turn, world trading partners imposed retaliatory tariffs on American goods.

The consensus today among economists is that the Smoot-Hawley tariffs were a significant contributor to the length of Great Depression, because world trade fell by about two-thirds from 1929 to 1933, as Table 4.2 shows. Since then, western economists have largely felt Smoot-Hawley was the capstone to the argument that tariffs do more harm than good.

Table 4.2 Annual World Trade Volumes, 1929–1933[*]

Source: League of Nations' World Economic Survey 1932-33 via The Economist (December 18, 2008).

1929

$5.3 billion

1930

$4.9 billion

1931

$3.3 billion

1932

$2.1 billion

1933

$1.8 billion

[*] Annual trailing volume as of January each year.

As for the argument that tariffs protect jobs, a study[22] by the London School of Economics of 38 years of trade and unemployment data from the 23 members of the Organization for Economic Cooperation and Development didn't find any statistical link between unemployment and imports or exports. More anecdotal evidence shows that over time, highly protectionist countries grow more slowly than more open economies. Argentina, for instance, had a higher per capita income in 1870 than Germany or Japan, but after highly protectionist policies it was well behind both a century later.

Economist David Gould wrote a paper on free trade when he was at the Dallas Federal Reserve Bank in the 1990s that points out that international trade is another way of moving economies to be more efficient. Even if we didn't have international trade or had tariffs so high as to effectively prevent trade, our economy would still face upheavals as new industries replaced old ones.[23]

In a capitalist society, progress entails what Joseph Schumpeter called “creative destruction.” Fundamentally, new job opportunities destroy old job opportunities. Jobs in the automobile and airline industries, for example, destroyed jobs in the railroad industry.

In the next chapter, we turn to biology as a lens through which we might gain some insights for our study of booms and busts. Two biologically inspired constructs are emphasized as specifically helpful: the use of an epidemic lens to understand a boom's relative maturity, and the application of an emergence lens to comprehend the processes through which uninformed individuals might form a consensus.

Notes

1. The author acknowledges that it is possible to argue that socialist systems in which the state owns all assets are subject to terminal busts as occurred in the former Soviet Union. Such terminal busts that destroy entire political economic organizational systems are beyond the scope of discussion in this book.

2. Armen Alchian, “Property Rights,” The Concise Encyclopedia of Economics (2008), Library of Economics and Liberty, http://www.econlib.org/library/Enc1/PropertyRights.html.

3. Karl Marx, “The Communist Manifesto,” as published in Robert C. Tucker, The Marx Engels Reader, 2nd ed. (New York: WW Norton & Company, 1978).

4. Karl Marx, “Critique of the Gotha Program,” as published in Robert C. Tucker, The Marx Engels Reader, 2nd ed. (New York: WW Norton & Company, 1978), 531. Needless to say, such an approach has deleterious effects upon individual incentives to succeed.

5. Office of the Historian, Bureau of Public Affairs, United States Department of State, “Mexican Expropriation of Foreign Oil, 1938,” https://history.state.gov/milestones/1937-1945/mexican-oil.

6. A sale that had been properly precleared through the Committee on Foreign Investments in the United States.

7. Joseph Stiglitz, Economics (New York: WW Norton & Company, 1993).

8. Press release dated May 22, 2009 from Tenaris management.

9. “¿Cuál es la Situación de Alimentación en Estudiantes y Docentes del estado Miranda?” Prodavinci (June 20, 2016), http://historico.prodavinci.com/2016/06/20/actualidad/laminas-cual-es-la-situacion-de-alimentacion-en-estudiantes-y-docentes-del-estado-miranda/print/.

10. “Venezuela's War on Cuteness,” The Economist (September 21, 2017), https://www.economist.com/the-americas/2017/09/21/venezuelas-war-on-cuteness.

11. Andrei Shleifer and Robert W. Vishny, “Reversing the Soviet Economic Collapse,” Brookings (1991), https://www.brookings.edu/wp-content/uploads/1991/06/1991b_bpea_shleifer_vishny.pdf.

12. Adam Smith, An Inquiry into the Nature and Causes of The Wealth of Nations, The Library of Economics and Liberty, https://www.econlib.org/library/Smith/smWN.html.

13. Smith, An Inquiry, Book I, Chapter IV.

14. Stiglitz, Economics.

15. Donald Boudreaux, “Information and Prices,” The Concise Encyclopedia of Economics (2008), Library of Economics and Liberty, https://www.econlib.org/library/Enc/InformationandPrices.html.

16. Alan Greenspan, “We Need a Better Cushion against Risk,” Financial Times supplement on “The Future of Capitalism” (March 27, 2009).

17. Ron Paul, “Gold Is Money” (December 30, 2015), http://www.ronpaullibertyreport.com/archives/gold-is-money-by-ron-paul.

18. “Ron Paul: Gold Could Go to ‘Infinity,’” CNBC (June 18, 2013), https://www.cnbc.com/video/2013/06/18/ron-paul-gold-could-go-to-infinity.html.

19. Pema Levy, “Trump's Budget Director Pick Spoke at a John Birch Society Event,” Mother Jones (December 19, 2016), https://www.motherjones.com/politics/2016/12/trump-mulvaney-john-birch-society.

20. Gottfried von Haberler, The Theory of International Trade (London: William Hodge, 1936), 281, https://www.cato.org/publications/commentary/truth-about-trade-history.

21. David M. Gould, Roy J. Ruffin, and Graeme L. Woodbridge, “The Theory and Practice of Free Trade,” Federal Reserve Bank of Dallas, https://www.dallasfed.org/~/media/documents/research/er/1993/er9304a.pdf.

22. Jaewon Kim, “The Effects of Trade on Unemployment: Evidence from 20 OECD Countries” (April 2011), http://www2.ne.su.se/paper/wp11_19.pdf.

23. David M. Gould, Roy Ruffin, and Graeme Woodbridge, “The Theory and Practice of Free Trade, Economic and Financial Policy Review (1993): 1–16.

CHAPTER 5

Biological Frameworks: EPIDEMIOLOGY AND EMERGENCE

Even completely rational people can participate in herd behavior when they take into account the judgment of others, and even if they know that everyone else is behaving in herdlike manner. The behavior, although individually rational, produces group behavior that is, in a well-defined sense, irrational.

—Robert Shiller

This chapter provides two biological lenses through which to study booms and busts: an epidemic lens and an emergence lens. The epidemic lens, as described next, has use in helping us to determine the relative maturity of a boom and the potential imminence of a bust. The emergence lens provides a powerful explanatory framework through which to understand how groups can be misled into an uninformed consensus.

Scientists and medical professionals alike have been studying the dynamics of epidemics for hundreds of years. The basic framework utilized by these practitioners has been one focusing on infection rates. Many variables complement this focus, but if we recognize human behavior typical of a boom as “feverish,” then the analogy becomes more obvious. Although the “infection rate” (for example, how quickly people believe the world is different) is not very useful by itself, combining it with the rate at which people are either “cured” of their disease or die from it exponentially increases its value to us. The chapter briefly touches on these dynamics before turning to the idea of emergence.

Emergence is the study of seemingly chaotic efforts by large groups of animals that tends to produce extremely robust and adaptive order. Herds and swarms are among the most prominent of such phenomena in the biological arena, but examples exist in urban planning and other domains. This chapter briefly discusses herd and swarm behavior in social insects and animals before evaluating these dynamics in humans. We close the chapter by considering the ramifications of these tendencies for financial markets.

Revealing the Maturity of an Unsustainable Boom

Epidemiology is the study of diseases and their transmission across a population. If we think about markets as being composed of individuals who are either affected or not affected by a particular “disease” (i.e. infatuation with a new thing), the basic terminology of epidemics has a striking pertinence to the study of booms and busts.

Epidemiologists have developed extraordinarily complex and intricate models of disease transmission. The most basic elements of all these models, however, are the infection rate and the removal rate. The infection rate is the rate at which the disease is transmitted from infected individuals to those who are susceptible to infection. The removal rate is the rate at which infected people are removed from the population of transmitters, either because they die or because they have recovered and are now immune to the disease. Although relapses may prove possible, for purposes of our discussion, we assume that those who contract the disease either die or recover into an immune state.

To better understand how these two rates interact, let us consider three primary scenarios: (i) infection rate > removal rate = 0, (ii) infection rate > removal rate > 0, and (iii) removal rate > infection rate > 0.

Our first scenario, in which the infection rate is greater than the removal rate and the removal rate is 0%, produces an epidemic that eventually infects 100% of the population. The pace at which the disease spreads will depend on the infection rate. Graphically, any scenario that involves a removal rate of 0% will follow a logistic curve, as in Figure 5.1. As can be seen in the graph, the initial percentage of the population that is infected rises slowly at first. Because the removal rate is set to 0%, the population is infected at the infection rate. As summarized by Robert Shiller in Irrational Exuberance:

Figure 5.1 Infection Rate > Removal Rate = 0%

Although the rate is nearly constant at first, the absolute number of people recorded as contracting the disease rises faster and faster: as more and more people become contagious, more and more people become infected … but the rate of increase starts to decline as the pool of yet-to-be-infected susceptible individuals begins to be depleted.[1]

Eventually, 100% of the population is infected, as seen by the flat line at upper right in the graph.

The second scenario is one in which the infection rate is greater than the removal rate, but the removal rate is greater than zero. In this case, the cumulative distribution of the infected population will resemble a bell curve. The graphical depiction of the infected population will rise from zero slowly and then accelerate before peaking and returning to zero. Although 100% of the population might still get infected in reality due to random factors, it is highly likely the peak will occur before 100% of the population is affected. Figure 5.2 summarizes this scenario.

Figure 5.2 Infection Rate > Removal Rate > 0%

The third scenario in which the removal rate is greater than the infection rate which is greater than 0% is particularly uninteresting as it provides for no epidemic. People are cured more quickly than the disease is spread, resulting in no cumulative infection of the population. The best analogy for this situation is a noncontagious disease.

Although the formal academic application of epidemic models to financial markets has not gained significant traction, there has been some research conducted on word-of-mouth dissemination of ideas. Ideas, however, are not nearly as consistent as biological diseases and are subject to transmission errors via what Shiller calls a “mutation rate.”[2] Shiller goes on to note that the transmission of ideas is similar to the children's game of telephone in which an original idea is so distorted via transmission as to be laughable after several transmissions. Further, alternative newsworthy items (such as a murder, car crash, or death of a major celebrity) may in fact affect the infection rate in a manner that deflects attention from the idea being transmitted. Thus, the relative prominence of an idea and the attention it therefore receives will affect its transmission rate.

Despite these concerns, the overarching objective of presenting this lens is to provide a vocabulary for thinking about the spread of ideas through a population during speculative bubbles. The framework of infection, removal, and mutation rates provides a useful conceptual method of understanding. Although Shiller is clear in describing the limitations of applying an epidemic model to idea diffusion through a population, he highlights the usefulness of the framework for thinking about speculative manias, emphasizing the need for a transmittable story:

Word of mouth may function to amplify public reaction to news events or to media accounts of such events. It is still necessary to consider the infection rate relative to the removal rate in order to understand the public impact of any new idea or concept, since most people's awareness of any of these is still socially mediated. Thus the likelihood of any event affecting market prices is enhanced if there is a good, vivid, tellable story about the event … . Word-of-mouth communications, either positive or negative, are an essential part of the propagation of speculative bubbles.[3]

Ultimately, the usefulness of an epidemic lens is its ability to help gauge the approximate maturity of an unsustainable boom. Just as a bubble that is relatively more progressed (i.e. closer to the point of bursting) will have more of its population infected, so too will an earlier-stage bubble have a large “yet-to-be-infected” and susceptible population. Because it is very difficult to gauge with any precision the percentage of the population that is infectable or that has been infected, red flags or indicators that reveal approximations are useful. For example, a growth in amateur or beginner investors into a particular asset class is very telling. After all, we must assume that expert investors have already been infected, and if amateur investors are now infected, who is left to infect? Thus, by gauging the prevalence of newcomers to an asset party, we can get an approximate sense of the bust's imminence.

How Micro Simplicity Drives Macro Complexity

In many biological examples, group actions that appear ordered and deliberate emanate from uncoordinated individuals. How is it that these collections of social animals appear to operate in a seamless manner typical of a single organism? The study of complex biological systems has been the focus of significant research and has spawned a niche industry of “emergence” scholars. Despite being a relatively new area of rigorous research, early studies on the topic were taking place in the 1970s, albeit without the label of emergence. One of the first studies on this topic involved the behavior of Atlantic pollock.[4] University of Miami biologist Brian Partridge took it upon himself to gather schools of 20–30 fish (each about 3 feet long and weighing 40–50 pounds) and have them swim in a circular tank (33 feet in diameter) while he observed them from above, while spinning himself in a manner that kept him geospatially above the moving fish. Each fish was labeled in a manner that allowed him to track individual behavior.

The movement of each individual fish was then analyzed by reviewing more than 10,000 frames of film and Partridge's real-time observations. After completing such painstaking research, Partridge and his research team concluded that individual pollock followed two simple rules to move as if they were a single unit: it was as if they were being told to “swim behind the fish directly in front of you” and “swim at a speed that keeps pace with the fish next to you.” As simplistic as these two rules seem, they appear adequate to explain how the school manages to react when threatened by a predator or seeking to avoid an obstacle in the water.

During the summer of 2010, I decided (don't ask me why, because I'm not sure I know the answer!) to compete in an international distance triathlon. In an effort to provide a “unique experience” to the athletes, the race director decided to send hundreds of swimmers into the ocean at once. Over the course of the roughly 1-mile swim, I was often stuck in a group of swimmers. If I sped up, I found I ran into other athletes. If I slowed down, others ran into me. When I tried to move to the left, I hit another swimmer. The same thing occurred when I tried to move right. Eventually, I decided to settle into following the rules that Partridge and his team found drove the fish in a school … and I was shocked by the overwhelming sense of peace that overcame my efforts when I did. Not surprisingly, several observing friends noted that “it looked like a school of fish were swimming together… . The whole group of you managed to turn and zigzag for no apparent reason, but almost as if someone were directing you all to do so.” Knowing full well that none of us were coordinating anything, I was impressed by the sense of group order that came out of what was utter chaos at the individual level.

As it turns out, swarms are examples of emergent complexity that originates from the following of simple rules on the part of its members. No group consciousness that forces the subservience of individual action is required. As an example of how simple rules exercised by individuals might create group behavior that appears complex to external observers, consider the popular human wave at a ballgame. As noted in Dr. Len Fisher's 2009 book The Perfect Swarm: The Science of Complexity in Everyday Life, “The wave might look to a visiting Martian like a complicated exercise in logistics, but its dynamic pattern emerges from a simple rule: Stand up and put your hands in the air (and put them down again) as soon as you see your neighbor doing it.”[5]

To better understand how such emergent order occurs, we turn to the behavior of several social insects[6] —locusts, bees, and ants—that have demonstrated the ability to generate coherent, organized, methodical group behavior, despite the seeming improbability of doing so with billions of individual members. After discussing how group complexity emerges from individual simplicity in insects, we consider the manifestation of this phenomenon in human behavior. Let's begin by understanding locust behavior.

Locusts

Of the more than 12,000 known species of grasshoppers, fewer than 20 are classified as locusts.[7] Nevertheless, locust plagues—written about since biblical times—continue to affect more than 10% of the world's population.[8] For this reason (as well as scientific curiosity), scientists have been very interested in understanding their behavior.

Locusts are different from other grasshoppers in one major and very important way: their behavior changes radically when they are placed into crowded situations. Grasshoppers tend to disperse if placed in close proximity to each other, but locusts tend to synchronize their movements. The conversion from chaotic crowd to orchestrated swarm occurs as the locusts find themselves in dense quarters. Research has shown that young locusts will move chaotically until the density of their crowd approaches seven locusts per square foot, at which point they begin marching in sync with each other.[9]

As it turns out, most marching is a quest for food, and given the extraordinary volumes of food—daily intake equivalent to their body weight—that locusts consume,[10] cannibalism is not uncommon.[11] This desire not to be eaten provides the motivation to keep moving. Although this might have seemed obvious to our pollock-watching biologist, it is a useful third rule to explicitly articulate: “Avoid hitting the member directly in front of you or being hit by the member behind you.” Given this avoidance desire, why don't the locusts simply disperse? The best way to avoid being eaten is to avoid your fellow hungry locusts, right?

As noted by Len Fisher,

Normally shy and solitary, the close proximity of other locusts … stimulates them to produce the neuro-modulator serotonin, which not only makes them gregarious, but also stimulates other nearby locusts to generate serotonin as well. The ensuing chain reaction soon has all the locusts in the vicinity seeking each other's company.[12]

Literal party animals! A physiological response then creates more mobile locusts that begin moving in swarms, initially on the ground and then in the air. Just as the serotonin, which is released in escalating amounts as the serotonin in close-by locusts rises,[13] drives the desire to be with each other, so too does such intense partying make the locusts hungry—thereby assuring some healthy distance between each of them. As the party progresses, it gathers more members, until dense swarms of around one hundred billion (100,000,000,000!) locusts cover areas of up to 500 square miles.[14]

Although locusts are helpful in demonstrating how group behavior emerges from seeming chaos through the individual member's application of three simple rules (avoidance, alignment, and attraction), they fail to show us how a swarm makes decisions and develops a group logic that is different from an individual's logic. To see how decision-making in swarms takes place, let us now turn to bees.

Bees

Bees are social insects that tend to follow the three simple swarm rules as they travel in groups. Where they differ, however, is in the group's ability to head directly for a particular target (for a new hive, or a food source) identified by scout bees. How is it that a group of uninformed bees in a swarm are able to efficiently find their way to a target? Although much has been written about the famous “waggle dance”[15] that scout bees conduct in their hives to communicate the direction and distance to a target,[16] recent evidence suggests this communication is not sufficient to explain the swarm's behavior. Fisher notes:

The dance is performed in a hive that is almost as dark as some discos, so only those bees nearby (about 5 percent of the total) see the dance. The majority doesn't see it, so most bees begin flying in complete ignorance. Those that have seen the dance aren't even out in front, showing the others the way. They are in the middle of the swarm, flying with the rest.[17]

Given the lack of obvious leadership in the swarm, efficient direction of the group toward a target seems a highly unlikely outcome. To better understand what might be going on, scientists used cameras to capture individual bee movement behavior to see if they might identify some possible explanation for the group's flight pattern.[18] By photographing the bees from below and leaving the camera's aperture open for a short time, researchers were able to produce a “map” of a bee swarm in motion in which each bee's movement was a short line. Most of the lines were short and curved, but a handful of the tracks were a bit longer (indicating greater relative speed) and pointed straight at the target. Those speedy bees heading straight for the target were labeled “streakers” by the scientists and provide the answer to our question.

It turns out that if a group is following the three simple rules of swarm behavior (avoidance, alignment, and attraction), these informed bees are able to take an unsuspecting group of ignorant bees rapidly and efficiently to their target. By moving a bit faster than the group, the informed bees exert a silent leadership that the uninformed bees follow.[19]

Fisher eloquently summarizes this finding:

In other words, it needs only a few anonymous individuals who have a definite goal in mind, and definite knowledge of how to reach it, for the rest of the group to follow them to that goal, unaware that they are following. The only requirements are that the other individuals have a conscious or unconscious desire to stay with the group and that they do not have conflicting goals.[20]

Ants

This methodology of the few informed animals leading a group directly toward a target is not the only form of emergent swarm intelligence that has been observed in social insects. Ants have proven to be equally effective at finding direct routes to food sources and other targets, yet their methodology is entirely different from that pursued by the bees.

Research conducted on a colony of Argentine ants at the University of Brussels specifically sought to understand why it was that ants were able to efficiently and directly travel to their targets.[21] Scientists working in the Department of Behavioral Ecology created a forked path between the ant colony and a source of food. One path was approximately twice as long as the other path. They found that although the initial ants chose randomly, within a few minutes the whole colony was utilizing the shorter route. How and why did that happen?

The answer is actually quite simple, and once understood, the efficiency of ants makes a great deal of sense. Ants emit a chemical substance called a pheromone, which attracts other ants. More pheromone attracts more ants more intensely, and pheromone dissipates with time. Thus, the ant that took the short path ended up getting back more quickly and producing an option for other ants that has at least twice[22] the pheromone of the longer path. Because of the greater pheromone on that trail, the next ant is highly likely to choose the shorter path. Even ants that take the longer trail to the food will be more likely, again due to the higher pheromone levels, to return via the shorter route and to further increase the pheromone levels on that route. Within minutes, the pheromone levels on the shorter trail overwhelm those on the longer trail to the point that the entire colony begins using the shorter path to the food. Here again, we find that individual adherence to basic rules lies at the root of this collective behavior: “The colony's efficient behavior emerges from the collective activity of individuals following two very basic rules: lay pheromone and follow the trails of others.”[23]

The other interesting finding from the study of ants is that emergent behavior with silent leadership (even if random or unintended) was able to efficiently guide the swarm of ants to a target. Thus, the idea of the queen bee or queen ant has been refuted by research in favor of group-driven behavior, with significant implications for group decision-making in animal groups.[24]

Emergent Behavior in Human Swarms

We now turn to the study of a particularly social and group-oriented animal, the human being. Historically, animal decision-making processes have been studied from a “simple rules” perspective, whereas human decision-making processes have been focused on complex utility functions. Unfortunately, the two disciplines have not cross-pollinated their research efforts until very recently. The next section provides highlights emerging from this multidisciplinary research.

Swarm Processes and Group Dynamics

As we transition from social insects to humans, swarm logic is more robust and transferable than most might believe. Although the case studies later in the book will illustrate how swarm logic among investors might exacerbate booms and busts, this section of the chapter will briefly touch on the research that has been conducted on humans as social animals and in which group decision-making processes are the focus. Unlike Chapter 3, which focused on how individuals make decisions, this section will focus on how groups make decisions and how individuals within them affect and are affected by group dynamics.

We begin by reviewing some recent research conducted by biologists, zoologists, and ecologists, and mostly published in journals not typically read by economists, financiers, or social scientists. Several of these studies were conducted to determine if humans act similarly to social insects in their approach to making group decisions. The Philosophical Transactions of the Royal Society of London dedicated an entire issue to the topic of “Group Decision Making in Humans and Animals.”[25] The findings, though not entirely shocking, have profound implications for our study of booms and busts.

One particularly interesting piece of research[26] published as part of this collection of papers was about an experiment conducted on groups of college volunteers. Several students were told to walk anywhere in the room, as long as they stayed within one arm's length of another student, which effectively created both the avoidance and the attraction criteria needed for swarm conditions. Communication was prohibited, but around the room were several targets in the form of uniformly placed letters. Prior to the experiments, one or two students in the group were given secret instructions to head toward one of the targets. By the time the experiment was stopped, most groups had ended up at the target letters given to the informed students. The unsuspecting students had been “led” there by a small minority of focused and informed leaders.

Although it is interesting to learn that uninformed human groups can be led by covert but informed leaders, the world is often filled with multiple leaders frequently targeting different objectives. Because of this fact, researchers began investigating the behavior of uninformed groups in situations including multiple leaders with conflicting targets. An article in the scientific journal Animal Behavior summarizes the research (which is based on a similar research design to the one just mentioned, but utilizing two informed students who were given differing objectives): “When conflicting directional information was given to different group members, the time taken to reach the target was not significantly increased; suggesting that consensus decision making in conflict situations is possible, and highly efficient.”[27]

Basically, the same experiments were conducted with two individuals given differing objectives. The groups were no slower in reaching the targets and quite rapidly made decisions about the appropriate course to be pursued. The implications of this finding on the study of booms and busts are enormous. If unsuspecting group members (think ordinary investors) can be led in any direction by a relatively small number of confident (regardless of whether such confidence is merited) members, we can imagine how such confidence might feed upon itself to generate a boom-like scenario.

Famous research conducted by Stanley Milgram[28] in the late 1960s demonstrated the power of silent leadership in groups.[29] Professor Milgram arranged to have people on the street stop and stare up at a window on the sixth floor of a tall building. Len Fisher eloquently summarizes the findings of the research: “With just one person staring up, 40 percent of passers-by stopped to stare with them. With two people, the proportion rose to 60 percent, and with five it was up to 90 percent.”[30]

If nothing else, the research confirms the ability of a few people to guide the behavior of a much larger mass. Note that this experiment actually demonstrated the ability of a few to mislead the many, an outcome that has significant pertinence to the study of booms and busts.

Intentional misleading may not even be a motivation of the group's pioneers, but the idea that initial decisions matter is significant. Consider the following informal experiment I ran after having read a great deal about bee logic and the ability of silent leaders to generate consensus. After landing at the Las Vegas airport (an airport with which I have a bit too much familiarity), I was the first person to exit the plane. I decided to get off the plane and head right (the opposite direction from the main terminal), walking briskly and with the conviction of a knowledgeable passenger. Despite signs pointing in the opposite direction, I was amused to see that the next 10–15 passengers who got off the plane immediately turned right and followed the “crowd.” I repeated this experiment on eight other flights over the next six months in various airports, all with similar results. It definitely seemed that bee logic applied to humans! The key lesson of this ad hoc research was that humans have a tendency to conform to the behavior of the seemingly knowledgeable group member.

The next section demonstrates how initial decisions made without meaningful reason might be interpreted by later deciders to have been based on careful analysis and contemplation. By placing greater weight on early decisions made during a chain of decisions, information cascades may develop and create outcomes that seem to defy explanation.

Information Cascades and Herd Behavior

Imagine, for example, that two equally good restaurants open on the same street adjacent to each other.[31] They are very similar in every way, including cuisine, price, and ambience. Suppose now a young couple comes by and must decide on which restaurant to choose. Given that both restaurants are empty at this point, they have very limited information with which to make a choice. After perusing both menus, they flip a coin. Heads they go to restaurant A, tails they go to Restaurant B. They flip the coin and end up in Restaurant A. The second couple that comes by now has the same information as the first, as well as the fact that it appears the first couple chose Restaurant A. Unable to decide based on the restaurants’ menus and atmospheres, they opt for Restaurant A, assuming the first couple must have chosen for a good reason. Likewise, a family of five comes by, and seeing Restaurant A fuller than Restaurant B, they choose Restaurant A. This process might continue until Restaurant A is full, while Restaurant B remains empty.

As stated at the outset, the two restaurants are virtually identical, so how is it that one received all the business, and the other remained empty the whole night? Given that customers made their decisions based on the decisions of those prior to them, an information cascade took place through which information—relevant or not, but embedded in the choices made by others—influenced the actions of those who followed.

Career Risk, Herd Behavior, and Client-Driven Bubbles

John Maynard Keynes noted in Chapter 12 of his General Theory of Employment, Interest and Money that “worldly wisdom teaches us that it is better for reputation to fail conventionally than to succeed unconventionally.” Boston-based money manager Jeremy Grantham of GMO has built on this logic to replace the word “reputation” with “your career.” Describing the phenomenon as “career risk,” Grantham notes that herd behavior and consensus investing are the norm not because professional investors do not recognize financial extremes, but rather because the risks to their careers for unconventional decisions are asymmetric.[32] Failing with the crowd is accepted, and succeeding with a crowd is expected, but failing on your own is likely to lead to termination. Succeeding on your own is likely to allow you to keep your job, perhaps with a promotion. In short, the likelihood of losing your job is small while sticking with the crowd and significant when deviating from the crowd. Why take such unnecessary career risks?

Might such behavior create investor herds that fuel bubbles? What are the costs of being a contrarian investor? Consider the fate of Grantham's firm GMO during the bull market of the late 1990s. While many crowd-followers accumulated assets, GMO stuck to its belief that markets were overvalued. The firm invested assets accordingly. Although eventually proven right, the firm watched assets in its International Intrinsic Value strategy fall by more than 75% (from ~$2.8bn to ~$650mm) between 1996 and 2001 as clients fired GMO.[33]

Andrew Smithers, author of Valuing Wall Street: Protecting Wealth in Turbulent Times, eloquently summarized the dilemma facing fund managers in a short piece he wrote on March 13, 2000, near the absolute peak of the technology bubble, in which he highlights the role of clients:

Most fund managers are aware the market has gone bananas. What they do not and cannot know is when the madness will end. If they are going to stand out for sanity, they must have stalwart clients who will back their judgment even if the result is poor performance over a number of years. Unfortunately, such clients are rare. It is, therefore, more reasonable to blame the clients than the fund managers.[34]

It is believed that such information cascades serve as the basis of herd behavior and have deep evolutionary value. If the leading buffalo in a herd suddenly stops and moves right, it may be because it has seen a lion. If 50 buffaloes do that, it would likely be unwise for the 51st buffalo to dismiss this data. When it comes to the rationality of markets, however, such herd behavior can be quite distortive of market prices.

All Aboard the Bitcoin Bandwagon

John McAfee, of McAfee computer security software, predicted on Twitter in November 2017 that Bitcoin would reach $1 million by 2020. “It's not going to disappear. It can't possibly go to zero. It can only grow as the user base grows,” he later said in an interview in May 2018[35]. McAfee endorsed the apparent consensus among players in the market that cryptocurrencies were the “new thing,” offering the promise of freedom from authoritarian manipulation of monetary instruments.

PayPal founder and investor Peter Thiel noted the promise of Bitcoin by highlighting his own failure: “PayPal had these goals of starting a new currency. We failed at that, and we just created a new payment system. I think Bitcoin has succeeded on the level of new currency.”[36] Internet stock analyst turned publisher Henry Blodget suggested Bitcoins could be worth $1 million per coin,[37] and CNBC host Brian Kelly described Bitcoin as “not just digital gold … it is a once-in-a-generation investment opportunity, similar to the internet, growing just as fast, if not faster … it's the internet of money.”[38] (It's worth noting Kelly also owns a crypto-focused investment firm.)

In tandem with the bubble effect of “it's different this time” is a tendency to frequently attack and belittle nonbelievers. Warren Buffett has been strongly critical of Bitcoin, saying it doesn't create any thing and “is probably rat poison squared.”[39] Unsurprisingly, that spurred claims that Buffett just doesn't “get it”; “years from now, when the dust settles, Warren Buffett's miss on Bitcoin will be the biggest miss of his career and will make his misses on Amazon and Google look forgivable,” said Darren Marble, CEO of CrowdfundX, a crypto-focused financial marketer.[40]

In most bubbles, the clarity of the change at hand is pushed as obvious and so fantastic as to be undeniable. McAfee even went so far as to say blockchain (the technology enabling Bitcoin) is “the most powerful technology that the world has seen, I believe, since the invention of agriculture” in a 2017 interview on RT television, where he predicted the U.S. dollar will massively devalue even as Bitcoin rises to $500,000 in value.[41] How could you possibly sit this out? It's bigger than agriculture!

The drumbeat that others are getting rich while you aren't and that that crypto values can't go down creates the sense that you have to get in on the action now or miss out. This led to a boom for trading platforms dedicated to cryptocurrencies. Fortune Magazine reported in 2017 that one of the more popular exchanges, Coinbase, had signed up its 12 millionth customer, more than the number of accounts at Charles Schwab, a 46-year old brokerage.[42] And by early 2018, the Washington Post reported that Coinbase had 20 million customer accounts, almost as many as Fidelity Investments and about as many as Vanguard.[43] Recode reported that Coinbase revenues topped $1 billion in 2017.[44]

The Blind Leading the Blind

The biological lenses presented in this chapter have broad applicability to the study of booms and busts. Although the epidemic framework is a useful tool for evaluating the relative maturity of a boom and the corresponding imminence of a bust, it is only valuable in providing an approximate sense of timing. By no means can it generate the precision needed by active risk managers. It yields virtually no insight on a day-to-day basis and is unlikely to prove useful in timing the bursting of a bubble with precision. Nevertheless, thinking about financial euphoria as a disease that has the potential to spread through an entire population proves quite useful in gauging the relative maturity of a boom. The most telling signs of a mature boom that is rapidly approaching the bust phase are a rapid growth in the number and type of participants, as well as the increasingly prevalent participation of unsophisticated or amateur investors.

The implications of the emergence phenomenon for our study of booms and busts are quite dramatic. The applicability of information cascades and the restaurant example discussed earlier are easily understood: seemingly irrelevant decisions take on greater meaning than originally anticipated and have the potential to snowball into herdlike behavior of uninformed individuals. If everybody else is making money investing in housing, why shouldn't I? Clearly they've done the analysis and everyone can't be wrong, can they? Yet everyone relying on the fact that everyone else has “done their homework” can create a dynamic in which random decisions made earlier in the chain acquire unwarranted and unintended significance.

The jump to silent leadership from information cascades is not a particularly large leap. The connection occurs via the (seemingly) informed individuals, and just as actually informed individuals like our streaker bees can accurately lead a group of uninformed individuals, so too can silent leadership by seemingly informed (but actually uninformed) individuals lead groups astray. Recall my ad hoc experiment at the Las Vegas airport. Not only did my seemingly informed status (likely conveyed by the definitiveness of my direction and focus of my efforts) lead others astray, but this silent leadership snowballed in a small cascade. Like ant pheromone, each additional person who followed me provided “guidance” to exiting passengers to follow the crowd. Surely not everyone would be walking in the wrong direction, would they?

The fact that many uninformed individuals can be so easily misled by other uninformed (but acting as if informed) individuals is a powerful finding. Suddenly, irrational group behavior is more understandable. If random events (such as the selection of a restaurant) have the potential to snowball into information cascades that create (without reason) completely lopsided outcomes, the implications become quite clear and important: Efficiency and stability can easily be replaced by positive feedback dynamics that drive instability and tipping away (rather than toward) an equilibrium point.

This chapter concludes Part I of the book and the presentation of the five lenses that we will use in our case studies that begin in Part II. Let us now turn to Tulipomania and the first demonstration of the lenses in action.

Notes

1. Robert Shiller, Irrational Exuberance (New York: Broadway Books, 2000).

2. Ibid.

3. Ibid. Emphasis in original.

4. Brian Partridge, “Internal Dynamics and the Interrelations of Fish in Schools,” Journal of Physiology 144 (1981): 313–325.

5. Len Fisher, The Perfect Swarm: The Science of Complexity in Everyday Life (New York: Basic Books, 2009).

6. Social insects are those species that, when considered as a group, possess the following three characteristics: (i) flexibility to changing circumstances, (ii) robustness to individual failure, and (iii) self-organization free from central control or local supervision.

7. Fisher, The Perfect Swarm.

8. Stephen Simpson and Gregory Sword, “Locusts,” Current Biology 18 (2008): 364–366.

9. J. Buhl et al., “From Disorder to Order in Marching Locusts,” Science 312 (2006): 1402–1406.

10. For more information, see Simpson and Sword, “Locusts.”

11. Sepidah Bezazi et al., “Collective Motion and Cannibalism in Locust Migratory Bands,” Current Biology 18 (2008): 735–739.

12. Fisher, The Perfect Swarm.

13. Michael Anstey et al., “Serotonin Mediates Behavioral Gregarization Underlying Swarm Formation in Desert Locusts,” Science 323 (2009): 627–630.

14. Simpson and Sword, “Locusts.”

15. A waggle dance is one in which scout bees shake their abdomen and waggle their bodies in a figure-eight pattern. The direction of the dance points toward a future hive destination or a source of food. The speed of the waggles provides an indication of distance to the destination.

16. Although this was first studied by Karl von Frisch, who was awarded a Nobel Prize in Medicine for this work, more recent evidence comes from the work of John Riley et al., “The Flight Paths of Honeybees Recruited by the Waggle Dance,” Nature 435 (2005): 205–207.

17. Fisher, The Perfect Swarm.

18. M. Beekman et al., “How Does an Informed Minority of Scouts Guide a Honeybee Swarm as It Flies to Its New Home?” Animal Behavior 71 (2006): 161–171.

19. D. Couzin et al., “Effective Leadership and Decision Making in Animal Groups on the Move,” Nature 455 (2005): 513–516.

20. Fisher, The Perfect Swarm.

21. S. Goss et al., “Self-Organized Shortcuts in the Argentine Ant,” Naturwissenschaften 76 (1989): 579–581.

22. I say “at least twice” because of the way dissipation and evaporation happens over time. If the evaporation removes 50% of the pheromone by the time the ant makes a round trip, then the shorter trail will have three times the pheromone as the longer path.

23. Eric Bonabeau and Christopher Meyer, “Swarm Intelligence: A Whole New Way to Think About Business,” Harvard Business Review (May 2001): 107–114.

24. Steven Johnson, Emergence: The Connected Lives of Ants, Brains, Cities, and Software (New York: Scribner, 2001).

25. For a good overview of the research presented in the issue, please see Larissa Conradt and Christian Lists's “Group Decisions in Humans and Animals: A Survey,” Philosophical Transactions of the Royal Society – Biological Sciences 364 (2009): 719–742.

26. John R. G. Dyer et al., “Leadership, Consensus Decision Making and Collective Behavior in Humans,” Philosophical Transactions of the Royal Society – Biological Sciences 364 (2009): 781–789.

27. Dyer et al., “Consensus Decision Making in Human Crowds,” Animal Behavior 75 (2008): 461–470.

28. Stanley Milgram is most famous for research on individual adherence to authority. His famous experiments involved the fictional administration of pain and tested the participants’ abilities to defy orders from an authority when their concerns against doing so arose. The experiments demonstrated a surprising willingness in individuals to follow orders, even when they had tremendous reservations about doing so.

29. Stanley Milgram et al., “Note on the Drawing Power of Crowds of Different Sizes,” Journal of Personal and Social Psychology 13 (1969): 79–82.

30. Fisher, The Perfect Swarm.

31. The example is a modified version of a similar story presented in Shiller, Irrational Exuberance.

32. “11th Annual Lynford Lecture: Grantham reveals financial meltdown causes, future investment focus” NYU Tandon School of Engineering (December 11, 2008), https://engineering.nyu.edu/news/11th-annual-lynford-lecture-grantham-reveals-financial-meltdown-causes-future-investment-focus.

33. David Swensen, Pioneering Portfolio Management: An Unconventional Approach to Institutional Investment, revised and updated ed. (New York: The Free Press, 2009).

34. Andrew Smithers, “Lemming Like Fund Managers and Dotty Markets,” The Evening Standard (London, March 13, 2000).

35. Valuetainment, “John McAfee Changes Bitcoin Prediction” (May 7, 2018), https://www.youtube.com/watch?v=WBgFGwJA1D0.

36. Evander Smart, “PayPal Founder Peter Thiel Comments on State of Bitcoin,” CCN (February 3, 2015), https://www.ccn.com/paypal-founder-peter-thiel-comments-state-bitcoin/.

37. Henry Blodget, “Bitcoin Could Go to $1 Million,” Business Insider (November 8, 2013), https://www.businessinsider.com/bitcoin-price-2013-11.

38. Molly Jane Zuckerman, “CNBC Fast Money's Brian Kelly: Bitcoin Is Like the ‘Internet in the 1980s,’” Cointelegraph (April 15, 2018), https://cointelegraph.com/news/cnbc-fast-moneys-brian-kelly-bitcoin-is-like-the-internet-in-the-1980s.

39. Paul R. La Monica, “Warren Buffett Says Bitcoin Is ‘Rat Poison,’” CNN (May 8, 2018), http://money.cnn.com/2018/05/07/investing/warren-buffett-bitcoin/index.html.

40. Panos Mourdoukoutas, “Warren Buffett Is Wrong About Bitcoin,” Forbes (May 7, 2018), https://www.forbes.com/sites/panosmourdoukoutas/2018/05/07/warren-buffett-is-wrong-about-bitcoin/#5f2b6b07379c.

41. “John McAfee: Bitcoin Is Not a Bubble, It Will Continue to Grow” (August 19, 2017), https://youtu.be/LzGmLDDE2Ss.

42. Robert Hackett and Jen Wieczner, “How High Can Bitcoin's Price Go in 2018?” Fortune (December 21, 2017), http://fortune.com/2017/12/21/bitcoin-price-value-prediction-bubble.

43. Brian Fung, “Move Deliberately, Fix Things: How Coinbase Is Building a Cryptocurrency Empire,” The Washington Post (May 17, 2018), https://www.washingtonpost.com/business/economy/move-deliberately-fix-things-how-coinbase-is-building-a-cryptocurrency-empire/2018/05/17/623d950c-587c-11e8-858f-12becb4d6067_story.html.

44. Theodore Schleifer, “Bitcoin Broker Coinbase Booked $1 Billion in Revenue Last Year — So the Company Has Told Hovering VCs to Back Off,” Recode (January 22, 2018), https://www.recode.net/2018/1/22/16911692/cryptocurrency-bitcoin-trading-coinbase-revenue-secondary.

PART II

Historical Case Studies

Each of the five lenses presented in Part I—microeconomic, macroeconomic, psychological, political, and biological—might be used on its own to solve puzzles. But the application of the five lenses in concert is suited to addressing mysteries. Part II illustrates the power of a multi-lens approach through several short case studies. From tulips in Holland to financial crises in Asia to McMansions in America and empty malls in China, there is no part of the world that has proven exempt from financial turmoil. Part II demonstrates how a multi-lens perspective might have helped observers to identify these bubbles before they burst.

CHAPTER 6

Tulipomania: A BUBBLE IN SEVENTEENTH-CENTURY HOLLAND

Speculation, it has been noted, comes when popular imagination settles on something seemingly new in the field of commerce or finance. The tulip, beautiful and varied in its colors, was one of the first things so to serve.

—John Kenneth Galbraith

During the 1630s in the Netherlands, a series of events occurred that have been popularized as one of the first recorded financial bubbles. Tulip bulbs escalated in price to the point that particularly rare bulbs traded for sums equivalent to several decades of an average salary.[1] More concerning than this rapid rise in the prices of rare bulbs, however, was a similarly rapid—albeit less dramatic—rise in the price of common tulip bulbs for which there were no meaningful supply constraints. Between November 1636 and January 1637, many tulip prices rose by a factor of 10.

After providing a basic overview of tulips and what made them particularly unique in seventeenth-century Holland, the chapter discusses the social, political, and economic context of the times. Finally, the chapter evaluates Tulipomania via the microeconomic, macroeconomic, psychological, political, and biological lenses—illustrating how the multilens approach presented in Part I can be used to evaluate the likelihood of financial bubbles forming and bursting.

The Uniqueness of Tulips

The tulip is not native to Western Europe. The first noteworthy shipment of tulips from the eastern Mediterranean (where they had been growing in the wild) to western Europe arrived in 1562 on a ship that had arrived in Antwerp from Constantinople.[2] A year later, in 1563, tulips were brought to Holland by a botanist.[3] Cultivation of tulips is believed to have begun in earnest in 1593 when Flemish botanist Charles de l'Ecluse accepted a position at the University of Leiden to establish the Hortus Academicus.[4]

Tulips grow from bulbs but can reproduce by either seeds in the flower or buds that form on the mother bulb. If handled with the appropriate care, buds can directly produce another bulb. After a flower blooms, usually for a week or two in April or May, the original mother bulb disappears. In its place will be the primary bud, in the form of a functioning bulb. Other buds may also be on this new bulb. It is estimated that bulb-based reproduction is able to generate a replacement rate of between 100–150%, implying that meaningful supply growth is severely constrained. Reproduction via seed is a longer and slower process, but one likely to produce greater volume given the high seed count per flower. Seed-based reproduction takes 7–12 years to produce a bulb.[5]

According to Michael Dash, author of Tulipomania, one of the most thorough histories written of the bubble and its context,

It is impossible to comprehend the tulip mania without understanding just how different tulips were from every other flower known to horticulturalists in the 17th century … . The colors they exhibited were more intense and more concentrated than those of ordinary plants …[6]

The vivid colors, unique patterns, and unusual flames on the petals of a tulip are generated when a mosaic virus “breaks” the flower. This is an important fact because the virus (and corresponding flower coloration and design pattern) is only reproduced via bud-based reproduction. Seed-based reproduction does not reproduce the virus, and hence results in bulbs that may or may not later be broken into a pattern and color scheme that may or may not be deemed desirable. The virus also impacts the bulb's health. Although it may improve the visual appeal of the flower, the virus adversely affects the bulb's ability to reproduce.[7]

As the popularity of the tulips began to rise, they were classified into four primary categories, based on their coloration:[8]

- Colouren—Solid red, yellow, or white

- Rosen—Red or pink on a white background

- Violetten—Purple or lilac on a white background

- Bizarden—Red, brown, or purple on a yellow background

Further, many tulips were given grandiose names, often prefixed with Admirael or Generael. The most famous and sought-after variety was known as Semper Augustus.

Because of the tulip bulb's need to be in the ground for much of the year, physical tulip bulbs could only be uprooted and exchanged between May and September. To accommodate the need for speculators to trade these bulbs throughout the year, contracts were developed and notarized for purchasers to commit to buying (and sellers to commit to selling) bulbs at an arranged price at the end of the growing season. Because these futures contracts did not require full payment, they effectively enabled purchasers to obtain economic and financial exposure to tulip prices with leverage. According to bubbleologist Don Rapp,

Many sales were made on contracts in which the buyers put up little cash, but paid a down payment in kind, with personal goods, and promised to pay the seller a large cash payment after the buyer took possession (based on the expectation that he could sell the bulbs to another buyer at a higher price) … . Thus, buyers were highly leveraged.[9]

Until 1630 or so, most bulbs were sold by the pound. By the 1630s, the rapid price escalation was drawing the attention of financial investors, and the bulbs became desirable not only for their rare beauty, but for their ability to increase in value and be sold for a profit. By offering the prospect of rapid riches, the tulips became an excellent mechanism through which to speculate. As noted by British journalist Charles Mackay in 1841,

It was deemed a proof of bad taste in any man of fortune to be without a collection of them … . The rage for possessing them soon caught the middle classes of society, and merchants and shopkeepers, even of moderate means, began to vie with each other in the rarity of these flowers and the preposterous prices paid for them.[10]

Although data is quite limited on the actual trading activity in the bulbs, several of the recorded transactions are noteworthy. In 1633, three rare bulbs were purchased for the equivalent of a farmhouse. The frenetic pace of trading escalated for approximately four years more, when, possibly representing the peak, a rare Violetten Admirael van Enkhuizen bulb sold for 5,200 guilders, an all-time record. A bill of sale from one transaction indicated that one Viceroy bulb was exchanged for “two lasts of wheat, two lasts of rye, four fat oxen, eight fat swine, twelve fat sheep, two hogsheads wine, four tuns beer, two tons butter, one thousand pounds cheese, one bed (complete), one suit clothes, and one silver cup.”[11] Quite the exchange for a single tulip bulb! During the 1636–1637 period, some bulbs were changing hands 10 times a day.

Eventually, the bubble burst “at a routine bulb auction, when, for the first time, the greater fool refused to show up and pay. Within days, panic spread across the country. Despite the efforts of traders to prop up demand, the market for tulips evaporated. Flowers that had commanded 5,000 guilders a few weeks before now fetched one-hundredth that amount.”[12]

Fertile Soil for Bubble Formation

The 1630s were an extremely unique time in Holland. The country was embarking on its own golden age of peace and prosperity, having just won itself independence from Spain. Much of the effort and many of the resources that had been channeled into the military struggle for independence were now being productively deployed for commercial and economic purposes. The lucrative East Indies trade was dominated by Amsterdam, where it was thought that per-voyage profits of 400% were not uncommon. According to economist Peter Garber,

From 1620 to 1645, the Dutch established near monopolies on trade with the East Indies and Japan, conquered most of Brazil, took possession of the Dutch Caribbean islands, and founded New York … . Spain ceased to be the dominating power in Europe, and the Netherlands, though small in population and resources, became a major power center because of its complete control over international trade and international finance. The Dutch were to seventeenth century trade and finance as the British were to nineteenth century trade and finance … . At the time of the tulip speculation, the Netherlands was a highly commercialized country with well-developed and innovative financial markets and a large population of sophisticated traders.[13]

To celebrate this new era of prosperity, grand estates were erected across Amsterdam, most of which were surrounded by flower gardens. According to Mark Frankel, in his review of Tulipomania, “the Dutch population seemed torn by two contradictory impulses: a horror of living beyond one's means and the love of a long shot.”[14]

This bifurcated approach to thinking of prosperity was a direct result of the unprecedented commercial success the country had experienced following its war with Spain, as well as its recent experience with the bubonic plague. From 1635 to 1637, contemporaneous with the formation of the tulip bubble, the Netherlands was ravaged by the plague.[15] Although data from the period is not complete, the following data points illustrate the magnitude of the problem:

- Almost 18,000 people (more than one-seventh of the population) were killed in Amsterdam in 1636.

- More than 14,500 people died in Leiden (more than a third of the last available population estimate from 1622).

- Between August 1636 and November 1636, more than 14% of Haarlem died from the plague.

This context of tremendously good times (and corresponding financial innovations) with an ominous overlay of disease, uncertainty, and death proved a potent mixture for speculative desire among the Dutch. Mackay captures the spirit of speculation in the air, highlighting the social mood on both the boom and subsequent bust phases of Tulipomania:

The demand for tulips of a rare species increased so much in the year 1636 that regular marts for their sale were established on the Stock Exchange of Amsterdam, in Rotterdam, Harlaem, Leyden, Alkmar, Hoorn, and other towns … . Many individuals were suddenly rich … . Every one imagined that the passion for tulips would last forever, and that the wealthy from every part of the world would send to Holland, and pay whatever prices were asked of them … . Nobles, citizens, farmers, mechanics, seamen, footmen, mid-servants, even chimney-sweeps and old clotheswomen, dabbled in tulips. People of all grades converted their property into cash and invested it in flowers …

At last, however, the more prudent began to see that his folly could not last forever. Rich people no longer bought the flowers to keep them in their gardens, but to sell them again at cent per cent profit. It was seen that somebody must lose fearfully in the end. As this conviction spread, prices fell, and never rose again. Confidence was destroyed, and a universal panic seized upon the dealers … . Defaulters were announced day after day in all the towns of Holland. Hundreds who, a few months previously had begun to doubt that there was such a thing as poverty in the land suddenly found themselves the possessor of a few bulbs, which nobody would buy, even though they offered them for one-quarter of the sums they had paid for them … . Many who, for a brief season, had emerged from the humbler walks of life, were cast back into their original obscurity. Substantial merchants were reduced almost to beggary, and many a representative of a noble line saw the fortunes of his house ruined beyond redemption.[16]

There are lots of theorized reasons for the bust phase of Tulipomania, ranging from regulatory intervention to a simple exhaustion of “greater fools.” Professor Earl Thompson at the University of California has suggested that the primary reason for the boom and subsequent bust was due to regulatory changes that effectively converted futures contracts into option contracts and thereby created asymmetric reward for limited risk.[17]

To understand why such a change might have a dramatic difference in the price purchasers might be willing to pay, it is best to think of tulip futures as actually paying full price today for a tulip bulb to be delivered in the future. At the time of delivery, the owner of the future contract will be entitled to the gain or loss from their original purchase price. For instance, if I agree to buy a Semper Augustus bulb from you today at a price of $25,000 for delivery in nine months, I effectively own the bulb today at that price. If the price of the bulb goes up, I do not have to pay more, and the gains are mine to keep. Likewise, if the price falls, I suffer the losses of having paid the higher price. I have indeed bought the bulb (to be delivered in the future) for $25,000 and am subject to both the gains and losses that emanate from price movements.

If, instead, I choose to purchase an option contract on the bulb with a price of $25,000, then I have acquired the right—but not the obligation—to purchase the bulb in the future. If the price is above $25,000, I am likely to purchase the bulb. If however, the price falls, then I can simply walk away from the deal and forfeit the money I paid to acquire the option. Given that options offer disproportionate (in theory, unlimited) gain with limited loss potential (your maximum loss is the price you paid to get the option), it is easy to understand why—particularly in a rapidly rising price environment—options might prove a more attractive manner through which to speculate.

Thompson argues that regulatory changes that effectively converted futures contracts into option contracts in November 1636 account for the massive upward surge, while the February 1637 revision to this change accounts for the bust: “The contract price of tulips in early February 1637 reached a level that was about 20 times higher than in both early November 1636 and early May 1637 … and it was simply a period during which the prices in futures contracts had been legally, albeit temporarily, converted into options exercise prices.”[18]

The Boombustology of Tulipomania

Having briefly described Tulipomania, we now turn to evaluating the events of the time through the five primary lenses presented in Part I of the book. Each lens considers relevant facets of Tulipomania. The objective of this section is to paint a multidisciplinary picture of the boom and bust sequence that characterized this bubble.

Microeconomics

The primary focus of the microeconomic lens discussed in Chapter 1 was the tendency of a financial phenomenon to move toward or away from an equilibrium price. Though we have limited data from the time, the price action of tulips over the 1636–1637 period does not suggest a tendency toward equilibrium. Rather, the fact that prices rose 10× for rare bulbs over a period of several weeks at the peak of the mania suggests that a reflexive process with positive feedback loops may have been at work. In fact, this may even have been the case as early as 1630.

According to George Soros, a reflexive situation is one in which perception not only reflects the so-called fundamentals but also affects the fundamentals. Was this the case in the tulip markets of 1630s Holland? Although data is limited, the evidence we do have suggests that it might have been the case. Consider the fact that “a trader at Harlaem paid one half of his fortune for a single root, not with the design of selling it again at a profit, but to keep in his own conservatory for admiration of his acquaintance.”[19] Is this a case of prices reflecting or affecting demand?

Even economist Peter Garber, who has suggested that the price behavior of rare tulip bulbs resembles the price behavior of other rare bulbs and is inherently a reflection of supply and demand fundamentals, concedes that the price behavior of common bulbs “defies explanation.”[20] By definition, any situation in which the laws of supply and demand are suspended (even temporarily) may be deemed one that does not tend toward equilibrium. Further, although it is clear that an excess of buyers drove prices higher, it seems conceivable that the higher prices generated additional demand, a dynamic that usually indicates the presence of a bubble.

Holland's Liquidity and Money

In an interesting article titled “The Dutch Monetary Environment during Tulipomania,” Doug French described the impact of increased money supplies on tulip prices.[21] Because of its global economic dominance, the Netherlands became the recipient of massive capital inflows. The stability of banking systems in Amsterdam, notes French, created “the impetus that channeled large amounts of precious metals being discovered in the Americas, and to a lesser degree in Japan, toward Amsterdam.”[22] In addition to these voluntary flows of precious metals toward Holland, precious metals also came to Amsterdam as a result of Dutch seizure of Spanish vessels possessing wealth en route from the Western Hemisphere.

Data from French's article tells the story. Table 6.1 is the best proxy we have for the growth of money supply that was occurring in the Netherlands: mint output. Table 6.2 demonstrates the balances and metal stock at the Bank of Amsterdam. Given the importance of reserves in the money-creation process, the noticeable growth in mint output helps explain the increase in the Bank of Amsterdam's total balances. Might this increase of money in circulation have found its way into the supply and demand equation of tulips, thereby affecting their prices?

Table 6.1 Total Mint Output in Seventeenth-Century Southern Netherlands (Guilders)

Source: Jan a Van Houtte and Leon Van Buyten (1977), as quoted in “The Dutch Monetary Environment during Tulipomania” by Doug French.

Years

Gold

Silver

Copper

Total

% Change

1628–1629

153,010

2,643,732

4,109

2,800,851

1630–1632

364,414

8,838,411

6,679

9,209,503

228%

1633–1635

476,996

16,554,079

17,031,075

84%

1636–1638

2,917,826

20,172,257

23,090,083

36%

1639–1641

2,950,150

8,102,988

11,053,138

–52%

1642–1644

2,763,979

1,215,645

47,834

4,027,458

–63%

Table 6.2 The Bank of Amsterdam's Balance Sheet Balloons (Currency: Florins)

Source: J. G. Van Dillen, History of the Principal Public Banks. New York: Augustus Kelley, 1964, as quoted in Doug French, “The Dutch Monetary Environment during Tulip Mania,” The Quarterly Journal of Austrian Economics, 9, no. 1 (Spring 2006).

Year

Total Balances

% Change

Metal Stock

% Change

1630

4,166,159

3,105,449

1631

3,784,047

–9%

2,976,742

–4%

1632

3,636,079

–4%

3,281,113

10%

1633

4,272,224

17%

3,866,890

18%

1634

3,995,666

–6%

3,474,527

–10%

1635

3,860,342

–3%

3,416,112

–2%

1636

3,992,338

3%

3,486,306

2%

1637

5,680,522

42%

5,315,576

52%

1638

5,593,750

–2%

5,256,606

–1%

1639

5,802,729

4%

5,446,002

4%

Although it is extremely difficult, given data constraints, to accurately identify the destination of this new money, it seems possible that the increase in money at least partially affected the demand for tulips and their prices. Similarly, it is also possible that the rapid gains in tulip prices generated additional deposits that increased the money supply.

Further, because many of the tulips were traded via early derivative contracts resembling today's futures contracts (due to the growing season constraints), the entire system was built on a precarious foundation of leverage. Data to confirm this hypothesis is lacking, but the fact that most of the transactions required speculators to put a fraction of their purchase price down led to significant embedded leverage. Further, if speculators were buying bulbs (on leverage) with the intention of selling them at higher prices, and not having the financial ability to service or repay the amount that they were basically borrowing, they were effectively engaging in a form of Ponzi finance. Such arrangements, as highlighted by Minsky's financial instability hypothesis in Chapter 2, are inherently destabilizing and highlight an increasingly imminent correction.

The Psychological State of Tulipjobbers

Given the end of the war with Spain and the impact of the bubonic plague on the residents of seventeenth-century Holland, residents were likely in a particularly vulnerable psychological state. The uncertainty of life due to rampant disease generated a short-term orientation and focus on the present and immediate future. Longer-term thinking was considered wasted thought. Alongside these reminders of mortality, however, overconfidence was ubiquitous during this golden age of the Netherlands’ economic history. In short, economic optimism combined with the heightened uncertainty of life to generate and strengthen a gambling tendency, thereby magnifying bubble possibilities.

Might the financial extremes have taken place without these conditions? Although such counterfactuals are mere intellectual speculation and there is no way to actually know what might have occurred under such “what if” scenarios, it seems unlikely that the bubble would have formed and ballooned as rapidly and as dramatically as it did in the absence of economic overconfidence or disease-inspired fatalism.

Mackay's account of the events captures the psychological conditions that prevailed among the general population: “Everyone imagined that the passion for the tulips would last forever.”[23] Given recent economic successes in the country, participants in the tulip market were likely affected by many of the psychological biases discussed in Chapter 3, including anchoring on the last price, insufficient adjusting of a range around the anchor, and believing regression in prices was unlikely. Might it have been possible that newfound riches from the East Indies were being treated as house money and gambled in the tulip market? Perhaps gains from tulips were themselves considered house money. Finally, the illiquidity that arose in the market for tulip bulbs might have been an expression of the endowment effect in action. Is it conceivable that sellers were over-valuing what they owned, thereby preventing market-clearing transactions from taking place?

Political and Regulatory Considerations

A large part of the confidence generated among the Dutch was driven by the political and economic successes they had been experiencing as a country. The winning of independence from the Spanish, combined with massive innovation and virtual domination of world trade, made the population extremely confident: perhaps overconfident.

A counterfactual scenario is again worth considering. If the Dutch had still been fighting a war with Spain, and resources that were channeled toward economic progress were instead being channeled toward military actions, would the Dutch have been as confident as they were? Although it is impossible to tell, it does not seem that they would have been as confident.

Perhaps most pertinent to our study of Tulipomania, however, is the regulatory and political dynamics that drove individual speculators. Specifically, the conversion of futures contracts into option contracts is the most significant feature. Such a policy change is a modification of property rights, and as noted in Chapter 4, such meddling by governments dramatically impacts the supply and demand dynamics that drive price determination.

Underlying the conversion was a political process in which many of Holland's influential elites, speculators who included politicians, had lost a great deal of money in the October–November 1636 tulip price correction. Because the losses were borne by leveraged (professional) speculators, tulip growers—many of whom had made a fortune during the prior price surge—became the object of resentment. Not surprisingly, politicians (many of whom had personally lost money) met with the enraged public to help address the issue. The solution discussed: convert the futures contracts into call option contracts.

These efforts to support prices instead created a buying frenzy. The debate that ensued between tulip growers and speculators focused almost exclusively on the option premium to be paid. Although public officials and speculators (often one and the same) had initially suggested the option premium be priced at 0% of the original futures contract price, the growers pushed back. Thompson summarizes the political dynamic between planters and buyers with respect to the option price:

The public officials were suggesting 0%. However, the planters were not totally lacking in political power. Although, after lengthy deliberations, the planters subsequently announced that they would, as accepted, accede to the conversion of their contracts and accept a price equal to a mere 10% of the contract price, they demanded a later conversion date than the October date that had been publicly supported by most of the government officials. In particular, the planters announced, again on February 24th, that they would convert only those contracts that had originated after November 30th, a date by which virtually all traders knew that the ostensible futures prices would be converted into option exercise prices, with a 0–10% price for the option to be subsequently determined by Holland's legislatures and courts.[24]

By effectively fiddling with the supply and demand–driven price-discovery process, regulators exacerbated the boom by encouraging more “swing for the fences” style speculation. Not surprisingly, this is precisely what seems to have occurred when they considered converting futures contracts to options contracts.

More than Botanical Biology

The two key insights from a biological perspective relate quite well to the Tulipomania phenomenon: The epidemic lens provides an interesting framework through which to gauge the maturity of the boom, and the swarm logic of leadership by example provides an explanation of how so many people could be misled into such inappropriate pricing.

Let's begin with the epidemic logic. A key lesson from Chapter 5 was that the participation of amateur investors heralded the beginning of the end. Just as the involvement of professional financiers in a market is rarely alarming as it represents their normal course of activities, the involvement of amateurs and historical nonparticipants is very concerning as it indicates the pool of vulnerable and potentially infectable individuals is dwindling. Thus, the involvement of “nobles, citizens, farmers, mechanics, seamen, footmen, maid-servants, even chimney sweeps and old clotheswomen”[25] in the tulip markets is particularly troublesome and indicates a significantly advanced (perhaps peaking?) stage of the boom.

The second biological lens of relevance to Tulipomania is the emergence or, more specifically, the impact of seemingly informed leadership on group behavior. Here again, Mackay's account of the events is informative. In particular, references to the participation in Tulipomania of “Councellor Herwart, a man very famous in his day for his collection of rare exotics”[26] indicated a willingness on the part of uninformed members of the speculative swarm to follow the movement of such informed individuals. Further, the fact that “many learned men, including Pompeius de Angelis, and the celebrated Lypsius of Leydan, the author of the treatise ‘De Constantia,’ were passionately fond of tulips”[27] further validates the view that silent leadership of seemingly informed individuals contributed to the development of a group consensus. (Given limited data from the time, there is no better gauge of seemingly informed status than fame, and these descriptions imply fame.)

The Multilens Look

The purpose of this chapter was not to be exhaustive in its treatment of Tulipomania or any facet of the period. Rather, it was to demonstrate the power of a multidisciplinary framework through which to analyze bubbles. A summary of the Tulipomania discussion is listed in Table 6.3. The next chapter will similarly illustrate the power of the five-lens framework during the Great Depression.

Table 6.3 The Five-Lens Approach to Tulipomania

Lens

Notes

Microeconomics

Higher prices induced buyers.

Lower prices induced sellers.

Macroeconomics

Hot money inflows provided cheap capital.

Financial innovation (leverage via futures contracts).

Psychology

Political-economic inspired overconfidence.

Conspicuous consumption / trophy bulbs.

“New era” thinking (golden age).

Politics

End of war.

Government meddling in property rights, distorting price mechanism.

Biology

Amateur investors.

Silent leadership.

Notes

1. Donald Rapp, Bubbles, Booms and Busts: The Rise and Fall of Financial Assets (New York: Copernicus Books, 2009).

2. John K. Galbraith, A Short History of Financial Euphoria (New York: Penguin Books, 1990).

3. Rapp, Bubbles, Booms and Busts.

4. Michael Dash, Tulipomania: The Story of the World's Most Coveted Flower (New York: Crown Publishers, 2000), 59–60.

5. Peter Garber, Famous First Bubbles: The Fundamentals of Early Manias (Cambridge, MA: MIT Press, 2000).

6. Dash, Tulipomania.

7. Ibid.

8. Ibid.

9. Rapp, Bubbles, Booms and Busts.

10. Charles Mackay, Extraordinary Popular Delusions and the Madness of Crowds (New York: John Wiley & Sons, 1996).

11. “The Tulip Mania,” Harper's New Monthly Magazine LII, no. CCCXL (April 1876), http://en.wikisource.org/wiki/The_Tulip_Mania.

12. Mark Frankel, “When the Tulip Bubble Burst” (Book Review of Tulipomania: The Story of the World's Most Coveted Flower), BusinessWeek (April 24, 2000).

13. Garber, Famous First Bubbles.

14. Frankel, “When the Tulip Bubble Burst.”

15. Garber, Famous First Bubbles.

16. Mackay, Extraordinary Popular Delusions.

17. Earl A. Thompson, “The Tulipmania: Fact or Artifact?” Public Choice 130, no. 2 (2007).

18. Ibid.

19. Mackay, Extraordinary Popular Delusions.

20. Peter Garber, “Tulipmania,” Journal of Political Economy, 97, no. 3 (1989): 536.

21. Doug French,“The Dutch Monetary Environment During Tulipmania,” The Quarterly Journal of Austrian Economics 9, no. 1: 3–14.

22. Ibid.

23. Mackay, Extraordinary Popular Delusions.

24. Thompson, “The Tulipmania.”

25. Mackay, Extraordinary Popular Delusions.

26. Ibid.

27. Ibid.

CHAPTER 7

The Great Depression: FROM ROARING TWENTIES TO YAWNING THIRTIES

Optimism built on optimism to drive prices up. Then came the crash and the eventual discovery of the severe mental and moral deficiencies of those once thought endowed with genius and their consignment, at best, to oblivion, but, more grimly, to public obloquy, jail, or suicide.

—John Kenneth Galbraith

The Great Depression began in October 1929 with the U.S. stock market crash and quickly became a severe worldwide economic contraction.[1] Before the Great Crash, however, a significant boom and bust sequence took place in Florida, one that revealed the speculative tendencies of the time. In this chapter, we'll discuss the Florida land boom that took place during the mid-1920s and the rapid ascent of the stock market in the late 1920s before evaluating the events via the five lenses presented in Part I of the book.

Castles in the Sand

The great Florida real estate bubble of the 1920s was a revelatory manifestation of the speculative tendencies that were sweeping through America following World War I. Confidence was running high, and by the mid-1920s, an unsustainable boom in Florida land was underway. In fact, to many, the rapid boom and bust of Florida real estate was thought to be like castles built in sand. It only took high tide to wash them away. One of the most prominent developers of the times, Carl Graham Fisher, was also a promoter of the Indy 500 and helped create some of the first transcontinental roads. Fisher was one of the primary reasons that Florida transformed into the hottest market during a great bull market. Fisher successfully converted portions of South Florida into a heavenly combination of golf, polo, deep sea fishing, luxury hotels, and glamour.

Although property fever spread throughout Florida, migrating up the state's east coast and west coast, Miami represented ground zero of the speculative tendency. Consider the following passage from a chapter titled “Home, Sweet Florida” in the book Only Yesterday, written by Lewis Allen in 1931:

There was nothing languorous about the atmosphere of tropical Miami during the memorable summer and autumn of 1925. Miami had become one frenzied real estate exchange. There were said to be 2,000 real estate offices and 25,000 agents marketing house-lots or acreage … . The city fathers had been forced to pass an ordinance forbidding the sale of property in the street, or even the showing of a map, to prevent inordinate traffic congestion … . A traveler caught in a traffic jam counted the license-plates of eighteen states among the sedans and flivvers waiting in line. Hotels were overcrowded. People were sleeping wherever they could lay their heads, in station waiting-rooms or in automobiles. The railroads had been forced to place an embargo on imperishable freight in order to avert the danger of famine; building materials were now being imported by water and the harbor bristled with shipping. Fresh vegetables were a rarity, the public utilities of the city were trying desperately to meet the suddenly multiplied demand for electricity and gas and telephone service, and there were recurrent shortages of ice.[2]

Not exactly a description of sparsely attended Sunday afternoon open houses in suburban settings! As the description reveals, there was an intense flurry of activity that had drawn many from near and far and swirled them into a speculative frenzy. The other prevalent component of the times was the effective use of leverage via the 10% down payments made to “buy” land. This facilitated sales and postponed the tiresome formalities of recording deeds, etc. An executive of the Retail Credit Company of Atlanta described the sales process quite succinctly:

Lots are bought from blueprints, they look better that way … . Reservations are accepted. This requires a check for 10 per cent of the price of the lot the buyer expects to select. On the first day of sale, at the promoter's office in town, the reservations are called out in order, and the buyer steps up and, from a beautifully drawn blueprint, with lots of dimensions and prices clearly shown, selects a lot or lots, get a receipt in the form of a “binder” describing it, and has the thrill of seeing “SOLD” stamped on the blue-lined square which represents his lot, a space usually fifty by a hundred feet of Florida soil or swamp.[3]

Allen continues his description of the speculative frenzy, noting that few actually intended to purchase the land:

The binder, of course, did not complete the transaction. But few people worried much about the further payments which were to come. Nine buyers out of ten bought their lots with only one idea, to resell, and hoped to pass along their binders to other people at a neat profit before even the first payment fell due at the end of thirty days.[4]

Allen cites seven primary causes for the speculative land boom that took place in Florida during the mid-1920s: (i) the climate; (ii) accessibility to the populous northeast United States; (iii) the automobile, which Allen notes “was making America into a nation of nomads, teaching all manner of men and women to explore their country …”; (iv) abounding national confidence inspired by years of economic prosperity under the Coolidge administration; (v) the backlash against “the very routine and smoke and congestion and twentieth century standardization of living” that created a desire for country club living; (vi) the success of Southern California's resort-image developments; and (vii) the belief that Florida offered a chance to develop sudden wealth (i.e. one could get rich quick!).

Eventually, however, the greater fools stopped showing up, and the boom turned into a bust. The land boom began to collapse during the late spring and summer of 1926 when binder-holders began defaulting on the payments they were supposed to make. Rapp notes how the embedded leverage combined with other developments, including two hurricanes, to put the finishing touches on the bust:

Many of those with paper profits found that the properties they owned were preceded by a series of purchases and sales, all at 10% down, and as many of these defaulted, the only options were to either hold onto the land at a great loss, or default. The land was often burdened with taxes and assessments that amounted to more than the cash received for it, and much of the land was blighted with a partly constructed development. As the deflation expanded, two hurricanes added the finishing touches to the bursting bubble. The hurricanes left four hundred dead, sixty-three hundred injured, and fifty thousand homeless.[5]

Following the bust, Henry Villard described the images he saw as he drove into Miami in The Nation:

Dead subdivisions line the highway, their pompous names half-obliterated on crumbling stucco gates. Lonely white-way lights stand guard over miles of cement sidewalks, where grass and palmetto take the place of homes that were to be … . Whole sections of outlying subdivisions are composed of unoccupied houses, past which one speeds on broad thoroughfares as if traversing a city in the grip of death.[6]

Allen highlights the economic impact on Miami by citing bank clearings data (see Table 7.1). After rising steadily during the early 1920s and crossing $1 billion in 1925, bank clearings steadily fell.

Table 7.1 Miami Bank Clearings

Year

Bank Clearings

1925

$1,066,528,000

1926

$632,867,000

1927

$260,039,000

1928

$143,364,000

1929

$142,316,000

Noting that this economic distress took place during “the very years when elsewhere in the country prosperity was triumphant,” Allen eloquently summarizes the Florida land bust: “Most of the millions piled up in paper profits had melted away, many of the millions sunk in developments had been sunk for good and all, the vast inverted pyramid of credit had toppled to earth, and the lesson of the economic falsity of a scheme of land values based upon grandiose plans, preposterous expectations, and hot air had been taught in a long agony of deflation.”[7]

From Booming Twenties to Busted Thirties

It is impossible to understand the Great Depression without understanding the Roaring Twenties. The economic boom of the 1920s is perhaps as significant a development in the history of booms as the Great Depression is in the history of busts. While lots of attention has been given to the Great Depression, it seems unlikely that it would have been as significant without the less-addressed boom of the 1920s.

There are numerous factors that help explain the 1920s boom. To begin, not unlike the Dutch military successes, the United States and its allies had just won the First World War, and not unlike the Dutch following their victory over Spain, American confidence was running high. WWI had helped the country accelerate its transition from an agricultural economy to an industrial nation. The Federal Reserve system, which had been created in 1913, was seen by many as the solution to the business cycle problem (i.e. the Federal Reserve would be able to steer the economy with such precision that booms, busts, and blowups would never again occur).

Mass production had been gaining prominence and reduced the costs of many goods, productivity soared[8] while unit costs sank, and several industries appeared poised to change the world. In particular, cars and radios were the “new thing” of the day, and there seemed to be limitless demand for these goods. Automobiles were increasingly commonplace, and by the 1920s, more than 50% of Americans owned cars.[9] Transportation was being revolutionized, and trains and cars provided the means for domestic commerce to boom with increasingly lower frictional costs. Taxes were low, consumer optimism ran high, and electricity distribution was now so widespread that most Americans were “on the grid.” Most industries (agriculture being a primary exception) were booming as consumption and investment were supported by a massive tailwind of hope and optimism.

According to John Kenneth Galbraith, the real economic gains in the 1925–1929 period were substantial. Gross National Product was up 13% in five years, auto production was up 23% in three years, industrial production was up 64% in seven years. He says, “Throughout the twenties, production and productivity per worker in manufacturing industries increased by about 43 percent. Wages, salaries, and prices all remained relatively stable.”[10]

Although economic progress was indeed substantial and based on genuine increases in productivity, asset prices—specifically, the stock market—eagerly reflected these developments and likely more. It is hard to know when exactly the stock market began to distance itself from the extraordinary fundamental developments and entered the realm of speculative excess, but Galbraith eloquently notes that “early in 1928, the nature of the boom changed. The mass escape into make-believe, so much a part of the true speculative orgy, started in earnest … . The time had come, as in all periods of speculation, when men sought not to be persuaded of the reality of things but to find excuses for escaping into the new world of fantasy.”[11]

Galbraith goes on to note how the market's quiet winter months of 1928 were followed by stock prices rising “not by slow, steady steps, but by great vaulting leaps.”[12] Figure 7.1 demonstrates the magnitude of the leaps by plotting the real S&P Composite Index divided by the 10-year moving average of real earnings. By using inflation-adjusted numbers and a 10-year earnings period, the data measure how expensive the market is relative to corporate profit-generating abilities and is not subject to single year surges (or depressions) in profits. Note the chart goes back to 1881 and stops in October 1929, right before the market crash.

Figure 7.1 Real S&P Composite Index as a Multiple of 10-Year Moving Average Earnings, January 1881–October 1929

Source: Robert Shiller, www.econ.yale.edu/~shiller/data.

Lest you think the index was the only manifestation of the market's upward jaunt, Table 7.2 summarizes the movement in share price of 12 very popular and widely held stocks between March 3, 1928 and September 3, 1929.

Table 7.2 The Big Bull Market

Source: Frederick Lewis Allen, Only Yesterday: An Informal History of the 1920s (New York: Harper & Row, 1931).

Company

Opening Price (3/3/28)

High Price [*] (9/3/29)

% Change

American Can

77

181 ⅞

136.2%

AT&T

179 ½

335 ⅝

86.9%

Anaconda Copper

54 ½

162

197.3%

Electric Bond & Share

89 ¾

203 ⅝

126.9%

General Electric

128 ¾

396 ¾

208.2%

Montgomery Ward

132 ¾

466 ½

251.4%

New York Central

160 ½

256

59.5%

Radio (RCA)

94 ½

505

434.4%

Union Carbide

145

413 ⅝

185.3%

US Steel

138 ⅛

279 ⅛

102.1%

Westinghouse

91 ⅝

313

241.6%

Woolworth

180 ¾

251

38.9%

[*] Adjusted to reflect the effects of stock splits and rights issues.

The beginning of the economic bust is widely believed to be the October 1929 crash of the stock market. Although most students of the Great Depression focus on the events of Tuesday, October 29, 1929, much can be learned by studying the three prior trading sessions—back to and including the prior Thursday.

On Black Thursday, as October 24, 1929 has since been known, the market entered a serious state of panic—with no immediate or palpable cause. Historian Edward Chancellor notes, “Unlike former stock market panics, it was not preceded by tightness in the money market. No banking, brokerage, or industrial failures served as a trigger—and yet panic there was.”[13] Many stocks were dropping more than several percentage points between trades, and the market appeared to be in the midst of a complete meltdown—until calm was restored by JP Morgan and others who jointly entered the market and began publicly buying to support share prices. By the end of the day, the Dow Jones Industrial Average had recovered most of its losses and closed down 6 points to 299. Nearly 13 million shares had been traded on the NYSE, which was almost triple then-average volume.

Friday was a relatively calm day, and many brokers worked through the weekend to catch up on trades and to calculate margin calls that needed to be sent out to clients. The market fell 38 points to 260 on Monday, and by Tuesday, the market was in complete panic. Chancellor describes the events as follows: “On the floor of the Stock Exchange, a broker grabbed a messenger by his hair, another fled the floor screaming like a madman, jackets were torn, collars dislodged, and clerks in their frenzy lashed out at each other … . On Black Tuesday, the glamour stocks of the bull market suffered the worst damage.”[14] Table 7.3 summarizes the magnitude of the one-day fall among these glamour stocks. To put this in context and gain a better sense of the drop, imagine a 35% drop in the value of Apple shares.

Table 7.3 The Rapid Fall of Glamour Stocks

Stock

10/28/29

10/29/29

% Change

Radio (RCA) [*]

$40.25

$26.00

–35.4%

GSTC

$60.00

$35.00

–41.7%

Blue Ridge

$10.00

$3.00

–70.0%

United Corp

$26.00

$19.30

–25.8%

First National

$5,200

$1,600

–69.3%

National City

$455.00

$300.00

–34.1%

[*] Adjusted for splits.

The slide into depression that followed the stock market crash was likely caused by numerous factors, including (but definitely not limited to) the presence of bank failures,[15] “sticky wages,”[16] adherence to the gold standard,[17] the unsustainable growth of consumer credit,[18] and high leverage levels among consumers, corporations, and other organizations.[19]

Allen cites seven economic diseases that plagued businesses around the world and contributed to the conversion of the Great Crash in America into the global Great Depression: (i) overproduction of capital and goods, (ii) artificial commodity prices, (iii) collapse of silver prices and the corresponding drop in purchasing power of Asian consumers, (iv) the international financial derangement caused by the shifting of gold to France and the United States, (v) unrest in foreign countries, (vi) the self-generating and vicious feedback loops of confidence that affected the economy, and (vii) “the profound psychological reaction from the exuberance of 1929” and corresponding destruction of consumer and corporate confidence.[20]

The full impact of the Great Depression that followed the stock market crash of 1929 is hard to understand for Americans born and raised following the end of World War II. Robert Samuelson summarized the massive impact it had on the American economy, the world economy, and the world that emerged after the Great Depression:

The Great Depression of the thirties remains the most important economic event in American history. It caused enormous hardship for tens of millions of people and the failure of a large fraction of the nation's banks, businesses, and farms. It transformed national politics by vastly expanding government, which was increasingly expected to stabilize the economy and to prevent suffering. Democrats became the majority party. In 1929 the Republicans controlled the White House and Congress. By 1933, the Democrats had the presidency and, with huge margins, Congress (310–117 in the House, and 60–35 in the Senate). President Franklin Roosevelt's New Deal gave birth to the American version of the welfare state. Social Security, unemployment insurance, and federal family assistance all began in the thirties.

It is hard for those who did not live through it to grasp the full force of the worldwide depression. Between 1930 and 1939 U.S. unemployment averaged 18.2 percent. The economy's output of goods and services (gross national product) declined 30 percent between 1929 and 1933 and recovered to the 1929 level only in 1939. Prices of almost everything (farm products, raw materials, industrial goods, stocks) fell dramatically. Farm prices, for instance, dropped 51 percent from 1929 to 1933. World trade shriveled: between 1929 and 1933 it shrank 65 percent in dollar value and 25 percent in unit volume. Most nations suffered. In 1932 Britain's unemployment was 17.6 percent. Germany's depression hastened the rise of Hitler and, thereby, contributed to World War II.[21]

The Boombustology of the Great Depression

Applying the five disciplinary lenses developed in Part I of the book proves fruitful in trying to understand the 1920s and 1930s from a bubble-spotting perspective. Let us now begin by evaluating the (lack of) equilibrium tendencies during the Florida real estate boom and the Roaring Twenties, as well as the subsequent Great Depression.

Disequilibrium Tendencies

How is it that individuals and institutions that found it logical and rational to pay one price for a security one day would not be willing to pay 50% of that price the next day? Surely such developments do not occur in a totally “efficient” market. In fact, the tendency toward equilibrium is one of the major tenets of microeconomics that seems to have broken down during both the booming 1920s and the busted 1930s.

The procyclical nature of leverage enabled consumers to buy more during good times as credit was easily obtained in a rising market. Financial innovations such as installment purchases (i.e. buy now, pay later) effectively increased consumer demand as the market boomed. When such credit contracted, not only did such credit-fueled buying slow, but so too did normal buying demand disappear, because it had been “brought forward” into the 1920s by access to this credit.

Perhaps the most obvious example of the self-reinforcing nature of the times is found in Florida. Allen captures the essence of the dynamic quite eloquently in describing the fate of an individual who experienced both the boom and bust as well as the accompanying joy and pain: “One man who had sold acreage early in 1925 for twelve dollars an acre, and had cursed himself for his situation when it was resold later in the year for seventeen dollars, and then thirty dollars, and finally sixty dollars an acre, was surprised a year or two afterward to find that the entire series of subsequent purchases was in default, that he could not recover the money still due him, and that his only redress was to take his land back again.”[22] Thus, just as higher prices induced more buyers, so too did lower prices generate more sellers. Not surprisingly, this dynamic generally failed to produce an equilibrium.

The economic bust that followed the Great Crash had a very reflexive component to it, with a very self-reinforcing element to the dynamics of business during the 1930s. As described in Only Yesterday, “each bankruptcy, each suspension of payments, and each reduction of operating schedules affected other concerns, until it seemed almost as if the business world were a set of tenpins ready to knock one another over as they fell; each employee thrown out of work decreased the potential buying power of the country.”[23] Again, the snowballing effect here seems to resemble a reflexive dynamic connected by confidence.

Inappropriate Interest Rates

Recall that one of the primary beliefs of the Austrian school of economics is that inappropriately cheap money results in malinvestment and overcapacity, which must be “cured” via capital destruction, deflation, and a general working-through of the excesses. Austrian economists believe that the meddling of central banks in setting the price of money distorts the economy and exacerbates the likelihood of booms and busts. Could this criticism be applied to the Great Depression? What role did the price of money play during the 1920s and 1930s?

According to Chancellor, “The Federal Reserve in Washington—the institution that had supposedly abolished panics—had inadvertently ignited the stock market boom by lowering rates in 1925.”[24] This was explicitly intended to help Britain manage the accelerating outflows of gold after their return to the prewar gold standard. Although this action might have been useful to the Brits, it had an extraordinary effect (i.e. increasing it) on the American appetite for risk.

Although such low-priced money found its way into increased corporate capital expenditures and increased consumer purchasing of both durable and consumable goods, one of its most powerful outlets was via margin loans used to enable the purchase of additional securities. Margin loans, which enable the purchase of financial securities with borrowed money, had grown concomitantly with the stock market's climb. By October 1928, credit extended by banks, brokerage firms, and other financing sources to investors had risen to nearly $16 billion, which equated to approximately 18% of the total stock market capitalization of the entire market.[25]

If inappropriately low rates had created the boom that manifested itself most evidently in the stock market, might more “normal” but higher rates be responsible for the bust that immediately followed the Great Crash? According to Shiller, “On February 14, 1929, the Federal Reserve Board raised the rediscount rate from 5 percent to 6 percent for the ostensible purpose of checking speculation. In the 1930s, the Fed continued the tight monetary policy and saw the initial stock market downturn evolve into the deepest stock market decline ever, and a recession into the most serious U.S. depression ever.”[26]

Perhaps the supply of money might help account for the boom–bust sequence. Figure 7.2 summarizes total money supply from June 1921 through June 1929. Might some of this money have found its way into asset markets?

Figure 7.2 Total Money Supply During the 1920s

Source: Murray Rothbard, America's Great Depression (Auburn, AL: Ludwig von Mises Institute, 2000), Table 1, 92.

Although it is very difficult to establish causality, it does appear that money supply was correlated with asset prices. These procyclical liquidity conditions likely exacerbated underlying boom and bust tendencies as asset prices correlated with the money supply.

The Psychological State of the Time

The psychological state of market participants during the 1920s was characterized by optimism and confidence inspired by self-reinforcing virtuous market developments. Might some of this optimism and confidence have translated into a sense of overconfidence and investor invincibility? Consider the following passage from Only Yesterday:

As people in the summer of 1929 looked back for precedents, they were comforted by the recollection that every crash of the past few years had been followed by a recovery, and that every recovery had ultimately brought prices to a new high point. Two steps up, one step down, two steps up again—that was how the market went. If you sold, you had only to wait for the next crash (they came every few months) and buy in again. And there was really no reason to sell at all: you were bound to win in the end if your stock was sound. The really wise man, it appeared, was he who ‘bought and held on.’[27]

“New era” thinking was clearly present, as noted by the prominent emergence of the automobile and radio industries. Aerospace and movie production played supporting roles among investors’ foci. The automobile soon replaced the railroads as the engine of commerce, and “it transformed the culture and geography of the nation; roads were surfaced, highways built, and garages erected to accommodate the increasing number of passenger cars, which rose from seven million to twenty-three million during the 1920s.”[28] Not surprisingly in this climate, General Motors’ share price increased by more than 10 times between 1925 and 1928.

The radio, launched by Westinghouse in 1920, also began to fascinate the investor with unlimited possibilities of information dissemination. The industry was dominated by Radio Corporation of America (RCA), often referred to by investors of the time as “Radio.” Known as the “General Motors of the Air” by investors,[29] Radio climbed from under $2 per share in 1921 to over $110 by 1929. In 1929, it was the most heavily traded stock on the New York Stock Exchange.

Aerospace also provided a believability to the new era thinking of the times, spurred in large part by Charles Lindbergh's solo crossing of the Atlantic in 1927; and the replacement of silent movies with “talkies” captured investor imagination.

The vision of the future held by most Americans in 1929 was one of unbridled optimism, a vision that likely generated and validated the (over)confidence that permeated investor sentiment. In describing the average American, Allen said he

visioned an America set free from poverty and toil. He saw a magical order built on the new science and new prosperity: roads swarming with millions upon millions of automobiles, airplanes darkening the skies, lines of high-tension wire carrying from hilltop to hilltop the power to give life to a thousand labor-saving machines, skyscrapers thrusting above one-time villages, vast cities rising in great geometrical masses of stone and concrete roaring with perfectly mechanized traffic—and smartly dressed men and women spending, spending with the money they had won by being far-sighted enough to foresee, way back in 1929, what was going to happen.[30]

As an additional manifestation of the (over)confidence of the times, 40 Wall Street reigned as the tallest building in the world in 1929, only to be outdone by the Chrysler Building in 1930, which itself was unseated from the throne by the Empire State Building eleven months later. Chapter 11 will develop this “tallest building” indicator a bit further. John Kenneth Galbraith succinctly and eloquently summarized the spirit (and literal construction activity!) of the times and the dynamic it imposed on prices: “Optimism built on optimism to drive prices up.”[31]

Political and Regulatory Conditions

A large part of consumer, business, and national confidence sprang from the ending of WWI and the redeployment of industrial efforts toward commercial, rather than military, ends. The Coolidge administration was particularly hands-off in its approach to handling markets, and prosperity was widespread.

Prohibition also played a role in the Florida land boom. Although unintended, the government's prohibition of the manufacture and sale of intoxicating beverages led money to flow to Florida, what William Johnson Frazer called “one of the country's leakiest spots on the country's dry border.” The result was a surge in revenue that was deposited in Florida banks, which, due to banking regulation at the time (i.e. banks had been granted state charters and mandated to conduct business only within the state), effectively mandated that they lend this money out within Florida.

Every aspiring politician yearns for an opportunity to blame incumbents and the existing system for the ills faced by society, and the Great Crash and economic slowdown that followed provided such an opportunity. The early 1930s typify this spirit, and Roosevelt's 1932 campaign for the presidency touted the failure of market economics and Wall Street's self-seeking greed. In fact, his inaugural address captures this spirit:

Let me assert my firm belief that the only thing we have to fear is fear itself—nameless, unreasoning, unjustified terror which paralyzes needed efforts to convert retreat into advance … . In such a spirit on my part and on yours we face our common difficulties. They concern, thank God, only material things. Values have shrunken to fantastic levels; taxes have risen; our ability to pay has fallen; government of all kinds is faced by serious curtailment of income; the means of exchange are frozen in the currents of trade; the withered leaves of industrial enterprise lie on every side; farmers find no markets for their produce; the savings of many years in thousands of families are gone. More important, a host of unemployed citizens face the grim problem of existence, and an equally great number toil with little return. Only a foolish optimist can deny the dark realities of the moment.

Yet our distress comes from no failure of substance. We are stricken by no plague of locusts. Compared with the perils which our forefathers conquered because they believed and were not afraid, we have still much to be thankful for. Nature still offers her bounty and human efforts have multiplied it. Plenty is at our doorstep, but a generous use of it languishes in the very sight of the supply. Primarily this is because the rulers of the exchange of mankind's goods have failed, through their own stubbornness and their own incompetence, have admitted their failure, and abdicated. Practices of the unscrupulous money changers stand indicted in the court of public opinion, rejected by the hearts and minds of men.

True they have tried, but their efforts have been cast in the pattern of an outworn tradition. Faced by failure of credit they have proposed only the lending of more money. Stripped of the lure of profit by which to induce our people to follow their false leadership, they have resorted to exhortations, pleading tearfully for restored confidence. They know only the rules of a generation of self-seekers. They have no vision, and when there is no vision the people perish.

The money changers have fled from their high seats in the temple of our civilization. We may now restore that temple to the ancient truths. The measure of the restoration lies in the extent to which we apply social values more noble than mere monetary profit.

Happiness lies not in the mere possession of money; it lies in the joy of achievement, in the thrill of creative effort. The joy and moral stimulation of work no longer must be forgotten in the mad chase of evanescent profits. These dark days will be worth all they cost us if they teach us that our true destiny is not to be ministered unto but to minister to ourselves and to our fellow men.[32]

Consider the biblical overtones invoked by using “money changers” rather than “speculators” and the overwhelming sense that someone must be blamed. Chancellor notes that “in place of market forces came federal welfare, housing and work programmes, bank deposit insurance, prices and incomes policies, minimum wage legislation, and a number of other measures. Speculation, whether in stocks, bonds, land, or commodities, was no longer to play such a key role in economic life.”[33]

The sweeping set of government programs were organized around the three Rs of relief, recovery, and reform. The economic policies of the Roosevelt administration were based on getting Americans back to work and alleviating economic hardships (i.e. relief), helping the American economy to recover toward full potential, and providing a new regulatory framework, complete with appropriate government authorities, to oversee the economy and prevent a repeat of the Great Depression. The New Deal policies produced a plethora of new government programs and agencies, including major programs such as Social Security and the Federal Deposit Insurance Corporation (FDIC) as well as the creation of the Securities and Exchange Commission (SEC).

Although such regulatory reform was designed to promote stability and prevent future hardships, it laid the roots of future troubles by creating issues of moral hazard through deposit insurance, a potentially unsustainable program of entitlements such as Social Security, labor market distortion via price floors (i.e. minimum wages), and the general growth of the government. The magnitude and impact of these programs by themselves suggest the 1920s and 1930s boom–bust sequence threatened the very fabric of the U.S. capitalist system. Although property rights were never directly threatened, the intention of Social Security and other programs was to redistribute from those who had, to those who needed.

Epidemic Thinking, Silent Leadership

One of the most important elements of a boom–bust sequence that helps us identify where in the cycle we might be is, to use language from epidemiology, the population of unaffected or unexposed individuals. When we hear that everyone, including those not traditionally active or invested in the market, is “in the market,” then we might naturally (and accurately) assume that the boom cycle is far along and very mature (perhaps approaching expiration), with a limited population of infectable participants.

Consider Allen's description of the market in 1929:

Grocers, motormen, plumbers, seamstresses and speakeasy waiters were in the market. Even the rebellious intellectuals were there: loudly as they might lament the depressing effects of standardization and mass production upon American life, they found themselves quite ready to reap the fruits thereof … . The Big Bull Market had become a national mania … . The speculative fever was infecting the whole country. Stories of fortunes being made overnight were on everybody's lips.[34]

Further, the development of financial products such as investment trusts to meet the desires of ordinary individuals to get involved in the market boomed: “During the first nine months of 1929, a new investment trust appeared for every working day and the industry issued over two and a half billion dollars worth of securities to the public.”[35] These two elements—namely the broad involvement in the market and the tremendous boom in products designed to meet the needs of the previously uninvested—clearly tipped the scales in favor of a very mature boom.

The epidemic lens also yields some insight into the aftermath of the Great Depression and subsequent speculative tendencies. The magnitude of the bust was such that a large percentage of the American population was negatively affected (some might say scarred) by the economic and financial implosion. Perhaps this “immunized” most citizens from the speculative fever that drives booms and busts. Might this be the reason that the United States did not have any major financial bubbles for decades after the Great Depression?

The logic of swarm leadership described in Chapter 5 is also helpful in understanding the 1920s and 1930s. For instance, the Florida land boom had lots of “informed” investors who drew the attention of the uninformed masses. John Martin, then governor of Florida, was quoted as having said “marvelous as is the wonder-story of Florida's recent achievements, these are but heralds of the dawn …” and S. Davies Warfield, president of the Seaboard Air Line Railway, supposedly talked of Miami's population exceeding 1 million within the next ten years.[36] Such informed members were able to lead the swarm of uniformed investors into the mid-1920s Florida land boom.

The list of similarly informed individuals who led the uninformed into the Great Crash is too long to mention, but the most famous quote is from Yale University professor Irving Fisher, who stated, “Stock prices have reached what looks like a permanently high plateau” just weeks before the stock market crash in 1929.

The Multilens Look

This discussion of the Great Depression was intended to demonstrate the power of a multidisciplinary framework through which to analyze financial extremes. A summary of the discussion is listed in Table 7.4. The next chapter will similarly illustrate the power of this five-lens framework during the Japanese boom and bust.

Table 7.4 The Five-Lens Approach to the Great Depression

Lens

Notes

Microeconomics

Reflexive credit/collateral tendencies.

Higher prices induced buyers.

Lower prices induced sellers.

Macroeconomics

Inappropriately cheap money.

Financial innovation (leverage via “binders”).

Psychology

New era thinking (new industries).

World's tallest skyscrapers (40 Wall, Chrysler, Empire State Building).

Politics

End of war.

Prohibition inspired money flows.

Re-regulation, blame game.

Biology

Amateur Investors (“national mania”).

Silent Leadership (Florida governor, JP Morgan).

Notes

1. This chapter is merely intended to be illustrative of the five lenses presented, not a comprehensive review of the Great Depression. Other treatments of the era are more thorough, and the curious reader is pointed toward them.

2. Frederick Lewis Allen, Only Yesterday: An Informal History of the 1920s (New York: Harper & Row, 1931).

3. Ibid.

4. Ibid.

5. Donald Rapp, Bubbles, Booms and Busts: The Rise and Fall of Financial Assets (New York: Copernicus Books, 2009).

6. Allen, Only Yesterday.

7. Ibid.

8. Some scholars, including Irving Fisher of Yale University, cited the work of Professor Paul Nystrom of Columbia University to justify the belief that America had entered a new level of productivity because of Prohibition, which began in 1920. By some estimates, eliminating alcohol might save $7bn of lost productivity.

9. Rapp, Bubbles, Booms and Bust.

10. John Kenneth Galbraith, The Great Crash 1929 (Boston: Houghton Mifflin, 1954).

11. Ibid.

12. Ibid.

13. Edward Chancellor, Devil Take the Hindmost: A History of Financial Speculation (New York: Plume, 1999).

14. Ibid.

15. For more detailed analysis, see Milton Friedman and Anna Jacobson Schwartz, The Great Contraction: 1929–1933 (Cambridge, MA: National Bureau of Economic Research, 1963).

16. For more detailed analysis, see Ben S. Bernanke and Kevin Carey, “Nominal Wage Stickiness and Aggregate Supply in the Great Depression,” in Ben S. Bernanke, Essays on the Great Depression (Princeton, NJ: Princeton University Press, 2000).

17. For more detailed analysis, see Ben S. Bernanke and Harold James, “The Gold Standard, Deflation and Financial Crisis in the Great Depression: An International Comparison,” in Bernanke, Essays on the Great Depression; and Barry Eichengreen, Golden Fetters: The Gold Standard and the Great Depression (New York: Oxford University Press, 1992).

18. For more detailed analysis, see Chancellor, Devil Take the Hindmost.

19. For more detailed analysis, see Richard C. Koo, The Holy Grail of Macroeconomics: Lessons from Japan's Great Recession (Hoboken, NJ: John Wiley & Sons, 2009).

20. Allen, Only Yesterday.

21. Robert Samuelson, “The Great Depression,” The Concise Encyclopedia of Economics, 1st ed.; www.econlib.org/library/Enc1/GreatDepression.html.

22. Allen, Only Yesterday.

23. Ibid.

24. Chancellor, Devil Take the Hindmost.

25. Ibid.

26. Robert Shiller, Irrational Exuberance, 2nd ed. (New York: Broadway Books, 2005).

27. Allen, Only Yesterday.

28. Chancellor, Devil Take the Hindmost.

29. Ibid.

30. Allen, Only Yesterday.

31. John Kenneth Galbraith, A Short History of Financial Euphoria (New York: Penguin Books, 1990).

32. Franklin D. Roosevelt (campaign speech, October 24, 1932), http://teachingamericanhistory.org/library/document/campaign-speech/.

33. Chancellor, Devil Take the Hindmost.

34. Allen, Only Yesterday.

35. Chancellor, Devil Take the Hindmost.

36. Allen, Only Yesterday.

CHAPTER 8

The Japanese Boom and Bust: A CREDIT-FUELED BUBBLE ECONOMY

The militaristic hubris that took Japan blindly into the Second World War found its counterpart in the speculative hubris of the Bubble Economy. History was repeating itself, except this time a stock market farce replaced the tragedy of war.

—Edward Chancellor

During the 1980s, Japan experienced an extraordinary speculative boom that resulted in a bust that has plagued the island nation for the last 20 years. This chapter describes the events that transpired during the 1980s and some of the resulting extremes witnessed in the course of unbridled speculation. The impact of the bust, which continues as this book is being written, is briefly considered as well, and the boom and bust are then evaluated via the five lenses presented in Part I.

Japan(ese) as Different

Japanese society emphasizes harmony. The primary religions in Japan, Buddhism and Shintoism, are heavily oriented toward collectivism. The heavy influence of Confucian ideals also strengthens the primacy of group harmony over individual success. Further, the Japanese, not unlike many other homogenous groups, genuinely think of themselves as unique and different from other societies and cultures. The Japanese hold a deep belief that they are unlike other races, religions, or frankly, humans. This belief is not one restricted to behavior, for as noted in Devil Take the Hindmost: A History of Financial Speculation, it actually begins with a notion that Japanese physiology is unique:

Japanese intestines were said to be different from those of Westerners and therefore unsuited to foreign beef and rice. It was even claimed that American skis were useless in Japan because the snow was different. At other times, pointing out such differences became a barely concealed expression of Japanese cultural nationalism and xenophobia: the Japanese brain was said to have a heightened sensitivity to the sounds of nature and a more intricate understanding of social relationships. The Japanese distrusted Western-style rationalism as being incompatible with the preservation of social harmony … . Japanese reason was described as “wet,” like the cloying rice of the national diet (which formed the glue of the community), while Western reason was “dry” and individualistic. Even in the ethical sphere, the Japanese were said to be different. They did not feel guilt, only shame on public revelation of misdeeds. At the root of all these differences, both real and spurious, lay a profound distrust of individualism, which found its counterpart in a strong attachment to community and deference to authority.[1]

This deep cultural focus on community (relative to the Western focus on individuals) manifests itself throughout Japan's political-economic systems. Although the West has historically[2] focused on a limited role for government in business, markets, and industry, the Japanese believed in an active role for government in delivering administrative guidance to companies and industries. Likewise, although Westerners distrust monopolies, Japanese seek industrial champions. Finally, Western distinctions between business matters and individual relationships have no counterpart among the Japanese. In Japan, relationships and social harmony supersede virtually all else.

Many Japanese assert that their society is less selfish and more long-term-oriented than the West. Perhaps due to their feudal roots[3] and collectivist culture, hierarchy reigned supreme. Samurai values emphasized frugality, and savings rates were high. Administrative guidance helped channel these savings into the most appropriate investments. Market share was deemed a better objective than profits as it aligned the organization toward long-term success.

Given the limited individual role in virtually all matters economic, a long-term orientation, cultural values oriented around frugality and thrift, and a system designed around governmental guidance, Japan was a highly unlikely place for a speculative bubble to form. Yet a speculative bubble driven by individualistic short-term pursuit of profits is exactly what Japan experienced in the mid- to late 1980s. The collective shock to Japan of the bubble and its subsequent bursting was monumental and continues to be felt today.

An Overview of the Bubble Economy

Japan had been absolutely devastated by the Second World War. In the aftermath, during which the United States and other nations provided meaningful economic support, Japan implemented numerous policies to promote savings. These savings, it can be argued, ultimately enabled banks to feel more “flush” and therefore effectively encouraged the expansion of credit—credit that fueled much of Japan's economic growth after the war.

The massive economic transformation that transpired between 1953 and 1973 generated tremendous confidence in the country. According to Paul Krugman, “in the space of two decades a largely agricultural nation became the world's largest exporter of steels and automobiles, greater Tokyo became the world's largest and arguably most vibrant metropolitan area, and the standard of living made a quantum leap.”[4] As a further illustration (albeit less direct) of the resignation that “Japan had won,” Krugman went on to author The Age of Diminished Expectations in which he effectively stated the United States was potentially losing the economic race to government/private partnerships like Japan.[5]

The Land Boom and Trophy Properties

During the 1980s, Japan's economy grew by leaps and bounds. The period was characterized by fast growth, low unemployment, and big profits. Although these conditions were highly supportive of rising asset prices, the property market raced ahead at unsustainable rates. The property bubble reached truly extraordinary heights, particularly when compared with America, and ultimately served as the foundation of the entire “Bubble Economy,” as it has since been known. Consider the following facts, as summarized by journalist-turned-strategist Christopher Wood:[6]

America is twenty-five times bigger than Japan in terms of its physical area. Yet Japan's property market at the end of 1989 was still reckoned by sober people in the government's Management and Coordination Agency to be worth over ¥2,000 trillion, or four times the estimated ¥500 trillion value of American property. This is truly history's greatest accumulation of wealth in one country. It creates ludicrous anomalies. In early 1990 Japan in theory was able to buy the whole of America by selling off metropolitan Tokyo, or all of Canada by hawking the grounds of the Imperial Palace.[7]

Crazy as the absolute land valuations might seem, the actively traded market that developed for golf course memberships is perhaps more noteworthy as another extreme of the land bubble. Wood's commentary captures the spirit of the times better than any other:

Not surprisingly, given the national obsession with golf, this became a ludicrously overheated market in the late 1980s. An estimated 1.8 million people own golf club memberships in Japan; the prices of these memberships, which are traded like securities, range from a few million yen up to the ¥250 million range. At the peak, Japan's 1,700 golf courses were estimated to have a total membership market value of some $200 billion.[8]

There are three primary reasons that land values in Japan were so high: physical scarcity, feudal tradition, and government policies. The following discussion will examine these three causal factors of the property bubble in greater detail, but for now, it is important to note that they created a market that was not particularly liquid.

Very few transactions (compared to what we might expect from such a highly valued market) actually took place. Although this might mean such high property values were meaningless, the fact that they served as collateral for a significant portion of bank lending meant that prices, regardless of their true “accuracy,” were very meaningful. In addition to supporting loans, much of the credit created through and supported by Japanese property price gains ultimately found its way into various assets and foreign property investments.

Trophy properties in the United States soon dominated the attention of the Japanese. New York's Rockefeller Center and the Exxon Building were two prized properties for which the Japanese paid handsomely. Japan's Mitsui Real Estate Company paid $625 million for the Exxon building on Sixth Avenue, well above Exxon's $310 million asking price, solely to be listed in the Guinness Book of World Records.[9] In 1990, a medium-sized Japanese company purchased America's most famous golf course, Pebble Beach, for $831 million. Hawaii also became a target for Japanese investor interest. Between 1985 and early 1991, Japanese investors purchased or financed the building of all but two of the main hotel resorts in Hawaii.[10] The Grand Hyatt Wailea Resort and Spa on Maui (which opened its doors in 1991) was built for a total cost of over $600 million, which equated to a per-room investment of over $760,000. According to Anthony Downs, former chairman of the Real Estate Research Corporation and former Senior Fellow at the Brookings Institution, the hotel needed to charge over $700 per room per night (in the 1990s) and maintain occupancy of at least 75% in order to just break even.

The Art Market Meets the Bubble Economy

As the Japanese infatuation with property was bubbling to ever-higher heights, their interest in art gained tremendous momentum. Edward Chancellor's description summarizes the phenomenon extraordinarily well: “In the 1980s, the combination of ambitious Western auctioneers, promoting art with every trick in the book, and Japanese speculators, their wallets swollen with bubble profits, created the most extravagant art market on record.”[11] Peter Watson, in his book From Manet to Manhattan: The Rise of the Modern Art Market, described the 1988–1990 period, driven primarily by Japanese buyers, as “the most sensational that the art world has ever seen.”[12]

The New York Times reported on the results of a Christie's art auction that took place in late March 1987 in London.[13] In the highest price ever paid for a painting, van Gogh's Still Life: Vase with Fifteen Sunflowers had been sold to an unidentified foreign buyer for $39.9 million, well above the previous auction record for a painting of $10.4 million for Andrea Mantegna's Adoration of the Magi. The foreign buyer was later identified as Yasuo Goto of Yasuda Fire & Marine, a Japanese insurance company.

On November 30, 1989, Tomonori Tsurumaki, a Japanese real estate developer, won an auction for Picasso's Pierrette's Wedding (Les Noces de Pierrette) that took place at the Paris auction house Drouot. Tsurumaki was bidding from the New Otani hotel in Tokyo while he was hosting a party launching his newest real estate development project—a $500 million auto racing resort to be called “Nippon Autopolis.” After winning the Picasso, Tsurumaki noted that “One highlight of Autopolis will be a museum featuring works by such famous artists as Monet, Renoir, Chagall, and Magritte—and now, of course, this world famous Picasso.”[14] The height of the Japanese art craze was reached when Ryoei Saito, chairman of Daishowa Paper Manufacturing, paid $82.5 million for van Gogh's Portrait of Dr. Gachet and over $78 million for Renoir's Le Moulin de la Galette in May 1990. He then proceeded to shock the art world by stating he would cremate the paintings along with his body upon his death.[15]

Sotheby's Stock as a Bubble Indicator?

Anyone who has witnessed a live auction in which bidding far exceeds pre-auction estimates or sets a new world-record price understands that there is something curious in the air—something electric, something indescribable, something magical. I believe that “something” is confidence, perhaps even overconfidence.

Consider the stock chart of Sotheby's, shown in the following figure, which has proven useful as a bubble indicator. A quick scan of the list of the world's most expensive paintings finds numerous chronological clusters: 1988–1990, 1997–1999, 2006–2007, 2011–2012 and 2015, and then again in 2017–2018. Not surprisingly, these clusters are associated with (relative) highs in the price of Sotheby's stock. New highs in Sotheby's stock price are an important indicator of overconfidence and bubbly conditions.

Sotheby's (BID) Stock Price, 1988–2018

Source: Yahoo! Finance.

At each of these times, confidence was running extremely high. In the late 1980s, for instance, Japanese art buyers domineered the market for high-end art and were responsible for numerous world-record prices. Sotheby's stock price peaked months before the Nikkei began a long decline. Likewise, the 1999 peak in Sotheby's stock price is associated with the (irrational?) exuberance that telegraphed the tech bust. Although the buyers were different, the dynamics in 2007 were not different. Beneficiaries of easy money (hedge fund and private equity executives, among others) bought art at world-record prices, driving Sotheby's stock price to new highs. Again, the stock's peak telegraphed the global financial crisis. The 2011–2013 peaks were driven by Chinese and emerging-markets buyers, possibly telegraphing a Chinese and emerging-markets slowdown. And most recently, Middle Eastern buyers have been setting new world-record prices, including $450 million paid for Salvador Mundi, a painting by Leonardo da Vinci, which sold at auction in 2017.

Some of my thinking about the usefulness of Sotheby's in predicting bubbly conditions was described in an article in The Atlantic by Derek Thomson, “The Art of Bubbles: How Sotheby's Predicts The World Economy.”[16] Since that piece was published, many have asked me to clarify why Sotheby's stock price seems to telegraph bubbly conditions.

I'll begin my answer with a question: might the relationship between Sotheby's stock price and bubbles merely be a coincidence? I personally do not think so, because in my eyes, Sotheby's is a leading indicator of leading indicators of leading indicators of confidence. Specifically, three layers of confidence stacked on each other can be seen in the firm's stock chart: (i) buyer confidence, (ii) appraiser confidence, and (iii) investor confidence. Unlike the actual prices of art—which may be a reflection of buyer confidence—Sotheby's stock price is less subject to the whims of individual buyers or the uniqueness factor associated with specific works of art.

Not surprisingly, those who set new world-record art prices tend to be those who have substantial personal resources—meaning they are usually very, very rich. In short, those who pay $100 million (or more!) for a painting usually have much more than $100 million in net worth; such buyers are likely to be billionaires with significant corporate interests. Because of this connection between art buyers and corporate leadership, art markets serve as a useful indicator of corporate and global confidence. If the CEOs of major companies begin to see clouds on the economic horizon (through their corporate capacity), they are likely to scale back on their personal art buying. The inflection point when executives convert from aggressive to reluctant bidders is very unlikely to result in world-record art prices. It should not be surprising, then, to note that corporate M&A activity had recent relative peaks in 1999–2000 and 2007. (There wasn't a new M&A peak in 2011 in terms of transaction value, due in some part to the extended nature of the global credit crunch.) Incidentally, the data for 2018 shows the volume and value of transactions at historically elevated levels. Time for caution?

Worldwide Mergers and Acquisitions, Transactions, and Value

Source: Institute for Mergers Acquisitions and Alliances, imaa-institute.org.

An additional layer of confidence is important to consider: auction house appraiser confidence embedded in pre-auction estimates. As new record prices are set, appraisers themselves begin increasing their estimates of what future sales should achieve. Imagine that you are an appraiser of Chinese artifacts, and a vase estimated to sell for between $800 and $1200 recently sold at auction for $18 million (true story!). Might you be inclined to raise your estimate of other Chinese artifacts? Higher prices yield higher estimates, which yield higher prices—until they don't.

Closely related to this appraiser confidence is management confidence. In the past, Sotheby's management grew so confident in their appraisers that they began guaranteeing prices to prospective sellers. The result was an increase in “inventory” owned by Sotheby's at precisely the time that the market for such art was cooling rapidly.

In addition to buyer confidence and appraiser confidence, a third form of confidence is captured by the price movements of Sotheby's: investor confidence. Because Sotheby's stock is itself an object of investor bidding, it is a manifestation of investor perceptions and analyst estimates. As auctions go well, analysts raise earnings estimates. Higher estimates make the stock appear less expensive, drawing investor interest. Relatedly, the investor relations function at Sotheby's exhibits variable confidence as the stock progresses and the underlying business results become obvious, thereby affecting analyst estimates.

At the time of this writing, Sotheby's stock price was telegraphing strong market (over)confidence. What should we make of it? Is it telling us that the Middle East may prove to be a new source of potential uncertainty and economic disruption? Should the fact that Saudi Arabia is spending gigantic sums on defense concern us? What about the attempt to transition the economy off of oil? Or the fact that the Kingdom is trying to sell its crown jewel (Aramco)? It seems to me that it would be prudent to take a more risk-averse stance vis-à-vis investing in the Saudi market, despite the global investor charm offensive that's currently underway.

At the end of the day, spotting bubbles is at best a probabilistic exercise and requires multiple confirmatory data points before we can say anything with conviction. Certainty is an elusive goal, but the use of multiple lenses can be very powerful in gaining an edge. Thus, to improve your performance in the art of spotting bubbles, focus on spotting bubbles of art.

The Stock Market Goes Vertical

Not surprisingly, Japanese investors found a receptive and fertile opportunity to speculate in the stock market. In attempting to summarize the magnitude of the stock market bubble, Krugman noted that “at the beginning of 1990, the market capitalization of Japan—the total value of all the stocks of all the nation's companies—was larger than that of the United States, which had twice Japan's population and more than twice its gross domestic product.”

Before describing the phenomenon in greater depth, a quick glance at Figure 8.1 demonstrates the tremendous boom the Japanese stock market experienced during the 1980s.

Figure 8.1 Japan's Nikkei 225 Index in the 1980s

Source: Bloomberg Finance L.P.

As is clear from the increasingly vertical move in share prices in the late 1980s in Japan, speculative juices were flowing rapidly. One stock that captures the spirit of the times perhaps better than any other is Nippon Telegraph and Telephone (NTT), the national telephone company. After being privatized in 1985 to encourage competition, NTT shares were floated to the public in several tranches. By November 1987, when the company did a further listing of shares valued at over $38 billion, the company had a total market valuation of approximately $300 billion. To provide some context for this figure, the New York Times suggested at the time that NTT was worth more than the stock markets of Switzerland and France combined.[17] Incidentally, Chancellor noted that NTT was worth more than the entire value of the West German and Hong Kong stock markets, combined.[18] Suffice it to say, NTT was a very highly valued company!

On top of this already-ebullient market, the fact that most Japanese companies owned land provided justification for highly priced shares as professional and amateur analysts alike highlighted the “hidden” real estate values that were not fully reflected in official financial statements. Such land and property valuation was commonplace. Chancellor notes, “even NTT was valued primarily for its land assets rather than as a telecommunications company. Propelled by its extensive landholdings, the market value of Tokyo Electric Power increased by a greater value than that of all the stocks listed on the Hong Kong Stock Exchange.”[19] Even airlines were considered land plays, with All Nippon Airways rising to 1200 times earnings and Japan Airlines at 400 times (it was thought to have less land).

To eliminate the noise originating from individual extremes, let's take a look at some of the valuations placed by the market on various sectors. Table 8.1 highlights the heights to which several sectors rose.

Table 8.1 Selected Sector Valuations

Source: Robert Zielinski and Nigel Holloway, Unequal Equities: Power and Risk in Japan's Stock Market (New York: McGraw Hill, 1992).

Sector

Average P/E

Textiles

103 x

Fishery & Forestry

319 x

Services

112 x

Marine Transportation

176 x

Bursting of the Bubble

In aggregate, the asset-price boom that took place in Japan during the late 1980s was perhaps the world's most spectacular (and ephemeral) wealth-creation event (up to that date).[20] As asset markets sprinted to unsustainable levels, Japanese authorities grew increasingly concerned and attempted to dampen speculative behavior. The Bank of Japan's incoming governor feared high housing prices might erode social harmony. (Separately, it is interesting to note that such social harmony was so highly valued by society that banks began designing products to allow such harmony to coexist with lofty valuations. One such product, according to Kindelberger and Aliber, was a 100-year, three-generation mortgage!) In 1989, the Bank of Japan issued new regulations limiting the growth of real estate loans to the growth rate of total loans and began raising rates. The Bank of Japan raised the interest rate from 2.5% in May 1989 to 6.0% in August 1990 over the course of five incremental policy actions—undoing the reduction of interest rates from 5% in January 1986 to 2.5% by February 1987.[21]

Figure 8.2, taken from Richard Koo's The Holy Grail of Macroeconomics: Lessons from Japan's Great Recession, visually demonstrates the magnitude of the correction in the stock market, the land market, and even the golf course market.

Figure 8.2 Bursting of the Bubble Economy

Source: Richard Koo, The Holy Grail of Macroeconomics (Singapore: John Wiley & Sons, 2009). Reprinted with permission of John Wiley & Sons, Inc.

Given the magnitude of the wealth created and the unsustainable heights to which asset prices had risen, the bursting of the bubble economy was inevitably going to have a massive financial and economic impact on corporations, investors, consumers, and, perhaps most importantly, the banks that had fueled the surge and accepted inflated assets as collateral. Kindelberger and Aliber eloquently summarize the bust:

Once the rate of growth of bank loans slowed, some recent buyers of real estate developed a cash bind; their rental income was still smaller than the interest payments on their mortgages, but they could no longer obtain the cash needed … from new loans. Some of these investors then became distress sellers. The combination of the sharp reduction in the rate of growth of credit for real estate and these distress sales caused real estate prices to decline; the cliché that the price of land always rises was tested and found to be false.

Stock prices and real estate prices began to decline at the beginning of 1990; stock prices declined by 30% in 1990 and an additional 30% in 1991. The stock price trend in Japan was downward … and at the beginning of 2003, stock prices in Japan were at the same level they had been 20 years earlier, even though the real economy was much larger …

Now the perpetual motion machine began to work in reverse. Property sales led to declines in property prices. The decline in real estate prices and stock prices meant that bank capital was declining; banks were now much more constrained in making loans … .

Bankruptcies increased, and the banks and other financial institutions incurred large loan losses. Those nonbank financial institutions that specialized in making real estate loans were in great distress.[22]

The Boombustology of the Japanese Boom and Bust

The Japanese boom and bust sequence exhibited many unique elements and took place in the most unlikely of locations—an administratively guided economy in which thrift and long-term thinking combined with market share prioritization over profits and the desire for social harmony at the expense of individual success. The application of the five lenses in this context will build the framework's relevance across the cultural spectrum and will help with the formulation of a generic framework through which to think about booms and busts.

The Reflexive Foundation of the Asset Boom: Land as Collateral

At the very root of the bubble economy was a tremendous boom and bust sequence in the property market. Land was, simply put, the foundation upon which the entire rickety system was built. By lowering rates to 2.5% in 1986, the Bank of Japan threw fuel on the already-burning speculative fire. Consider the reflexive dynamic eloquently described by Wood:

This sparked a liquidity boom to beat all others. At its center lay the economy's main engine of credit creation, the banks. They were able to use a rising stock market to literally create bank capital and thus boost their lending. That extra credit was funneled back into two main markets (shares and property), boosting the value of banks’ favored collateral (shares and property) against which to lend still more money.[23]

Further, the utter dominance of property as a source of collateral (some estimates indicate that real estate may have served as the backing for around 80% of all loans outstanding in Japan[24]) virtually assured that any dynamic that transpired (boom in the case of rising prices or bust in the case of falling prices) was surely going to tend away from any equilibrium condition that might theoretically exist.

Nevertheless, we might argue that the origin of the boom was a sound economic success story in which corporate Japan began growing profits as they took market share from global competitors. Over time, however, this initial fundamental development transformed into a reflexive, self-fulfilling and self-sustaining asset boom that ultimately exceeded its own capabilities and imploded upon itself.

The Effects of Macroeconomic Policy

Because of a savings-oriented culture and the Bank of Japan's decision to lower the official discount rate from 5% to 2.5% between January 1986 and February 1987, banks were flush with cash, and money was close to free. As noted in the previous section, this reduction of rates ignited a liquidity boom that fed upon itself in an unrivaled manner.

Another factor that proved to be quite important was the deregulation and liberalization of the financial sector, which enabled Japanese banks to increase the amount of loans guaranteed by property. Although this was initially a result of international lobbying to enable foreign banks to compete for Japanese business, the largest beneficiaries of the liberalization process were probably the Japanese banks.

A third factor was the Japanese yen. Because of external influences on the Japanese government to moderate the rate of Japanese currency appreciation, the Bank of Japan was active in the foreign exchange markets by constantly selling yen. Although the direct result of these efforts was a slower appreciation in the yen, the indirect result of it was a flooding of the system with yen. According to Kindelberger and Aliber:

The result of extensive intervention was that money supply in Japan began to increase at an exceptional rate—that is, the monetary base was increasing. The increase in reserves of the Japanese banks meant that they were able to increase their loans at a rapid rate.[25]

Was the Japanese government's desire to control the currency the ultimate reason for many of these boom–bust oriented policies? It is not clear, but it does seem that stopping and/or slowing a rising yen, and the pressure that put on export industries in the country, was the target of policy efforts. Might the government have sought to induce speculation in a quest to get asset markets to support real economic activity?

The following quote, attributed to an unnamed executive at the Bank of Japan, suggests that asset price inflation may in fact have been the Bank of Japan's goal:

We intended first to boost the stock and property markets. Supported by this safety net—rising markets—export-oriented industries were supposed to reshape themselves so they could adapt to a domestic-led economy. This step was then supposed to bring about an enormous growth of assets over every economic sector. This wealth effect would in turn touch off personal consumption and residential investment, followed by an increase of investment in plant and equipment. In the end, loosened monetary policy would boost real economic growth.[26]

The Psychology of Conformity

In discussing the observation made by Charles Mackay in Extraordinary Popular Delusions and the Madness of Crowds that “men think in herds, go mad in herds, but only recover their senses one by one,”[27] Wood notes that “as a group culture that discourages individualistic thinking, the Japanese are even more vulnerable than most to this decidedly human trait.”[28] The cultural values that dominated pre-1980s Japan were uniquely communal in that social harmony was valued more highly than individual happiness. In a society focused on conformity and not being different, the mere introduction of the slightest differences can be quite destabilizing.

An analogy with preschool appears apt here. My daughter went to a Montessori school in Boston when she was younger. Virtually all the kids in her class (they're mostly 3-, 4-, and 5-year-olds) would always default to “go with the flow.” With the exception of the random tantrum that inevitably arose among children of this age, the group was quite harmonious. However, there are some key moments during the day when an individual opinion snowballs rapidly into group thinking. Arriving one afternoon shortly before snack time to pick up my daughter, I patiently (and covertly) observed the classroom dynamic. The teacher was about to put out some fruit and crackers when one young boy yelled, “I don't like crackers.” In an almost comical sequence of events that left the teacher confused, probably frustrated, and definitely busy, all the other kids started agreeing. Nobody wanted crackers, despite the fact that I had seen virtually all of the kids voraciously eating crackers on other afternoons. It was as if a domino had fallen, knocking over all other opinions.

Not unlike this pre-primary classroom, Japan—a consensus-oriented, thrifty society focused on long-term economic development—found itself rapidly pursuing conspicuous consumption (in the form of art and trophy properties) and short-term individual gain despite harmful long-term implications.

Just as this harmonious society found itself drifting rapidly toward overconfidence and economic hubris during the boom phase of the cycle, so too did the bust phase of the cycle lead to a consensus of dejection, underconfidence, and utter despair. In referencing the dishonor that spread among Japan's financial services employees during the 1990s and onward, Wood notes that “this demoralization matters because it can become self-feeding. In a consensus society where there are few contrarians, yesterday's collective euphoria can too easily degenerate into tomorrow's collective panic.”[29]

As this collectivist, consensus-oriented approach to life combined with the extraordinary confidence that emerged as a result of Japan's postwar economic success, textbook versions of cognitive biases soon emerged and were clearly recognizable. Might a society that had experienced great economic success (such success that books such as Japan as Number One were published) develop a collective sense of overconfidence? Would the availability bias lead to the extrapolation of most recent trends (land prices have been rising for a long time) into future projections (land prices will continue rising for a long time)? Might the public stories of grandiose global accomplishment such as the purchase of Rockefeller Center or prized Picasso paintings be seen as representative of Japanese society's new global economic status more than as examples of conspicuous consumption? In thinking about contractions, might anchoring and insufficient adjustment lead to the belief that a multiyear asset-price deflation was particularly unlikely?

Political and Regulatory Conditions

Although the number of political and regulatory factors that influenced the boom and subsequent bust are too numerous to fully describe here, several policies seem vital to understanding the bubble economy. Given that the root cause of the bubble economy was an unsustainable, credit-fueled rise in property prices, this section will focus on two main types of political and regulatory considerations that directly affected property prices: tax policies and financial deregulation and credit controls.

In terms of tax policies, two taxes likely influenced the incentives to own property. The first tax, which might be deemed penal property taxation, was designed to discourage short-term trading of properties. The policy was straightforward, albeit very distortionary: “If land is sold within two years of its purchase, then 150% of the capital gain is added to the seller's annual income and taxed accordingly. If sold within five years, then 100% of the gain is added to income and taxed.”[30] What effect might this have on purchasers who owned real estate that appreciated rapidly in the first five years of ownership? What might such taxes do to the trading volume and liquidity of these assets? By distorting the supply of housing that was available for purchase each year, these policies created a false sense of scarcity, resulting in rapidly rising prices. Further, this impact might also have created “house money” effects in which owners were not as disturbed or concerned about initial housing losses.

Another Japanese tax that seemed to affect real estate prices by artificially increasing the demand for property was the inheritance tax. The tax had a marginal rate structure in which mortgages were fully deductible from assessed property values, and property assessments (for tax purposes) were often well below market values (as was the case in much of America at various points in time). Thus, it was possible to create negative asset value by purchasing a property with significant leverage. This negative value could offset other positive asset values and thereby reduce the tax burden. Citing this as a “well-known bequest strategy,” Takatoshi Ito described the process as follows: “Those who were planning a bequest to their heirs were alarmed as their real estate values went up. In order to avoid high taxes, they purchased more real estate with high leverage, so that they could lessen the bequest tax burden. The higher prices generated more demand … [which may] have created an upward spiral in prices.”[31]

Financial deregulation might also have contributed to the upward property-price spiral and its subsequent reversal. Initially due to pressure to open up the banking sector to foreign competition (the Americans wanted the ability to compete for business in Tokyo on terms comparable to the Japanese ability to compete for business in New York), Japanese financial deregulation was ultimately about decreasing administrative guidance.

As noted by Kindelberger and Aliber, “interest rate ceilings on deposits and loans were raised. Window guidance became much less extensive. The restrictions on the foreign investments of Japanese firms were relaxed.”[32] The overarching philosophy is best summarized as follows: “Traditional banks were safe, but also very conservative; arguably, they failed to direct capital to its most productive uses. The cure, argued reformers, was both more freedom and more competition: let banks lend where they thought best, and allow more players to compete for public savings.”[33] As a result of this deregulation wave, banks began increasing the amount of money they lent against property.

Eventually, the rapid rise in asset prices caught the attention of policy makers. In an effort to deflate the bubble, the government began reversing some of these pro-competition policies. Credit policies were reconsidered, with the idea that slowing access to property financing might defuse the rising inequality resulting from skyrocketing prices. The government implemented credit controls in April 1990, stipulating that any increase in bank lending for property must be smaller than the increase in a bank's overall loan book. Given the high percentage of lending that had been collateralized using property prior to this mandate, credit effectively stopped flowing toward property. Deregulation of the bond market also ensured that this mandate had teeth, as corporate nonproperty borrowing (a very traditional role for Japanese banks) was slowly shifting away from banks to the bond market, a trend that accelerated concomitantly with financial deregulation.[34] The market rapidly swung from “an illiquid market where no one wants to sell (the traditional condition of this land-worshipping society) to an equally illiquid market where no one wants to buy.”[35]

Housewives, Preschool, and Pollock

The two lenses of Chapter 5 are quite powerful in evaluating the Japanese bubble economy. The epidemic model provides a view of the bubble economy's maturity and the relative proximity of a bust. The Far Eastern Economic Review noted in 1988 that “stocks have become a national street-level preoccupation,” and near the top of the best-seller list was a Japanese comic book about the economy and the stock market.[36] By the late 1980s, more than 22 million people were investing in the stock market, up from around 13–14 million in the mid 1980s. Nomura Securities, the largest of the domestic brokerage firms, had more than 5 million customers, mainly Japanese housewives, who regularly invested with Nomura salesmen. Speculation was encouraged, and through broker “guidance,” more than a third of stocks held in private accounts were held in margin accounts.[37]

Given the high proportion of the infectable population that appeared infected, an imminent slowdown of infections seemed likely. In fact, the rapid acceleration in the number of brokerage accounts opened by individual investors was a spectacular early warning indicator of the beginning of the end of the bubble economy.

Any consensus-oriented collection of individuals is highly prone—like the bees in a swarm—to the silent leadership of a seemingly informed individual. Consider the earlier preschool example, in which the establishment of the “we don't like crackers” conclusion in the classroom is not dissimilar to the movement of ants described in Chapter 5. The investment climate in Japan through the middle of the 1980s was a stable equilibrium with everyone “tied,” in an opinion sense, to everyone else. Social harmony, conformity, and consensus ruled the day. For whatever reason, once the balance tipped[38] toward the pursuit of immediate individual gain, the whole swarm of formerly long-term-oriented social beings became speculators. The cohesive power of communal thinking that had stabilized Japanese society for so long was now creating a speculative frenzy. The result was a spectacular manifestation of herd behavior in speculation, despite the view that gambling was a Chinese vice from which the Japanese were immune:

The Japanese were particularly susceptible to the lure of the stock market … [because] they have a tendency to exhibit herdlike behavior when pursuing a certain activity, whether at work or play. This was said to stem from the communal demands of rice farming, which had fostered a national shudankizoku ishiki (group consciousness). During the war, Japan was portrayed in government propaganda as “one hundred million hearts beating as one.” After the October crash, the president of a securities house boasted that Japan had survived the period of volatility because it was “a consensus society—a nation that likes to move in one direction.”[39]

Being a consensus society is definitely a two-edged sword, for if consensus were organized around a stable equilibrium, all would be well. However, if the accepted perspective was one of disequilibrium, short-termism, or speculative behavior, then instability would dominate and the former stability would evaporate.

The Multilens Look

As the discussion has demonstrated, the five lenses presented in Part I were able to shed light on the bubble economy of Japan in the late 1980s and the subsequent bust during the 1990s in a manner not possible with the use of only one lens. Table 8.2 summarizes the five-lens approach to thinking about the “Bubble Economy.” The next chapter utilizes the fives lenses to evaluate the Asian financial crisis that emerged in the mid 1990s and the contagion effects it had on the rest of the world.

Table 8.2 The Five-Lens Approach to the Japanese Boom and Bust

Lens

Notes

Microeconomic

Reflexive credit/collateral dynamic.

Higher prices induced buyers.

Lower prices induced sellers.

Macroeconomic

Inappropriately cheap money.

Financial innovation (100-year mortgages).

Psychology

New-era thinking (economic power).

Conformity-driven social harmony.

Conspicuous consumption (trophy art).

Economic overconfidence.

Politics

Supply/demand distortions (penal property taxation).

Deregulation of the banking industry.

Credit regulations that distorted incentives.

Biology

Amateur investors (housewives).

Silent Leadership (communal philosophies).

Popular Media (Japan as Number One ).

Notes

1. Edward Chancellor, Devil Take the Hindmost: A History of Financial Speculation (New York: Plume, 1999).

2. The author concedes that this fact may be changing as this book is written.

3. Japan had remained a feudal society with no individual rights until the mid-1800s. The entire economy was closed off to the outside world, and virtually everything was hierarchically controlled.

4. Paul Krugman, The Return of Depression Economics and the Crisis of 2008 (New York: WW Norton, 2009).

5. Paul Krugman, The Age of Diminished Expectations (Cambridge, MA: MIT Press, 1997).

6. Perhaps the best account of the spectacular excess that characterized Japan in the 1980s is found in a book written by a financial journalist at The Economist named Christopher Wood. His book, The Bubble Economy: Japan's Extraordinary Speculative Boom of the ‘80s and the Dramatic Bust of the ‘90s was written in 1993 and accurately predicted much of what transpired during the time since it was written.

7. Wood, The Bubble Economy.

8. Ibid.

9. Charles P. Kindelberger and Robert Aliber, Mania, Panics, and Crashes: A History of Financial Crises (Hoboken, NJ: John Wiley & Sons, 2005).

10. Wood, The Bubble Economy.

11. Chancellor, Devil Take the Hindmost.

12. Peter Watson, From Manet to Manhattan: The Rise of the Modern Art Market (New York: Random House, 1992).

13. Francis Clines, “Van Gogh Auction Sets Record: $39.9 Million,” The New York Times (March 31, 1987).

14. Rita Reif, “Japanese Developer Buys Picasso at Record Price,” The New York Times (December 1, 1989).

15. David Usborne, “Missing Van Gogh Feared Cremated with Its Owner,” The Independent (July 27, 1999).

16. Derek Thompson, “The Art of Bubbles: How Sotheby's Predicts The World Economy,” The Atlantic (April 5, 2011).

17. Vartanig Vartan, “Big Stock Sale by Japanese,” The New York Times (November 9, 1987).

18. Chancellor, Devil Take the Hindmost.

19. Ibid.

20. Later episodes (including the internet boom) rivaled it, but at the time, the Japanese boom might have been the greatest one on record.

21. Takatoshi Ito, “Retrospective on the Bubble Period and Its Relationship to Developments in the 1990s,” in Gary Saxonhouse and Robert Stern, Japan's Lost Decade: Origins, Consequences, and Prospects for Recovery (Malden, MA: Blackwell Publishing, 2004).

22. Kindelberger and Aliber, Mania, Panics, and Crashes.

23. Wood, The Bubble Economy.

24. Ibid.

25. Kindelberger and Aliber, Mania, Panics, and Crashes.

26. Cited by Tomohiko Taniguchi, “Japan's Banks and the Bubble Economy of the Late 1980s” (Princeton University, Center for International Studies, working paper no. 4, Program on U.S. Japan Relations, 1993).

27. Charles Mackay, Extraordinary Popular Delusions and the Madness of Crowds (New York: John Wiley & Sons, 1996).

28. Wood, The Bubble Economy.

29. Ibid.

30. Ibid.

31. Ito, “Retrospective on the Bubble Period.”

32. Kindelberger and Aliber, Mania, Panics, and Crashes.

33. Krugman, The Return of Depression Economics.

34. Mitsuhiro Fukao, “Japan's Lost Decade and Its Financial System,” in Saxonhouse and Stern, Japan's Lost Decade.

35. Wood, The Bubble Economy.

36. Chancellor, Devil Take the Hindmost.

37. Ibid.

38. Malcolm Gladwell, The Tipping Point: How Little Things Can Make a Big Difference (New York: Little Brown & Company, 2000).

39. Chancellor, Devil Take the Hindmost.

CHAPTER 9

The Asian Financial Crisis: THE MIRAGE OF A MIRACLE

The great Asian slump is one for the record books. Never in the course of economic events—not even in the early years of the Depression—has so large a part of the world economy experienced so devastating a fall from grace.

—Paul Krugman

In many ways, the seeds of economic success in Asia were sown in the early aftermath of World War II. In an effort to rebuild societies and generate long-term economic growth, many Asian countries adopted export-oriented development policies designed to utilize their abundant (and therefore cheap) labor to meet global demands. Combined with increased opportunities for trade, the economic strategies caught a massive tailwind in the early 1990s—and asset markets took notice, rising to lofty heights. The resulting bubble eventually collapsed in 1997 and 1998, with the ripple effects being felt around the world.

Boom Times in East Asia

The early to mid-1990s were a spectacular time for many East Asian economies (Japan, as we have just learned, was an exception). A 1993 book titled the East Asian Miracle: Economic Growth and Public Policy, began with this summary:

East Asia has a record of high and sustained economic growth. From 1965 to 1990 the twenty-three economies of East Asia grew faster than all other regions of the world. Most of this achievement is attributable to seemingly miraculous growth in just eight economies: Japan; the “four Tigers”—Hong Kong, the Republic of Korea, Singapore, and Taiwan, China; and the three newly industrializing economies of Southeast Asia—Indonesia, Malaysia, and Thailand.[1]

Against this backdrop of economic performance, it is not surprising that booms might develop across the region's asset markets. According to Robert Barbara, “the booms were initially sensible, reflecting sound investment opportunities. The dynamics were straightforward. The collapse of the former Soviet Union and China's newfound willingness to interact with capitalist nations supercharged trade and capital flows between the developed world and emerging Asian economies.”[2]

Rapid growth was taking place throughout the entire region, albeit with slightly nuanced and different driving factors in each country. South Korea, which had been devastated by the war, embarked on a remarkable period of economic growth that utilized high savings rates, cheap labor, an inexpensive currency, and strong industrial policy to produce products needed by global consumers and corporations. Singapore, which was literally a swamp in the 1950s, embarked on a strategy to become a corruption free, rule-of-law oriented outpost in a land of crony capitalists and, in so doing, elevated itself to first-world status.

Deng Xiaoping's rise to power in China resulted in an economic revival that opened the country up to international trade and global investment, as well as an economic reform agenda more typically found in capitalist nations. For many years following this policy shift, China grew its GDP in excess of 10% per year. Hong Kong transformed into a regional, if not global, financial center. Manufacturing activity surged in Thailand, Indonesia, and Malaysia as globalization took hold, trade barriers were lowered, and protectionism retreated.

Many stock markets in the region rose at rapid rates. In 1993, for instance, many East Asian countries saw their stock markets double in value, with growth continuing into 1994. Figure 9.1 graphs the performance of the Philippine, Thai, Malaysian, and Indonesian markets in 1993. Real estate prices rose throughout the region as the optimistic outlook combined with inexpensive capital to create significant demand. In sum, Asian economies and asset markets boomed.

Figure 9.1 The Performance of Selected Asian Stock Markets in 1993

Source: Bloomberg Finance L.P.

In some senses, what transpired was simple: abundant capital combined with cheap labor to produce competitive economies. The developed world (or, more precisely, the rich world) got excited about these prospects and began pouring money (primarily dollars) into the East Asian markets. As a result, many Asian currencies appreciated quite rapidly, and banks and local corporations borrowed significant sums of U.S. dollars (i.e. not local currency). Kindelberger and Aliber note the importance of the interconnections that began to form between East Asian countries and the developed world:

China, Thailand, and the other East Asian countries were on the receiving end of outsourcing by American, Japanese, and European firms that wanted cheaper sources of supply for established domestic markets. Rapid economic growth was both the result and cause of the inflow of foreign capital, especially from Japan. Japanese investment initially took the form of construction of manufacturing plants to take advantage of lower labor costs … from there a large part of the production would be exported, some to the United States, some to Japan, and some to third countries … . The buzzword was export-led growth, which was almost always based on a low value for the countries’ currency in the foreign exchange market.[3]

The system fed upon itself, and soon Americans were outsourcing jobs to Korea; and in turn, the Koreas were outsourcing jobs to China and Indonesia, where labor was even cheaper. In many ways, the whole chain was based on two primary enabling factors in the production country: inexpensive labor and cheap currencies. As part of this “game,” countries sought to constantly keep their currencies undervalued.

In fact, on January 1, 1994, the Chinese government effectively devalued their currency relative to the U.S. dollar by ~50%. Might this action have provided a tremendous boost to Chinese exporters (at the expense of other Southeast Asian exporters)? It's possible that the 1994 Chinese devaluation may have been one of the primary catalysts for the Asian financial crisis.

“Ground zero” in this Asian export-oriented game was Thailand. It typified everything about the boom and also served as the catalyst for the regional bust. Let us now turn to Thailand to understand what occurred within it and how it illustrates the boom dynamics of the region.

Thailand Catches the Flu

On the surface, it seems odd that a country like Thailand could be responsible for a global economic meltdown that resulted in debt defaults in Russia, the collapse of perhaps the world's largest hedge fund, and economic and currency contractions to rival the largest in history. In The Return of Depression Economics, Paul Krugman explains:

The world economy is almost inconceivably huge, and in the commercial scheme of things, Thailand is pretty marginal. Despite rapid growth in the 1980s and 1990s, it is still a poor country; all those people have a combined purchasing power no greater than that of the population of Massachusetts. One might have thought that Thai economic affairs, unlike those of an economic behemoth like Japan, were of interest only to the Thais, their immediate neighbors, and those businesses with a direct financial stake in the country. But the 1997 devaluation of Thailand's currency, the baht, triggered a financial avalanche that buried much of Asia.[4]

It was the devaluation of the Thai baht that eventually snowballed into a global mess; but before the bust, there had been a great boom. From 1985 to 1996, Prime Minister Prem Tinsulanonda enacted policies that opened the doors of the economy to the outside world. During this time, Thailand was one of the world's fastest-growing economies and averaged annual GDP growth of 9.4%. Cheap labor, fiscal conservatism, and natural resources formed a powerful cocktail that modernized the formerly agricultural-dominated economy into a manufacturing-led, export-oriented powerhouse.

Foreign capital came to Thailand in droves in the early 1990s, and with its arrival, the country's financial self-sufficiency began to rapidly erode. Although it had basically self-funded its growth from domestic savings through the early 1990s,[5] Thailand grew increasingly dependent on foreign capital, most of which was being lent in U.S. dollars.[6]

Multiple factors contributed to the rapid inflow of capital into Thailand during the early to mid-1990s, including several external factors. To begin, the resolution of the Latin American debt crises and the fall of the Soviet Union made investing in riskier places in the world more fashionable. Second, the sharp drop of interest rates in the developed world drove investors on a global search for better yields. Third, development agencies like the International Monetary Fund and the World Bank began rapidly increasing their funding into emerging Asian countries like Thailand. And finally, there was the rapid growth of emerging-markets funds (due in no small part to the name change—see “‘Third World’ Becomes ‘Emerging Markets’”) that began allocating capital in a diversified manner to developing countries like Thailand, Malaysia, and Indonesia.

As foreign inflows began coming into Thailand in greater and greater volumes, the stock market rose simultaneously. Might this have been a sign that capital was being recycled into speculative investing in shares? Krugman describes the phenomenon as follows:

“Third World” Becomes “Emerging Markets”

We often hear of countries such as China and Brazil referred to as emerging markets. This was not always the case. In perhaps one of the greatest marketing coups of all time, former investment banker Antoine van Agtmael managed to rename the less-developed countries of the world the “emerging” markets. He did so after struggling to raise a diversified fund to invest in what were then known as third-world countries. Van Agtmael recounts the experience he had after a prospective client (Francis Finlay of JP Morgan) said to him, “This is a very interesting idea … but you will never sell it using the name ‘Third World Equity Fund’!”

I immediately knew he had a point. We had the goods. We had the data. We had the countries. We had the companies. What we did not have, however, was an elevator pitch that liberated these developing economies from the stigma of being labeled as “Third World” basket cases, an image rife with negative associations of flimsy polyester, cheap toys, rampant corruption, Soviet style tractors, and flooded rice paddies. Over the weekend, I disappeared into one of the mental isolation spells that my wife and children so heartily dislike, but which I often find oddly productive. Racking my brain, I at last came up with a term that sounded more positive and invigorating: Emerging Markets. “Third World” suggested stagnation. “Emerging Markets” suggested progress, uplift, and dynamism.

Van Agtmael went on to found a firm (Emerging Markets Management) focused exclusively on investing in emerging markets.

Source: Antoine van Agtmael, The Emerging Markets Century: How a New Breed of World-Class Companies Is Overtaking the World (New York: The Free Press, 2007).

As more and more loans poured in from abroad, then, the result was a massive expansion of credit, which fueled a wave of new investment. Some of this took the form of actual construction, mainly office and apartment buildings, but there was a lot of pure speculation too, mainly in real estate, but also in stocks.[7]

As the credit-fueled boom continued, non-bank finance companies sprung up everywhere. These institutions were usually controlled by a relative of a government official and were believed to have implicit government guarantees—enabling them to raise money at advantageous rates from respected banks and foreign lenders. These finance companies could then relend the capital to riskier projects or speculative ventures at higher rates to capture the spread. The implication of these relationships is that the government would backstop any losses, but that gains would accrue only to the finance company. Such crony capitalism, as this system was later named, was widespread in Asia. Some have argued that this crony capitalism was in fact a very rational way of doing business as it enabled transactions in the absence of strong contract law.[8]

In the winter of 1996, against this backdrop of inefficient capital allocation via relationship lending practices, a dramatic concern emerged that began to spook foreign investors. Consumer finance companies began reporting large losses. Might this have been a manifestation of the 1994 Chinese devaluation that decreased the competitiveness of Thai exports (relative to Chinese exports)? Many of these companies had been set up by large domestic banks to circumvent regulations that prevented them from growing their consumer lending practices as rapidly as they would like. Foreign inflows began to slow and eventually reversed.[9] Although capital inflows into the emerging Asian countries had been approximately $93 billion in 1996, by 1997 that number had turned into an outflow of approximately $12 billion.[10]

Eventually, the outflow of currency created downward pressure on the currency, something the government sought to prevent. On July 2, 1997, after expending significant reserves in an attempt to defend the currency, the Thai baht was allowed to depreciate and moved from a price of 25 baht per U.S. dollar to more than 55 baht per dollar in early 1998. Such a currency move could have a devastating effect on those with misalignment between their earning currency and their borrowing currency; to illustrate the point, consider the following hypothetical example.

Mr. T is a Thai businessman who decides to borrow $10 million to expand his business. He doesn't need dollars, but because the rate to borrow them is cheaper and the bank is willing to lend to him at a good rate, he takes the $10 million loan and converts it to Thai baht. At the time, 25 baht = 1 US$, so he received 250 million baht for his loan. He is not concerned about the loan because his business is healthy (but his earnings are all in baht). His business continues to grow, and Mr. T uses the cash flow to reinvest in the business rather than to repay the loan. Then disaster strikes. For simplicity of math, let's say that the baht is now worth 50 baht = 1 US$. Now, in order to repay the $10 million loan, Mr. T must come up with 500 million baht. The loan value doubled in local currency terms, effectively bankrupting Mr. T.

This is exactly what happened to thousands of individual entrepreneurs, banks, and big businesses in Thailand during the Asian financial crisis. Because foreign capital sources feared that they might not get paid back, they all retrenched and began recalling capital whenever possible, thereby creating an effective bank run on Thailand. Everyone wanted their money back at the same time, creating a self-fulfilling vicious cycle of selling assets at depressed values to repay loans, but also further depressing values in the process. The largest Thai finance company, Finance One, which was worth over $5.5 billion at one point, collapsed completely.[11]

To complicate the situation, in order to prevent the currency from continuing to fall, Thai authorities began to sharply raise interest rates to attract capital. Although the strategy worked to stabilize the currency (the baht was trading at ~36 baht = 1 U.S. dollar by the end of 1998), it increased the cost of doing business and slowed the economy so dramatically it entered a recession, with GDP shrinking by approximately 2.2% in 1998. Needless to say, confidence in the Thai economy was shattered. This loss of confidence led to less economic activity, which fulfilled fears of a slowing economy, which hurt companies, banks, and consumers, which led to lower confidence. The feedback loop was nasty.

But the question remains: how and why did the problems in Thailand create a financial tsunami that engulfed so many other, seemingly unrelated countries and organizations? Although Thailand's trading partners would be hurt because of the economic slowdown in Thailand, an important new mechanism through which Thailand's contagious disease spread were the same emerging markets funds that had enabled its boom. When bad news and financial losses came in from Thailand, fund managers needed to reduce holdings throughout the region to meet redemption requests from investors. Regardless of how countries may have differed, they were linked in the portfolios of these fund managers and hence were all vulnerable to self-validating panics.

To illustrate this concept in action, consider the following simplified scenario. A fund manager has chosen to invest in five countries and has spread his money equally between them (20% of the portfolio to each). Because of economic hardships in Country A, the stock market of Country A has fallen 50%. Now, instead of having 20% in each of five countries, our fund manager has 11.1% in Country A and 22.2% of his portfolio in each of the other countries. Assuming he wants to return to his prior country weightings, he must sell shares of companies in Country B, Country C, Country D, and Country E—despite the fact that these countries have not had the same economic difficulties as Country A. This contagion effect is further complicated by the risk that our fund manager faces investor redemptions, in which case he might indiscriminately be selling all countries. Thus, even though the spark started in Country A, the flames might eventually engulf all five countries.

Through such capital market linkages, the panic spread from Thailand and eventually engulfed most of Asia, Russia, most other emerging markets, and the famed U.S. hedge fund Long-Term Capital Management. To appreciate the magnitude of the capital markets impact, particularly when combined with currency impacts, consider Figure 9.2, which illustrates the performance of the Jakarta Stock Exchange Composite Index from January 1, 1995 through December 31, 1999 in U.S. dollar and Indonesian rupiah terms. Note that the U.S. dollar–denominated chart reflects what most emerging-markets managers experienced. Peak to trough declines during this period were 65% in local currency terms and 93% in U.S. dollar terms. Over the 5-year period displayed in the chart, the index actually rose 44% in Indonesian rupiah terms (granted, the currency fell a great deal) but fell more than 55% in U.S. dollar terms.

Figure 9.2 Indonesian Stock Market, 1995–1999

Source: Bloomberg Finance L.P.

The Boombustology of the Asian Financial Crisis

Given the complexity of the East Asian financial crisis in terms of the number of countries, governments, nongovernmental organizations, and currencies involved, it is impossible in several pages to utilize the five lenses of Part I in a comprehensive manner. Instead, this section will highlight a handful of illustrative issues.

The Reflexivity of Confidence

As the rapid transmission of bank losses into a reversal of foreign fund flows and a self-validating panic demonstrated, the events in Asia during 1997 and 1998 were highly reflexive. They were not self-correcting in the traditional sense of efficiency. The primary cause of this reflexivity was the use of borrowed money that was collateralized by assets—assets which had their values inflated by the excess purchasing power generated through borrowed money. Because these are not self-correcting dynamics, they become quite disruptive and tend toward disequilibria.

The dynamics leading to disequilibrium can be summarized as a self-reinforcing, self-validating reflexive feedback loop that connects what I label the five Cs—confidence, collateral, credit, conditions, and capital, as shown in Figure 9.3. During the boom phase of the cycle, confidence inspires credit, and credit improves collateral values, which generates confidence and better economic conditions via increased activity. The improved conditions drive more confidence and attract capital, which results in greater availability of credit. The greater availability of credit broadens the universe of acceptable collateral, which generates better conditions, and on it goes.

Figure 9.3 The Five Cs

Unfortunately, the world discovered that these highly interconnected and reinforcing five Cs can also work in reverse. So, during the bust phase of the cycle, reduced confidence led to less credit, and credit contraction hurt collateral values, which hurt confidence and conditions. Deteriorating conditions further hurt confidence, which caused capital to flee, while further reducing access to credit. The contraction in credit tightened collateral standards, which further hurt conditions, and so on.

A classic example of a positive feedback loop and the role of signal strength can be found with a microphone in an auditorium. If the signal is too strong, then the speakers will generate noise sufficiently loud that the microphone will pick it up, with the speakers amplifying that feedback even more, generating even greater feedback, and so on. If the signal is dampened by other noises or interference of any sort, then the signal strength does not multiply. If, however, the feedback loop combines with a strong signal, the result is that utterly offensive nails-on-a-chalkboard, toe-curling screeching noise.

If confidence is the root of our signal, then the shock to our confidence is what tips the balance and turns our virtuous cycle into a vicious one. How intensely might Mr. T's confidence have been shaken when he woke up one day and found he was effectively bankrupt? Quite seriously, I assume. How intensely might the foreign bank's confidence have been shaken when it learns that it lent money out to borrowers that suddenly appear highly unlikely to repay, even though last month those very same loans appeared quite safe? Again, we can only assume quite severely. Thus, the highly iterative and self-reinforcing loop is very reflexive and can lead the system to extremes of instability, rather than any resemblance of a calm equilibrium.

Miracle or Myth?

In an aptly timed essay published in Foreign Affairs at the end of 1994, Paul Krugman warned that Asia's economic success was more likely to be a mirage than a miracle. His essay, “The Myth of Asia's Miracle,” began by describing the supposed economic success story of the Soviet Union. He describes how the idea of central planning and the prioritization of collective objectives over individual pursuits was considered by some as a better alternative to Western market-dominated individualism. Of course, his writing of such thoughts in 1994 after the Soviet Union had imploded upon itself was intentional.[12]

His example provided a powerful reminder that growth by itself is meaningless, but rather, we need to consider the sources of economic growth and their sustainability. The Soviet Union, claimed Krugman, was able to outgrow the United States in the 1950s and 1960s not because it had a more sustainable system, but rather because it had been very efficient at mobilizing inputs. Productivity, he noted, holds the key to long-term sustainable growth.

In order to fully appreciate Krugman's argument and its applicability to the East Asian story, we need to understand the basics of growth accounting. The term was first introduced by MIT professor Robert Solow in a 1957 paper published in the Review of Economics and Statistics.[13] The basic framework suggested that there are two primary sources of growth: a change in inputs and a change in the productivity of those inputs. Inputs can further be broken down into capital and labor. Isolating each of these three variables (capital, labor, and total productivity) helps to understand how each affects economic growth.

Suppose for a moment that a country has 100 people and only one revenue-generating activity: picking apples. Fifteen of the people are too old to work, and 15 are too young, so the working population is 70 people. Suppose on any given day, only 30 of those 70 are actually working. One very obvious way to grow the output of the economy is to have some of the 40 idle workers begin working. If the average worker can pick 1,200 apples per year, then our initial output is 36,000 apples. Now after adding additional workers (suppose another three workers join the effort), our production will grow to 39,600 apples, representing 10% growth. In this case, because the input of labor grew by 10% and output grew by 10%, we can say 100% of our growth is accounted for by labor inputs.

Now suppose we are able instead to purchase some machines (cost = 1,200 apples per machine, paid for 100% from savings) that help us gather more apples per worker. The machine (a handheld apple-picking arm extender that eliminates the need for climbing trees) is able to help each worker pick 1,320 apples per year. Assuming that we have added no new workers, but that each worker now has the machine, annual production will rise to 39,600 apples—exactly 10% higher than previously. But how can we account for this new growth, since it comes at a cost of the machines? One method is to weight the cost of the machine by the return it generates and consider that our capital investment. In this case, the machine costs 1,200 apples and generates 120 apples per year of additional production. Capital thus produced a 10% return. In this example, because capital grew by 10% and output grew by 10%, we can say that 100% of our growth is accounted for by capital inputs.

Finally, let us consider a third scenario in which our 30 workers get neither additional colleagues in the fields nor new equipment to help them. Instead, they are able to grow output by simply increasing their productivity. In this case, 100% of the growth in output can be accounted for by efficiency gains.

Because neither capital nor labor inputs can be grown indefinitely, growth from inputs is ultimately unsustainable. Krugman argued in 1994 that the supposedly miraculous growth rates of Asia were destined to fall. They were unsustainable because they were based on growth of inputs. He highlights the case of Singapore:

Between 1966 and 1990, the Singaporean economy grew at a remarkable 8.5% per annum, three times as fast as the United States; per capita income grew at 6.6%, roughly doubling every decade. This achievement appears to be some kind of economic miracle. But the miracle turns out to have been based on perspiration rather than inspiration: Singapore grew through a mobilization of resources that would have done Stalin proud. The employed share of the population surged from 27 to 51 percent. The educational standards of that work force were dramatically upgraded: while in 1966 more than half the workers had no formal education at all, by 1990 two-thirds had completed secondary education. Above all, the country had made an awesome investment in physical capital: investment as a share of output rose from 11% to more than 40%…

Singapore's growth has been based largely on one-time changes in behavior that cannot be repeated. Over the past generation the percentage of people employed has almost doubled; it cannot double again. A half-educated work force has been replaced by one in which the bulk of workers has high school diplomas; it is unlikely that a generation from now most Singaporeans will have PhDs. And an investment share of 40% is amazingly high by any standard; a share of 70% would be ridiculous. So one can immediately conclude that Singapore is unlikely to achieve future growth rates comparable to those of the past.[14]

Similar stories, albeit less extreme, can be found across Asia. Because money was being thrown at countries like Thailand by external investors and the five Cs were in a virtuous phase, capital was easy to come by. Combined with the overconfidence developed through the prior years of spectacular economic performance, it is conceivable that money was allocated to projects with unrealistic return expectations. In an outcome that would not have shocked Austrian economists, malinvestment seemed not only likely, but inevitable.

“Hasn't Happened” Is Not “Can't Happen”

One of the great financial industry disclaimers is that “past performance is no guarantee of future results.” I've always found this statement a bit odd, even misleading. Perhaps it should be modified to “past performance is not related to future performance” or “insofar as past performance indicates a replicable skill and the future looks like the past, then we might think it possible that future results might or might not resemble past results,” or something like that. In any case, just because something has happened, doesn't mean it will continue to happen. Likewise, just because something has not happened, does not mean it cannot or will not happen. In fact, underestimation of the probability of events that have not happened is a common cognitive bias. It is the natural result of employing an availability heuristic. Obviously, images and stories of an event that has not happened are less available.

Many Thai individuals, companies, banks, and government officials did not spend much time thinking about the currency mismatches that plagued their financial structures. Given the role that such mismatches played in the rapid unraveling of the economy, let us consider how the decision-making biases discussed in Chapter 3 might have affected Thai thinking.

From a psychological perspective, why were major movements in the currency not seen as possible by lenders or borrowers? Perhaps it was because the Thai currency had been very stable prior to the crisis. Perhaps it was because there was not any available data on Thai currency volatility. (Surely, however, foreign lenders would have been aware of the recent Mexican depreciation.) Perhaps they were anchored on the current ratio of ~25 baht per dollar and made insufficient adjustments for the range of likely outcomes. Might lenders and borrowers alike have thought the baht might move by ~10%?

There are clearly dozens upon dozens of questions that we could ask about the decision-making processes that led to the currency mismatch, but one thing remains certain: the decision to borrow in dollars while earning in baht created vulnerabilities—risks that were inappropriately considered (likely for one of the many psychological reasons discussed in Chapter 3) in the course of making that decision.

Another indicator of overconfidence (as well as excess liquidity and loose monetary conditions driven by foreign inflows) was the construction of Malaysia's Petronas Towers. The twin skyscrapers, which were completed in 1997 and almost perfectly marked the top of Asian financial markets prior to the Asian financial crisis, were the tallest buildings in the world. Later in the book, we will discuss the skyscraper indicator in greater detail.

Crony Capitalism as Cultural Coping

Although there are hosts of political issues and policies that we could consider through a political lens, this section will focus on the idea of rights and prices (the two primary elements presented in Chapter 4). Let's begin with property rights, which are, across most emerging markets, less well developed than those in the United States or the “first” world. Nevertheless, property rights did exist in many emerging Asian countries, but were significantly less well protected and infringements on them less well enforced. How might individuals and companies attempt to cope with such an environment?

One way to gain greater comfort in your claim for certain property is to have strong relationships (even blood-based relationships, i.e. family) with those with whom you might end up having a dispute. Harvard University Professor Dwight Perkins summarizes the issue at hand:

Societies made up of self-contained villages or autonomous feudal estates do not have to worry much about the security of economic transactions. The village elders or the feudal lord can enforce whatever rules they choose. However, when trade takes place over long distances, local authority can no longer guarantee that a transaction will be carried out in accordance with a given set of rules … . A general authority must provide security along the road or river; each individual trader should not have to provide it on his own …

In Europe and North America, the required security was supplied by laws backed up by a judiciary that over time became increasingly independent of the other functions of government. This development of the rule of law backed up by an independent judiciary took place over centuries, and the process was well along by the eighteenth century … . There was no comparable development of this kind of legal system in East and Southeast Asia. There was, however, the development of long-distance commerce both within and between economies in Asia, and that commerce had to have something that substituted for the rule of law. That substitute drew on one of the strengths of East Asian culture: close personal relationships based on family ties, as well as ties that extended beyond the family.[15]

Thus, it seems at least reasonably likely that the lack of strong institutional structures to enforce property rights led to the mass adoption of lending based on noneconomic considerations. Perhaps the simple lack of well-defined property rights should have been an alarm bell for foreign lenders.

In terms of price considerations, perhaps the greatest government-inspired distortions took place in the currency markets. Given the export-oriented nature of most of the region's economies, most governments worked to keep their currencies cheap relative to their trading partners. The result of these efforts was an increase in the country's relative dependence on exports as cheap currencies supported exporter profits. Likewise, it hurt importers and therefore discouraged the generation of domestically oriented industries. The managed foreign exchange rates also drove, as discussed previously in the example involving Mr. T, significant currency mismatches in the financial structures of domestic companies. Thus, by interfering with the price mechanism's efforts to determine the price of a currency, many Southeast Asian governments magnified their vulnerability to currency volatility.

Asian Harmony vs. Western Individualism

Our epidemic lens provides little value here. As is evident by the now common reference to the East Asian financial crisis (not boom or bubble), the story here is really one of a bust. Sure, stories exist of property prices and stock prices going through the roof, but there are few stories of taxi-drivers, housewives, and gardeners investing in the market. Rather, it was a story of global capital flows (some might argue “hot money”) that filled the void and provided the fuel for the boom to take place.

The emergence lens, however, does provide insight. As we found in our study of the Japanese boom and subsequent bust, Asian philosophies tend to be less individualistic and more communal. They emphasize social harmony and group cohesion over individual pursuits. The impact of this pack mentality is that markets become highly tippable in one direction or another. Just as was discussed in the previous chapter about Japan, so too is the swarm/herd framework applicable with respect to Asia. We won't recount the same logic here, but it might make sense to review Chapter 8 in light of the East Asian scenario. It will seem eerily pertinent.

Another “herd” that emerged during the early 1990s was the group of fund managers focused on emerging markets. Inherent in their design as diversified managers was a linkage and coupling of very different economies into one bucket. Further, given that they merely allocate other people's money, these fund managers became herdlike in their behavior because of client flows. Thus, in good times (think early 1990s), flows into their funds would likely be positive and the herd would stampede in—bringing capital along. However, if the herd changed direction, the stampede would occur in the opposite direction, with capital flight from the country.

In discussing the Asian crisis, Michael Lewis described this phenomenon bluntly: “The collapse of the Thai baht in July 1997 caused the people who had invested in places that reminded them a bit of Thailand (South Korea, the Philippines, Indonesia, Malaysia) to take their money and go home.”[16]

The Multilens Look

Given that the East Asian financial crisis was really a set of many different crises that fell like dominoes, this chapter attempted to focus on the case of Thailand as representative of the situation. The five-lens approach to thinking about the events that unfolded yields some striking similarities to other booms and busts. Table 9.1 provides a quick summary of the chapter via the lenses discussed.

Table 9.1 The Five-Lens Approach to the Asian Financial Crisis

Lens

Notes

Microeconomic

Pro-cyclical capital flows.

Reflexivity of confidence.

Macroeconomic

Hot money inflows providing cheap capital.

Financial innovation (finance companies to hide leverage).

Moral hazard motivated lending.

Psychology

Anchoring on currency values; insufficient adjustment.

World's tallest skyscraper (Petronas Towers).

New era thinking (“miracles” and “tigers”).

Politics

Crony capitalism inspired moral hazard.

Political focus on undervalued currencies.

Biology

Silent Leadership (communal philosophies).

Herd of emerging markets funds.

Notes

1. World Bank, The East Asian Miracle: Economic Growth and Public Policy (New York: Oxford University Press, 1993).

2. Robert Barbara, The Cost of Capitalism: Understanding Market Mayhem and Stabilizing our Economic Future (New York: McGraw Hill, 2009).

3. Charles P. Kindelberger and Robert Aliber, Mania, Panics, and Crashes: A History of Financial Crises (Hoboken, NJ: John Wiley & Sons, 2005).

4. Paul Krugman, The Return of Depression Economics and the Crisis of 2008 (New York: WW Norton, 2009).

5. Ibid.

6. Timothy Geithner, “Reflections on the Asian Financial Crisis” (speech at the Trends in Asian Financial Services Conference, Federal Reserve Bank of San Francisco, June 20, 2007).

7. Krugman, The Return of Depression Economics.

8. Dwight Perkins, “Law, Family Ties, and the East Asian Way of Business,” in Lawrence Harrison and Samuel Huntington, eds, Culture Matters: How Values Shape Human Progress (New York: Basic Books, 2000).

9. Kindelberger and Aliber, Mania, Panics, and Crashes.

10. Janet Yellen, “The Asian Financial Crisis Ten Years Later: Assessing the Past and Looking to the Future” (speech to the Asia Society of Southern California, February 6, 2007).

11. David Liebhold, “Thailand's Scapegoat?” Time 154, no. 25 (December 27, 1999).

12. Paul Krugman, “The Myth of Asia's Miracle,” Foreign Affairs (November/December 1994).

13. Robert M. Solow, “Technical Change and the Aggregate Production Function,” The Review of Economics and Statistics 39, no. 3 (Aug1957).

14. Ibid.

15. Perkins, “Law, Family Ties.”

16. Michael Lewis, Panic: The Story of Modern Financial Insanity (New York: Norton, 2009).

CHAPTER 10

The U.S. Housing Boom and Bust: THE HOMEOWNER'S SOCIETY CREATES THE PEOPLE'S PANIC

It's the English speaking world's favorite economic game: property. No other facet of financial life has such a hold on the popular imagination. No other asset-allocation decision has inspired so many dinner-party conversations. The real estate market is unique. Every adult, no matter how economically illiterate, has a view on its future prospects.

—Niall Fergusson

The global credit crunch that began with a hiccup in the U.S. subprime markets in February 2007 snowballed into the greatest economic contraction since the Great Depression. The financial engineering that expanded the ability to afford homes in the United States in the middle of the 2000s became the underpinnings of the boom and bust of residential real estate that helped spur the world economy into a series of financial and political reactions that are still being felt today.

“Safe as Houses”

In his book The Ascent of Money: A Financial History of the World, Niall Fergusson explains what property ownership can mean to individuals as well as the world of finance. For very good reasons, investing in housing seems like a safe bet:

“Safe as Houses”: the phrase tells you all you need to know about why people all over the world yearn to own their own homes. But that phrase means something more precise in the world of finance. It means that there is nothing safer than lending money to people with property. Why? Because if they default on the loan, you can repossess the house. Even if they run away, the house can't. As the Germans say, land and buildings are “immobile” property. So it is no coincidence that the single most important source of funds for a new business in the United States is a mortgage on the entrepreneur's house. Correspondingly, financial institutions have become even less inhibited about lending money to people who want to buy property.[1]

The ending of the internet, media, and telecom boom of 2000 resulted in an unprecedented asset bubble implosion in the United States. In an effort to stimulate the economy, monetary policy became extraordinarily loose, with a host of unintended consequences. Krugman describes leadership at the Federal Reserve as follows: “Greenspan acted like a parent who sternly warns teenagers against overdoing it but doesn't actually stop the party, and stands ready to act as designated driver when the fun is over.”[2] And so it was that the Greenspan Fed lowered rates in the aftermath of the internet bubble's bursting, and proceeded to hold them at low levels for an extended period of time.

Writing in 2005, economist Robert Shiller, co-creator of the widely followed Case-Shiller housing price index, whose March 2000 publication of Irrational Exuberance proved extraordinarily well timed in predicting the internet bust—predicted a housing bust. He noted that “the market for real estate, particularly individual homes, would seem likely to display speculative booms from time to time, since the psychological salience of the prices of the places we see every day and the homes we live in must be very high, and because home prices are such a popular topic of conversation.”[3] Just as he meticulously dismissed popular explanations for why the internet boom was different and would not bust, so too did he address the numerous rationalizations explaining why the housing boom would not bust. He notes:

One such explanation is that population pressures have built up to the point that we have run out of land, and that home prices have shot up as a result. But we didn't just run out of land since the late 1990s: population growth has been steady and gradual. Another theory is that the things that go into houses—the labor, the lumber, the concrete, the steel—are in such heavy demand that they have become very expensive. But construction costs are not out of line with long-term trends. Another glib explanation is that the boom is due to the interest rate cuts implemented in many countries in an effort to deal with a weak global economy. But while low interest rates are certainly a contributing factor, central banks have cut interest rates many times in history, and such actions never produced such concerted booms.[4]

To understand the magnitude of the bubble that was created in the U.S. housing market, consider the charts (using data compiled by Shiller going back to 1890) that show the U.S. home price index (a proxy for the real, inflation-adjusted values of homes) compared to the long-run drivers of housing costs, namely the cost of building materials and the demand (i.e. population) for housing.

As visible in Figure 10.1, inflation in building materials and labor does not adequately explain the sudden and rapid rise of the home price index during the 2000s. Might demand for housing be the culprit? Figure 10.2 plots that same home price index against the U.S. population.

Figure 10.1 Shiller's Home Price Index vs. Building Cost Index

Source: www.econ.yale.edu/~shiller/data.htm.

Figure 10.2 Shiller's Home Price Index vs. Population

Source: www.econ.yale.edu/~shiller/data.htm.

As seen in Figure 10.1 and Figure 10.2, U.S. home prices very clearly had appreciated at a rate faster than that of the fundamental housing market drivers. Between 1890 and 2005, when this chart was produced, housing prices were up 85% in real terms, far greater than the appreciation in building costs. Likewise, prices grew more rapidly than the population, and the additional cost of money (i.e. interest rates) does not amount to much of a cost pressure on housing. In 2006, these figures clearly indicate an unsustainable boom in progress.[5]

The Music Stops

In July 2007, the chief executive officer of Citigroup, Chuck Prince, granted an interview to the Financial Times. During the interview, he was asked about the potentially excessive housing market lending that Citigroup was conducting, particularly in light of early indications of problems in the subprime market. In a now-famous response that captures the competitive risk taking that took place among virtually all participants in the housing finance markets, Prince noted, “When the music stops, in terms of liquidity, things will be complicated. But as long as the music is playing, you've got to get up and dance. We're still dancing.”[6] Unfortunately for Citigroup and the rest of the housing finance complex, the music definitely stopped at some point in 2007, and inappropriately undertaken risks were blatantly exposed—resulting in what was one of the worst economic contractions and credit crunches of all time.

The magnitude of the pullback is best captured by the earlier-referenced home price index. Housing prices clearly crashed from their peak, which was actually reached in mid-2006, and bottomed out in early 2012 after having surrendered nearly all of the price gains of the prior decade. Professor Shiller maintains the data and updates it quarterly. Figure 10.3 includes the most recent data as of September 2018.

Figure 10.3 Shiller's Home Price Index, 1890–2018

Source: www.econ.yale.edu/~shiller/data.htm.

The “canaries in the coal mine” of the housing boom were the subprime lenders, many of which began keeling over in early 2007. A March 2007 article in Business Week noted, “After years of easy profits, the $1.3 trillion subprime mortgage industry has taken a violent turn: at least 25 subprime lenders, which issue mortgages to borrowers with poor credit histories, have exited the business, declared bankruptcy, announced significant losses, or put themselves up for sale.”[7] After significant losses due to subprime exposure were reported during the summer of 2007 by hedge funds managed by Bear Stearns, the matter snowballed into a global problem by the fall of 2007 when French bank BNP Paribas indicated that it could not accurately value assets in three of its asset-backed securities (ABS) funds because of exposure to U.S. subprime assets.

In order to fully understand how and why the housing bust accelerated and spread, it's important to understand the dynamics of the shadow banking system and how it differs from traditional banking operations. To do so, we need to take a step back and understand the process of securitization and how it applies to the mortgage industry.

Securitization is a process by which a pool of assets (such as mortgages or credit card loans) is divided into several pieces known as collateralized debt obligations (CDOs). For instance, if 5,000 mortgages, each of $500,000, are put into one structure, the entire pool has assets worth $2,500,000,000 (assuming all mortgages are considered good). Let's also suppose that the pool is divided into 250 individual securities, each worth $10,000,000. Because each security has a claim against the whole pool (and not against 20 specific mortgages), the securities created in this process are seemingly less risky and more diversified.

Investors such as pension funds may be able to get $10 million of exposure to mortgages without the concentration risk of having 20 direct obligations. Further, because securitization allows for the allocation of cash flows coming from the mortgages in any manner desired, each tranche (piece of the pool) need not be treated equally. Thus, senior tranches may be shielded from the first losses. This unique element allows for significant structuring flexibility and enabled the creation of seemingly very secure tranches out of arguably very insecure pools of mortgages.

The ramifications of securitization were numerous. First, it necessarily broke the symbiotic relationship between lender and borrower. Whereas banks would historically keep the mortgages on the books and service the loan directly (maintaining a relationship with the borrower), securitization enabled the separation of mortgage origination and mortgage servicing. Thus, originators no longer had a vested interest in the long-term viability of a borrower, resulting in definitively lower lending standards. At the very least, it allowed banks to issue more loans than their balance sheet might otherwise allow, because they were able to securitize loans made with bank capital and thereby continue lending to more customers with recycled resources.

Second, securitization made dealing with problem loans particularly difficult. Because mortgages were in a pool that had their cash flows allocated according to prearranged dynamics, no one party was responsible for a particular mortgage. Whereas a delinquent borrower might previously have been able to negotiate directly with the bank holding their mortgage, securitization made such negotiations and potential loan modifications almost impossible. The result was more foreclosures and forced sales in a falling market. Some 2.7 million foreclosures were on properties with mortgages packaged in securitized instruments, about half of all the foreclosures in the housing crisis.

The third and final element of the shadow banking system is that it is effectively part of the global capital market, and is therefore subject to the whims and vagaries typical of capital markets. For instance, a successful securitization requires there to be willing buyers of the securitized products. If capital markets become unreceptive, or downright hostile, to further securitization, as they did in 2008, then the shadow banking system is likely to shut down and generate dynamics comparable to banking failures.

At the end of the day, shadow banking is just another form of credit provisioning, because it allows borrowers to obtain money from lenders. When the system broke down following the subprime market implosion, the results were felt far and wide. Banks that had previously depended on the shadow banking market to take loans off their hands were stuck with more loans than they desired, the secondary market in collateralized obligations slowed dramatically, and fearful originators stopped lending. The whole system came to a screeching halt with a dramatic contraction in the availability of credit. The resulting impact upon commerce, housing markets, and other asset classes was severe.

Since the crisis, a number of measures have been enacted to constrain the financial products that help feed the housing boom. Reforms were focused on minimizing risky credit extended to consumers and on limiting the amount of leverage taken by large financial institutions. The Dodd-Frank Act of 2010 created the Consumer Financial Protection Bureau to protect consumers from abuses by the financial sector in everything from mortgages to student loans. Dodd-Frank also raised the capital reserves many investment banks have to hold against their investments and forbid certain banks from proprietary trading, which critics had suggested effectively turned investment banks into large-scale hedge funds. (Admittedly, some “proprietary” trading was really market-making activities that enabled more fluid markets.) The net effect was doubling the amount of capital held against risk-weighted assets from 6.5% in 2007 to 13% and cutting leverage exposure in half, according to 2017 a report in the Harvard Business Review.[8]

The Boombustology of the U.S. Housing Boom and Bust

The housing bubble offers a few vivid lessons, of large tracts of housing developments empty, and neighborhoods where one third of homes were foreclosed. The bubble also interestingly gave renewed life to Donald Trump as a cult figure among young, ambitious business professionals. His personal wealth-creation in real estate had been enormous and included the launch of a Las Vegas casino resort and some of the most prominent real estate developments in major cities (with little mention of the grand-scale credit that fueled his developments). The image of real estate as wealth creator was reinforced by his television show The Apprentice, where contestants vied for the right to work for Trump selling real estate, including a Palm Beach mansion that was flipped in 2006 to Russian billionaire Dmitry Rybolovlev. At the peak of the real estate bubble, there was even a book for the Trump-wannabes: Trump Strategies for Real Estate: Billionaire Lessons for the Small Investor. Surely if The Don was investing, it had to be safe for Joe Six-Pack to invest, right?

Reflexivity Relationships Between Credit and Collateral

One of the primary factors that drove the boom in mortgage lending was that “loans are made on the basis of the value of the property, not on the ability of the borrower to repay,”[9] resulting in a powerful reflexive dynamic. Had lenders constrained their issuance of loans to those able to repay (i.e. adopting income or cash flow–based lending criteria), it's unlikely a reflexive dynamic would have been as drastic, even if it did take hold. Nevertheless, because asset-based lending was the order of the day, a virtuous cycle ensued during the boom phase and turned into a vicious cycle during the bust phase. Although related impacts from this dynamic were evident in capital flows, confidence, and economic conditions, the primary impact was found in the relationship between collateral and credit.

Cheap Money = Expensive Housing

The relationship between the cost of money (i.e. the interest rate a buyer might pay) and the value of an asset that can be bought with that money (i.e. a house) is perhaps most obvious in those assets that are typically bought with a high percentage of borrowed money. Housing provides the clearest example of this phenomenon at work. See Chapter 2 for a refresher.

Consider the following situation. A house is sold for $400,000 to a buyer who secures a mortgage for 80% of the purchase price ($320,000) at an interest rate of 10%. For ease of calculation, let's assume that the mortgage is interest only. In this case, the monthly payments would equal $320,000 * 10% / 12 months = $2,667. If interest rates were to fall to 5%, those same payments would fall by 50%, with the new monthly required payments equal to $320,000 * 5% / 12 months = $1,333.

Although lower interest rates have tremendous power to lower mortgage payments, the connection with house prices—which may be less obvious—is equally powerful. Suppose now that a person had a budget of $2,667 per month. How much could he afford to pay for the same house with the lower mortgage rates? Simple algebra facilitates this calculation: $2,667 * 12 months / 5% = $640,000 of loan amount. Add to this loan amount the original down payment of $80,000, and we see that the lower interest rate might increase the amount that our buyer can afford to pay to $720,000—assuming no minimum down-payment percent.

Given the dynamics between interest rates and housing prices, then, it should come as no surprise that monetary policy, which brought interest rates down to 1% by 2002, ignited a serious property boom. By manipulating the cost of money down to highly stimulative levels to encourage economic activity, the Federal Reserve unintentionally created tremendous upside pressure on the prices of highly leveraged assets such as houses.

Further, it might make sense to revisit Hyman Minsky's financial instability hypothesis and the role of financing structures in generating instability. In particular, there appears to be a remarkable similarity between Minsky's description of Ponzi finance and the terms on which some mortgages were underwritten. Stories of mortgages with 1-year teaser rates abound. Likewise, it now appears that many loans were taken by individuals who had the ability neither to service the debt nor to pay it back with income. They were “banking” (pun intended) on the value of the home rising, thereby allowing them to either sell the property at a profit or cash out adequate money to pay the mortgage. Such financing structures are inherently unstable, as discussed in Chapter 2.

“House Values Don't Go Down”

The phrase “it takes two to tango” is highly applicable when it comes to the U.S. housing boom and bust. Ambitious homeowners-to-be were eager to “lever up” in a quest to buy the largest, most expensive home they could purchase, and eager lenders were willing to design various mortgage products to meet their needs. The seemingly symbiotic relationship was taken to extremes by both lenders willing to lend more money than a borrower could afford, and borrowers willing to borrow more than they could repay. Both parties were operating under an extraordinarily flawed assumption—namely, that house values simply do not fall.

Before we dive into the social psychology of home ownership and its virtues, we should consider some of the mortgage innovations that emerged prior to the housing boom and fueled it to lofty heights. In particular, the emergence and rapid growth of subprime loans (loans made to those with problematic credit histories), adjustable rate mortgages (mortgages that have floating interest rates that can go up over time—many of which were accompanied by very low initial teaser rates), interest-only mortgages (loans in which the borrower did not have to repay any principal), and stated-income loans (mortgages that required no documentation of income) greatly enlarged the pool of available homebuyers by increasing access to credit. Down payment assistance programs further enlarged the universe of homebuyers by providing capital for down payments.

Media reports about the housing boom and seemingly ubiquitous dinner party conversations about the rapidly rising prices of homes also contributed to the belief that house prices don't fall. The cover of Time magazine in 2005 highlighted the role of housing in wealth creation and how low interest rates were making the housing market more affordable to more people. Its headline tells the whole story: “Home $weet Home: Why We're Going Gaga Over Real Estate.” As if that wasn't enough, Boston magazine's cover story (see Figure 10.4) in May 2006 (quite possibly the absolute top of the real estate market) was titled “Buy! Buy! Buy! The Smart Real Estate Moves Right Now.” Unfortunately, no one appeared to be questioning whether it was even smart to be invested in real estate.

Figure 10.4 Telling People What They Want to Hear: Popular Sentiment Exemplified

In addition to the obvious sense of overconfidence that existed among U.S. home buyers in 2005 and 2006, it seems as if even cautious home buyers suffered from cognitive biases such as anchoring and insufficient adjustment. Sure, the logic went, house prices could fall a bit, but not meaningfully enough to matter. This logic is textbook insufficient adjustment. Likewise, the fact that these adjustments were made from lofty current prices (i.e. that buyers anchored on them) made the adjustments even less useful for even cautious buyers.

Homes for Everyone!

Another critical factor in the U.S. housing boom and bust was political encouragement for every American to own a home. In a well-written article that shed light on the role of government in the housing boom and bust, Peter Wallison, the Arthur F. Burns Fellow in Financial Policy Studies at the American Enterprise Institute, noted that “the crisis has its roots in the U.S. government's efforts to increase homeownership, especially among minorities and other underserved or low-income groups, and to do so through hidden financial subsidies rather than direct government expenditures.”[10]

How exactly did the government go about promoting homeownership? Wallison argues that the “Community Reinvestment Act, Fannie Mae and Freddie Mac, penalty-free refinancing of home loans, tax preferences granted to home equity borrowing, and reduced capital requirements for banks that hold mortgages and mortgage-backed securities” all contributed to a system that grew to be increasingly vulnerable as lending standards fell to accommodate these and other government efforts.

The Community Reinvestment Act (CRA), originally enacted in 1977, underwent a major revision in 1995, the impact of which was immediately felt in homeownership rates. CRA was adopted to prevent discrimination in access to capital and encouraged banks to focus on community needs. After the 1995 revisions were enacted, homeownership jumped from the stable 64% level it had been for several decades to over 69% by 2004.

Freddie and Fannie were two government entities designed to purchase loans from banks in an effort to encourage additional mortgage lending. In this sense, these entities were the original securitizers of mortgages and effectively created the secondary market in mortgages. As pressure mounted during the Clinton administration to increase the availability of housing finance, Freddie and Fannie responded. Wallison notes,

By 1997, Fannie was offering a 97 percent loan to value mortgage, and by 2001, it was offering mortgages with no down payment at all. By 2007, Fannie and Freddie were required to show that 55 percent of their mortgage purchases were LMI (low to moderate income) loans and, within this goal, that 28 percent of all purchases were from underserved areas (usually inner cities) and 25 percent were purchases of loans to low-income and very-low-income borrowers.[11]

Clearly, these were the least creditworthy borrowers in the system, perhaps explaining why profit-oriented banks had not previously paid attention to them as potential borrowers. Only after the securitized market took off, and government mandates were clear, did those banks begin paying attention to this group of potential customers.

The final area of political influence on the housing market that deserves discussion is that of tax policies. Specifically, the mortgage interest deduction has effectively served as a subsidy to homeowners, with the largest subsidies going to the homeowners with the largest mortgages (presumably associated with the largest incomes). To see the subsidy in action, let's compare the difference between a person who has a $2,000 per month rent payment and one who has a $2,200 a month mortgage payment, of which $2,000 is interest and is therefore deductible. Because the renter is not able to deduct their rent payments, their after-tax cost of housing remains $2,000 per month. The homeowner with a mortgage, however, is able to deduct (subject to certain limitations) the interest from their taxable income. Assuming the homeowner is in a 30% tax bracket, their after-tax cost of housing will be $2,000 * (1 – 30%) + $200 = $1,600. This basically equates to a governmental handout of $600 per month to our homeowner. Further, given that $200 of this $1,600 payment is for repayment of principal, the rent equivalent is $1,400 per month, $600 per month less expensive than our renter.

In an eloquent New York Times Magazine article titled “Who Needs the Mortgage Interest Deduction?” Roger Lowenstein describes the logic of the mortgage interest deduction as follows:

Some fellow in the Treasury Department had long ago decided it would be a good thing for families like ours not to suffer through our lives as tenants. In fact, he (whoever he was) decided it would be good for our neighbors and for society in general if we could be owners and not just dwellers. In early America, only those who owned property were eligible to vote, and the notion that tenants were only provisional citizens, or at least had a lesser stake in things, has somehow endured. According to studies, people who own their homes take better care of them; they fix the roof more often and plant more lilacs. They join more clubs and community groups; they vote more often; they move around less often; and their kids do better in school. The government is subsidizing my house so I will do more gardening. Or something like that.[12]

Further, because interest on home equity loans and lines of credit were also deductible prior to the 2017 tax reform, homeowners were encouraged to take out second and sometimes third mortgages against a property to fund (over)consumption. As noted by Wallison,

Interest on consumer loans of all kinds—for cars, credit cards, or other purposes—is not deductible for federal tax purposes, but interest on home equity loans is deductible no matter what the purpose of the loan or the use of the funds. As a result, homeowners are encouraged to take out home equity loans to pay off their credit card or auto loans, or to make purchases that would ordinarily be made with credit cards, auto loans, or ordinary consumer loans.[13]

By encouraging the use of home equity financing for consumption purposes, the government promotes lower equity values in homes—thereby inadvertently enhancing the likelihood of defaults and foreclosures during a real estate downturn.

Epidemics and Emergence

The fact that the subprime market served as the last untapped universe of potential homebuyers to access the housing market indicates that their increasing participation in the housing market was an indicator of the boom's maturity. In the spectrum of mortgage quality, prime loans are considered the safest and are offered to the most creditworthy borrowers. Following prime, there is a category known as Alt-A loans, which are offered to borrowers without full documentation, with lower collateral values, and with lower credit scores. Subprime is the riskiest of the mortgages and was offered to borrowers who had historically been deemed uncreditworthy.[14] Figure 10.5 highlights the growth in subprime and Alt-A lending that took place during the final stages of the housing boom.

Figure 10.5 Rapid Growth in the Number of Subprime and Alt-A Loans

Source: http://www.gao.gov/new.items/d09848r.pdf.

In addition to the absolute growth in the number of subprime and Alt-A loans issued (as evidenced in Figure 10.5), the absolute dollar amount of the risky loans (defined as subprime and Alt-A) grew exponentially, as seen in Figure 10.6. Figure 10.6 also demonstrates how these risky loans became an increasingly large share of all mortgage originations, having risen from under 10% in 2002 to almost 34% by 2006. Within this category, it is interesting to note that the riskiest of loans, subprime, grew during the same period from approximately 5% of the total mortgage market in 2002 to over 20% of the mortgage market by 2006.[15]

Figure 10.6 Exponential Growth in Risky Loans

Source: The 2010 Mortgage Market Statistical Annual, Volume 1 (Bethesda, MD: Inside Mortgage Finance Publications, 2010).

From the data, it definitely appears that most of the eligible buyers (i.e. prime, creditworthy borrowers) had already been infected, thereby leaving the “disease” only to infect the historically un-creditworthy. In fact, the percentage of Americans who owned homes actually started to decline after reaching its peak in 2004, easing down one-half a percentage point by the time the Case-Schiller price index peaked, a sign the market had been saturated and speculators with multiple properties were a growing force in the market. Obviously, the available universe of “infectable” potential homebuyers was rapidly shrinking. By enabling the most amateur of investors to enter the housing markets, the boom in subprime lending was a useful indicator of the housing bubble's maturity and imminent burst. The fact that TV shows like Flip This House were in competition with other TV shows like Flip That House further validates the maturity of the boom. If ordinary, television-watching nonspeculators enter the housing investment business, who is left to infect?

The popularity of real estate not only grew as an investment activity, but also grew (unsustainably) as a professional activity. Figure 10.7 displays the number of members in the National Association of Realtors (NAR). Given the licensing requirements of the profession, NAR membership is a reasonable proxy for the number of real estate salespersons in the United States. Note how it peaked in 2006 and fell by more than 26% to the bottom of the correction in 2012.

Figure 10.7 Membership in the National Association of Realtors, 1987–2007

Source: National Association of Realtors membership report, https://www.nar.realtor/membership/historic-report.

Lest we dismiss this as an indicator visible only in retrospect, consider Figure 10.8, which illustrates NAR membership before, during, and after the Florida land boom. As is clear from the chart, membership did fall. Given our anchoring and insufficient-adjustment biases, surely few of us would have predicted realtor levels to drop by a quarter.

Figure 10.8 Membership in the National Association of Realtors, 1916–1936

Source: McCormick, Field Guide to NAR Membership Statistics, 1908–Present, http://www.realtor.org/library/library/fg003.

The Multilens Look

The U.S. housing boom and bust is a classic case of a credit-fueled reflexive bubble formed by the interaction of the five Cs described in this chapter. However, an equally supportive role was played by government policies in supporting the five Cs through both the creation of a securitization market and the encouraging of homeownership and home finance. Table 10.1 summarizes the five-lens view of the U.S. housing boom and bust.

Table 10.1 The Five-Lens Approach to the U.S. Housing Boom and Bust

Lens

Notes

Microeconomic

Reflexive credit/collateral dynamic.

Higher prices induced buyers.

Lower prices induced sellers.

Macroeconomic

Financial innovation (subprime, Alt-A, etc.).

Securitization enabled “shadow banking.”

Ponzi finance via teaser rates, etc.

Psychology

New era thinking (housing demand).

Anchoring on prices; insufficient adjustments.

Politics

Tax policies encouraging homeownership for all.

Moral hazard (government role in mortgage finance: Fannie, Freddie).

Supply/demand distortions (Community Reinvestment Act, mortgage interest deduction).

Biology

Amateur Participants (Subprime, NAR members).

Silent Leadership (Donald Trump).

Popular Media (Flip This House, Flip That House ).

Update: Uh Oh! Has the Music Restarted?

Fortunes have been made in real estate, but none as quickly as that made by John Paulson, the New York hedge fund manager who made ~$15 billion by shorting securitized mortgage instruments. In fact, Michael Lewis's book and subsequent movie The Big Short profiled a handful of personalities who profited handsomely as the housing bubble burst. These success stories from the crash have certainly increased the interest in seeing bubbles before they explode. In the case of the U.S. housing market, this means a new round of pundits seeing a renewed bubble in U.S.

Consider the chart of U.S. housing prices over the last 25 years shown in Time for caution?

Case Shiller Home Price Index

Source: https://us.spindices.com/index-family/real-estate/sp-corelogic-case-shiller.

Rarely, if ever, does a bubble occur in the same way in the same asset class, especially immediately following the first bubble. Regulatory reforms since then likely have dampened the likelihood of riskier loans being made to riskier borrowers. The CoreLogic Housing Credit Index, which tracks creditworthiness of mortgage borrowers from 2000 onward, shows that credit risk is within a normal range and well below 2006 housing bubble levels. Home ownership levels have returned to their long-term normal level of 64% after dipping to a 50-year low of 62.9% in 2015. The fall in home ownership to historical levels suggests the excessive confidence of 2006–7 has disappeared.

On the other hand, the Case-Shiller Index of real estate prices in various markets indicates that prices have indeed by rising rapidly, which combined with a growing mortgage market, imply the possibility of a reflexive dynamic. Higher prices are boosting lender confidence as rising collateral values give the appearance of falling risks.

Relatedly, the fact that interest rates have been low for a while probably means there has been excessive borrowing. (Recall the Warren Buffett quote: “Only when the tide goes out do we see who is swimming naked.”) At the time of this writing, the Federal Reserve was in the midst of increasing rates, a process that usually slows the economy and can affect asset prices. It's also worth revisiting the “affordability-based-approach” to valuation. Higher rates, all else equal (which is rarely the case), usually drive down the prices of leveraged assets.

The Boombustology framework works best when indicators are confirming each other, rather than providing contradictory evidence. That doesn't mean housing prices are not overvalued (they may be!). It also doesn't mean they are cheap (they're probably not!). It would not be surprising to see prices stagnate or retreat. Recall one of the main messages from the Austrian economists: mispriced money is often misused money. If money has been “underpriced,” might it have been misallocated? Caution seems warranted.

Notes

1. Niall Fergusson, The Ascent of Money: A Financial History of the World (New York: Penguin Press, 2008).

2. Paul Krugman, The Return of Depression Economics and the Crisis of 2008 (New York: WW Norton, 2009).

3. Robert Shiller, Irrational Exuberance, 2nd ed. (New York: Broadway, 2005).

4. Ibid.

5. Just so the reader does not think I am revising my version of history, I thought I might share a bit of my personal housing story with you. Upon seeing Robert Shiller's data on the housing market in early 2005, I immediately went about selling my apartment in Boston's Beacon Hill Neighborhood. My wife and I then proceeded to rent an apartment for the next four years and only recently purchased a condo in Brookline, MA.

6. Chuck Prince, interview with the Financial Times (July 5, 2007).

7. Mara Der Hovanesian and Matthew Goldstein, “The Mortgage Mess Spreads,” BusinessWeek (March 5, 2007).

8. Christian Edelmann and Patrick Hunt, “How the Great Recession Changed Banking,” Harvard Business Review (October 31, 2017), https://hbr.org/2017/10/how-the-great-recession-changed-banking.

9. Christopher Dodd, opening statement on “Mortgage Market Turmoil: Causes and Consequences,” U.S. Senate Committee on Banking, Housing, and Urban Affairs (March 22, 2007).

10. Peter Wallison, “Cause and Effect: Government Policies and the Financial Crisis,” Financial Services Outlook (November 2008). Washington, DC: American Enterprise Institute.

11. Ibid.

12. Roger Lowenstein, “Who Needs the Mortgage Interest Deduction?” New York Times Magazine (March 5, 2006).

13. Wallison, “Cause and Effect.”

14. Frank J. Fabozzi, The Handbook of Mortgage Backed Securities (New York: McGraw Hill, 2001).

15. Mara Lee, “Subprime Mortgages: A Primer,” National Public Radio story that was summarized on the NPR website. Lee cites Inside Mortgage Finance for her subprime data.

CHAPTER 11

China's Credit-Fueled Investment Boom

The real estate industry's excessive prosperity has not only kidnapped local governments but also kidnapped financial institutions—restraining and even harming the development of the real economy, inflating asset bubbles and accumulating debt risk.

—Yin Zhongqing, deputy director of the finance and economics committee of the National People's Congress

When the first edition of this book was published, in March 2011, China was extending its run as a booming economy that had been one of the world's fastest growing economies for years. Industrialization, urbanization, modernization, and entrepreneurship all appeared to be on steroids in the world's most populous nation, resulting in a relative consensus among global investors that China would continue growing at 8% for the foreseeable future, providing much-needed support to the global economy.

By almost any metric, economic progress in China over the past several decades has been phenomenal. GDP per capita, literacy rates, health care, infant mortality, life expectancy, and national wealth have all improved remarkably. However, as the famous disclaimer reads on most mutual fund advertisements, “past performance is no guarantee of future performance.” In 2011, this appeared to be the case with respect to China's progress, so we examined China as a potential bubble to be identified through our five-lens approach. Results in the years after proved what our Boombustology approach told us then: China's property market was indeed bubbly and continues, as indicated by the June 2018 quote at the beginning of this chapter, to be a disproportionate influence on the financial and real markets.

Here's what happened: money was inappropriately cheap, with resulting overinvestment and capital misallocation. Returns on invested capital began to fall while at the same time confidence remained lofty, as trophy-seeking behavior popped up in the art and wine markets and construction of several of the world's largest skyscrapers proceeded unabated, at least for a time. As a communist state, China is also prone to significant distortions to the price mechanism. Finally, many of the biologically inspired indicators, such as amateur investor participation, silent leadership, and popular media attention, suggested that China was bubbly. Let's examine in greater detail how all this developed and transpired.

Tendencies toward Equilibrium

As we have seen elsewhere, cheap money drove a massive credit-fueled investment boom that began reversing in 2012. While China created the appearance of a sustained growth that couldn't end, such dynamics were more of a paper tiger than a perpetual growth machine.

Perhaps the best indicator of a debt-fueled asset boom driven by reflexive tendencies is the simultaneous rise of debt levels with asset prices. Consider the following two charts, taken from NBER research published in July 2010 about the property markets in China. Figure 11.1 shows outstanding loan balances in China at this time, highlighting the massive growth in mortgage debt that had taken place over the 10 years from 2000–2010—with a particularly pronounced growth in mortgage lending over the 2007–2010 period. Likewise, Figure 11.2 demonstrates what occurred to property prices on a price-to-income basis. This concomitant rise in both leverage and asset prices is evidence of the reflexive dynamic under way at the turn of the past decade. The data presented in these two figures suggested then that China was in the midst of a reflexive debt-fueled housing boom.

Figure 11.1 Outstanding Loans in China, 2000–2010

Source: Jing Wu, Joseph Gyourko, and Yongheng Deng, “Evaluating Conditions in Major Chinese Housing Markets,” NBER Working Paper Series, Working Paper #16189 (Cambridge, MA: National Bureau of Economic Research, July 2010).

Figure 11.2 Price-to-Income Ratios for Several Chinese Cities

Source: Jing Wu, Joseph Gyourko, and Yongheng Deng, “Evaluating Conditions in Major Chinese Housing Markets,” NBER Working Paper Series, working paper #16189 (Cambridge, MA: National Bureau of Economic Research, July 2010).

Another reflexive dynamic that established itself was the belief that both the Chinese currency and land prices would only move in one direction—up. These beliefs proved to be highly supportive, validating, and fulfilling of each other. Because both Chinese and global investors believed that the currency could only appreciate, there emerged a desire to park money in Chinese-denominated assets. Chinese capital controls limit the free flow of capital, leaving local investors with few viable investment destinations. As a result, many Chinese recycled their profits into domestic investments.

Instead of parking excess capital in U.S. Treasuries, the Chinese focused on yuan investments in which to leave their money.[1] However, the Chinese stock market bust in 2007–2008 was scarring to most Chinese speculators and drove them to the property market. The belief that property prices are unlikely to fall has its origins in China's rural economic roots. Not too long ago, China was a primarily agricultural country and land was the single most important asset. Land obsession is still a deep reality in today's Chinese culture.[2]

Signs of bubbly conditions in Chinese property were ubiquitous, as we noted in the first edition of this book. On one February day, a Shanghai investor purchased 54 apartments. Separately, a villa sold for more than $30 million in late 2009. Even more spectacularly, the New York Times noted that developers in Tianjin “have created a $3 billion ‘floating city,’ a series of islands built on a natural reservoir, featuring villas, shopping malls, a water amusement park and what they say will be the world's largest indoor ski resort.”[3] Andy Xiang, a Shanghai-based real estate investor, noted “the speed you buy a house here is faster than you buy vegetables.”[4] In one case, more than 800 people lined up around a sports stadium—some waiting in a downpour for six hours or more—with the hope of purchasing one of the 220 units in a new Shanghai development.[5] Just as we notice in the Bitcoin bubble, interest rates on savings were below the real rate of inflation, leading people to invest in real estate as a way to avoid having savings eroded by inflation.

Further, if equilibrium-seeking dynamics were under way, it would be highly unlikely to see excess capacity and rising prices. In the land of mean-reverting, supply and demand–driven price equilibrium, it would be impossible to have empty apartments and rising apartment values. Yet that is precisely what happened in China. In addition to property prices rising rapidly (in some cases by 8–10% per month), there were reports of as many as 65 million urban electricity meters registering zero consumption over a six-month period of time in the midst of the bubble.[6] Although Chinese authorities tried to downplay such reports, anecdotal evidence of empty apartments became commonplace. On the commercial property front, reports varied from the official vacancy statistic of around 22% to one local distressed investor's vacancy estimate of 50%.

This may explain why despite such high vacancy rates, construction continued unabated. Noted short-seller Jim Chanos estimated that there were roughly 2.6 billion square meters (which equates to approximately 30 billion square feet) of nonresidential (i.e. office) real estate under construction in January 2010. In a speech he gave that same month at Oxford University, he noted, “There is a 5 foot by 5 foot office cubicle being built for every man, woman, and child in China.” Needless to say, the equilibrium-seeking tendency of Chinese property markets was temporarily suspended.

Leverage, Cheap Money, and Potential Deflation

The Mundell-Fleming model developed in the 1960s states that it is impossible for a country to simultaneously maintain a fixed exchange rate and independent monetary policy, and allow for free capital movement.[7] China has attempted to maintain a fixed exchange rate with the U.S. dollar and also retain its monetary independence.

Capital accounts are not freely convertible. Despite the allure of this approach, there is limited evidence supporting its success. As described by Sebastian Edwards, the Chief Economist for Latin America at the World Bank from 1993 to 1996, “the blunt fact is that capital controls are not only ineffective … but also breed corruption and inflate the costs of managing investment.”[8] In fact, the IMF's 2010 Global Financial Stability Report noted that capital “controls tend to lose effectiveness as market participants find ways to circumvent them … . many studies find no effect of controls on the volume of inflows …”

By linking their currency to the U.S. dollar, the Chinese effectively outsourced their monetary policy to the United States. And although the pegged currency may have historically made sense, it no longer appears to do so. Historically, when the Chinese economy was very dependent on trade with the United States, the U.S. and Chinese economies were basically synchronized. If the U.S. economy was doing well, then so too did the Chinese economy, as U.S. imports of Chinese goods likely rose. If the U.S. economy was in the doldrums, then Chinese exports were likely down.

This synchronicity of the Chinese and U.S. economies meant that a common monetary policy worked and was logical. If the United States began to overheat as economic conditions created inflationary pressures, similar conditions would likely exist in China. The Federal Reserve would then probably raise interest rates, providing an appropriate monetary response to both countries. Likewise, if the U.S. economy was struggling, the Federal Reserve might lower interest rates in a bid to increase economic activity—providing appropriate medicine for two similarly sick patients. The system made sense. It worked. However, over time, China and the United States became increasingly less interconnected as China increased its trade with the rest of Asia and the world.

The Chinese property boom coincided with the U.S. housing bust. The U.S. Federal Reserve issued massive doses of economic adrenaline in the form of very stimulative monetary policies. Through the veins of a fixed exchange-rate system and a Chinese yuan pegged to the U.S. dollar, this adrenaline was transmitted to the Chinese economy. The only problem was that the Chinese patient was not as dangerously ill as the U.S. economy was when the medicine was administered. The outcome was is extraordinarily easy money in China (and for that matter, in all countries that either peg or manage their currency to the U.S. dollar). Thus, while the United States fought deflation and struggled to maintain low real interest rates, Chinese real rates were driven to be remarkably negative.

To understand why negative real interest rates are a recipe for an unsustainable asset boom, we need only think of the fact that negative real rates mean that investors get paid to borrow from a bank and park the money in any asset that grows in nominal terms (i.e. an asset that has its price move with inflation). Thus, negative real rates tend to promote borrowing and leveraged investing. They encourage credit-fueled asset bubbles. At the time, Chanos highlighted this fact during an interview on financial news channel CNBC: “Bubbles are identified by credit excesses, not valuation excesses, and there's no bigger credit excess than in China.”[9]

The sheer volume of lending in China during this period was is staggering. According to the BCA Research writing in late 2010, “in the past two years, China's new credit creation has amounted to US$2.7 trillion—an amount equal to 4.4% of global GDP, or as much as the size of the U.S. credit expansion in the mid-2000s.”[10] Noted investor Hugh Hendry noted that a majority of this lending took place since the beginning of 2009, highlighting that the $1.9 trillion of lending that took place between January 2009 and May 2010 (the first 16 months of the Chinese stimulus program) was bigger than the economies of South Korea, Taiwan, and Hong Kong—combined.[11]

Although Beijing tried to slow the rate of credit growth numerous times (with analysts applauding some of their successes), research from Fitch Group indicated meaningful growth in a Chinese shadow banking industry more than offset this supposed slowdown in credit growth. The report noted that inflationary pressures seemed to take market participants by surprise, likely due to “the widespread misconception that the acceleration in lending in 2009 was a short-lived anomaly and that monetary conditions began to normalize in 2010.”[12] The report went on to concede that headline credit (i.e. bank lending) data pointed to a slowdown, but highlighted that “actual credit flows in China remain as high as in 2009: lending has not moderated, it has merely found other channels.”[13]

This scenario of massive credit flowing without resistance and easy money sloshing around in asset markets is precisely the foundation of an Austrian school prototypical unsustainable boom. Excess investment and overconsumption will result in too much capacity and an eventual bust. If money is inappropriately priced, it will be inappropriately allocated. Peking University professor of finance Michael Pettis notes that “low interest rates (along with their cousin, socialized credit risk) are the main causes of capital misallocation and excess capacity in China.”[14]

Yet China couldn't easily adjust these rates because to raise interest rates and thereby bring market rates toward equilibrium, it would have raised costs on state owned enterprises (SOEs). An analysis conducted by Hong Kong Institute for Monetary Research found that during this period “if SOEs were to pay a market interest rate, their existing profits would be entirely wiped out.”[15] For reference, it is thought that in this period non-bank SOEs controlled more than 33% of China's assets,[16] thereby having a disproportionately high impact on both labor and capital markets. The impact of “market interest rates” would have had a devastating impact upon the Chinese economy.

The other obvious manifestation of excessively low interest rates is misallocation of capital. When thinking about overinvestment and the potential for a bust, the steel industry exemplifies overcapacity. According to Worldsteel, the trade group representing the global steel industry, Chinese steel production grew from 23 million tons in 1977 to 683 million tons in 2011. This growth took the Chinese steel industry's share of global steel production from around 3% in 1977 to almost 50% by 2011, a level the country has held since.[17] Figure 11.3 summarizes the growth in reported Chinese production as well as Chinese steel's share of global production in the period running up to the investment bubble's peak.

Figure 11.3 Chinese Steel Production (MM Tons) and Global Market Share

Source: Steel Statistical Yearbooks, published by the International Iron and Steel Institute and Worldsteel Association; yearbooks from 1980 through 2009 accessible via www.worldsteel.org.

If we take a moment to break down the sources of demand for Chinese steel, we find a potentially reflexive relationship with the property market just described. In fact, according to Bank of America/Merrill Lynch, 53% of all Chinese steel demand was for construction purposes.[18] Although such data is hard to come by, anecdotal evidence suggests that up to 20% of Chinese steel production was being used to construct more steel mills! Truly a reflexive dynamic if ever there was one. In the middle of the China property bubble, it was estimated perhaps 175 million tons of excess capacity existed in Chinese mills. To put these numbers in context, 175 million tons of annual steel production is more than the production of South Korea and Japan—combined! And steel does not appear to be an outlier; similar grandiose statistics can be found for the size of the Chinese cement (greater than the rest of the world, combined) and aluminum industries.[19]

The overcapacity remains, prompting some to suggest it was a partial motivation for the Chinese development program to rebuild the historic Silk Road. The “Belt and Road Initiative,” as the plan is called, may help mop up some of the overcapacity that arrived from central planning and the property boom. The solution, it seems, to solving the ills of central planning is to … drum roll please … have more central planning. Building roads, ports, rails, and other infrastructure will help employ some of these idle human and capital resources, albeit at a cost of $1 trillion or more. Proponents of the program suggest it is China's version of the American postwar Marshall Plan and is a long-term strategy by which China is building future markets for its goods and services. Critics claim it is burdening nations like Malaysia, Pakistan, Zambia and others with onerous debt that they cannot support.[20]

Another example of unsustainable building in this period was found in the inner Mongolian district of Kangbashi, the urban center of Ordos City. Due to massive coal deposits in the area, Ordos rose to be the second wealthiest city (on a per-capita basis) in China. Despite having a functioning (albeit somewhat less modern than ideal) downtown area, the government decided to utilize stimulus spending to build an entire new downtown area. This massive, 137-square mile city became the poster child for China's “ghost cities”: centrally planned cities built well ahead of demand. For years, the appearance of a pedestrian in the city was described as unusual, “like a hallucination … like a lone survivor of some horror movie apocalypse,” Time wrote in 2010.[21]

Even in 2016, a Forbes report noted Ordos City still can appear uninhabited: “If you merely walk around the central plaza where the most prominent landmarks are located you are liable to encounter very few people.”[22] The report went on the note the expectation is the city will be saved by mandated diversification of its economy away from mining. “The government is putting a lot of money into it,” one official was quoted. Still, detail on population and occupancy of buildings remains disputed among observers.[23]

A final example of the overinvestment boom of this period was the South China Mall, labeled by Abu Dhabi's The National newspaper as the “Mall of Misfortune.”[24] Built in southern China on what were agricultural lands as recently as 2003, the South China Mall was the largest mall in the world when constructed and remains so today, with the ability to accommodate around 1,500 businesses. The 7 million square foot complex was described by PBS as being “Disneyland, Las Vegas, and Mall of America rolled into one. There are carnival rides, mini-parks, canals and lakes amid classic Western-style buildings.”[25] Although completed in 2005–2006, a PBS documentary noted in late 2009 that the mall had 10 or 12 operating tenants. In 2013, vacancy was reported as still being around 99% by the U.K. Daily Mail newspaper[26] Originally planned to have 70,000 visitors a day, the mall has not come anywhere near achieving this objective. The local population of largely migrant workers had little income to make a trip to the mall feasible. In 2018, mall management insisted that after a renovation to add an IMAX theater and additional amusement rides for children they would draw near-full occupancy,[27] although no statistics on occupancy or visitors are available.

When pondering how such a real estate failure might have taken place, Dick Groves, a retail consultant in Hong Kong, noted that, “When it's easy to get financing without having to convince someone of the project's feasibility, and without having to show preleasing commitment, you can start to get into trouble.”[28] PBS captured the essence of the South China Mall story by describing it as “a cautionary tale of capitalist hubris.”[29] The South China Mall was just one of more than 500 new malls built in China between 2005 and 2010, according to the Mall China Information Center.

Recent academic work has connected the overinvestment that is generated by cheap money—examples of which were just shared in this section—with overconfidence. In a recent study of Chinese companies, researchers found that overconfident managers overinvested, particularly when there was easy money.[36] Let us now turn to another indicator of overconfidence, namely conspicuous consumption.

China's Growth: How Sustainable?

As eloquently described by Paul Krugman in his 1994 Foreign Affairs article titled “The Myth of Asia's Miracle,” mobilization of resources cannot continue indefinitely. Growth derived from growth of inputs (capital and/or labor) is ultimately unsustainable as it eventually yields to diminishing returns. Economic growth slows, and the extrapolation of past input-driven growth trends are rapidly deemed inaccurate. Looking at China in 2009, it appeared as if China's past growth had been based on more inputs.

Let's begin by evaluating what happened vis-à-vis capital in China. The best measure of capital-derived growth is a country's investment rates. The following chart demonstrates that China's fixed asset investment (as a percentage of GDP) over the 1990–2010 period surged. Clearly, growth in capital has been an important contributor to Chinese growth. Unfortunately, such investment only provided one-time gains to GDP growth, leading to an ever-increasing appetite to invest. MIT professor Yasheng Huang described this reality as “Beijing's addiction to investment-driven growth.”[30] How likely was it that such investment-driven growth could continue without the creation of growth-limiting (or bust-inducing) overcapacity? In the first edition of this book, I suggested that investment-led growth had plateaued.

Chinese Investment as a Percent of GDP

Source: CEIC.

When it came to labor, the outlook was not much better. According to Bijal Shah, the urban labor force had grown at an annualized 7.5% rate between 2000 and 2010.[31] A major driver of this growth in labor was migration-driven urbanization. This growth trend continued through 2014, according to a 2015 IMF working paper.[32] Yet how likely was it that this migration process would continue at the same rate? The Economist analyzed the demographics of Qilin, a rural town about 38 miles northwest of Chongqing. By 2007, urban opportunities had drawn many from Qilin; “those left behind are mostly school-age children and the middle-aged and elderly, who would have little chance of finding jobs in urban areas.”[33] Shah's research highlighted that the number of 15–24-year-olds—those most likely to migrate—fell from 230 million to 180 million between 2010 and 2015. Given the high percentage of this population that had already moved to cities, demographic realities and continued high migration rates seemed very likely to be incompatible.

The IMF paper also found that workforce participation had been falling the first half of this decade,[34] and as of this writing China may already have entered into a period where the size of its working-age population has begun to decline, based on data from the United Nations and China's National Bureau of Statistics.

Evidence also exists that China may be more urbanized than commonly believed. At the time, many believed China was approximately 50% urbanized, a statistic based on a Chinese definition of an “urban center” as an area with population density of at least 1,500 people per square kilometer. According to such standards, Houston, Texas (the fourth-largest city in America, with a population of more than 2.2 million) would not qualify as a city! Using Chinese definitions, the United States has a very compelling urbanization opportunity. More likely, however, is the reality that China is more urbanized than thought.

Such data on the outlook for continued growth of capital and labor led the Journal of Chinese Economic and Business Studies to bluntly conclude that “growth in China has largely been driven by factor inputs.”[35] Such growth, I suggested in the first edition of this book, was ultimately unsustainable. As analysts worked diligently to estimate future Chinese economic growth, they most likely overestimated such growth because the origins of historical growth were heavily influenced by growth of inputs. Might the Chinese growth story still be less sustainable than widely believed?

Conspicuous Consumption and Overconfidence

The New York Times noted in April 2010 that Chinese influence on the art market was rising rapidly, analogizing China's soaring financial power and impact on the art markets to “a hurricane carrying off all that lies in its path.”[37] Although the article focused primarily on the Chinese influence on Chinese art, the implications for the global art market were soon felt in New York. China Daily noted on May 24, 2010, that “the art world is at fever pitch after an anonymous telephone bidder, now believed to be Chinese, paid a world record $106.4 million for a work by Pablo Picasso at a Christie's sale in New York earlier this month.”[38]

In case you think this purchase was a random, one-time event, consider contemporaneous comments by Ken Yeh, chairman of Christie's Asia, noting that “China's billionaire art buyers are creating something of an electric atmosphere in the art world.”[39] Yeh highlighted that the surge in Chinese interest was noted by up to 20 buyers who suddenly embarked on a mission to buy Western art. Traditional buyers recognized this newfound buying pressure, finding it frustrating when seeking to purchase art. Morgan Long, head of art services at The Fine Art Fund, noted that funds “have come across frantic bidding from people from the Chinese mainland. We haven't been able to buy anything we wanted at the prices we were looking for.”[40]

A November 2010 auction held in London captured the electricity of the Chinese art appetite quite well. An eighteenth century vase, dating from the period of emperor Qianlong, which had been found in a dusty old attic, was purchased at auction for $70 million by a buyer from mainland China.[41]

A similar appetite developed with respect to Chinese desires for wine.[42] During an October 2010 wine auction, three bottles of Chateau Lafite's 1869 were sold for the record-setting price of US$230,000 per bottle (more than 30 times pre-auction estimates) to an undisclosed Asian buyer. Immediately following the sale, however, Sotheby's head of international wine Serena Sutcliffe highlighted that “there's a lot of speculation why the Chinese like Lafite so much,” revealing the buyer's nationality as Chinese.[43]

China's appetite continued unabated for another year. According to Artnet, the value of art sold in China in 2011 surged to more than $9 billion, about double 2010! From 2008 through 2011, total growth in the auction market in China was more than 500%, overtaking the United States as the most valuable auction market in the world. By 2012, the excess capacity in the Chinese economy and the slowing of exports to European and U.S. markets led to more sober market activity among Chinese buyers. Interestingly enough, as this edition was going to press, there were signs that capital flight from China was accelerating, possibly reigniting the Chinese art market fire that was burning strongly a decade ago!

Evidence from a host of other markets showed that the rampant collector-driven buying went well beyond art. Consider the fact that the best-performing commodity in China in 2009 was garlic. As noted by The Guardian, “it is tastier than property, more pungent than gold, and rising in value faster than either. An astonishing market surge has seen Chinese speculators pile into garlic deals, causing prices to rocket between 10- and 30-fold in the last 18 months.”[44] Rare teas became equally exciting to Chinese speculators. Dahongpao tea, grown only in a small mountainous region of Fujian province, is a noteworthy case. Prices for Dahongpao tea rose more than tenfold between 2009 and 2010 and approached £1,000 per kilogram.[45] Or consider the fact that “mutton fat” jade, so named for its marbled white appearance, was selling for more than $3,000 per ounce, more than double the price of gold at the time. This represented a 10-fold increase from the decade prior, for “rocks” that had 20 years ago been used to fill sandbags to control flood waters![46]

In early 2011, a Chinese buyer paid $200,000 for a racing pigeon (a world record) at a Belgium auction. The bird, named “Blue Prince,” was one of 218 birds that fetched $1.8 million at the auction.[47] Perhaps the Chinese fascination with art, wine, jade, garlic, tea, and pigeons was a coincidental phenomenon with no relation to national overconfidence and/or conspicuous consumption. The 2012 retreat in Chinese art buying surely tells us it was not. Consider as well Chinese buying intentions in professional sports. In early August 2010, it was reported that the $200 billion China Investment Corporation, the government's overseas investment vehicle, was behind a bid to buy Liverpool Football Club.[48] Further, in 2009, the Chinese were reported to have purchased a minority stake in the Cavaliers Operating Company, the corporate entity that owns the Cleveland Cavaliers and operates the Quicken Loans Arena.[49] It wasn't until 2017 that the government decided to largely halt outflowing capital to sports investments outside the country.

Although overinvestment (see preceding section) is a telltale sign of managerial overconfidence, Chinese confidence can be reflexively validated into overconfidence through popular media noting “it's different this time” or by announcing the arrival of a new era. It is precisely with this logic that Martin Jacques's well-written, thoroughly researched, and thought-provoking book When China Rules the World from this bubble period was so concerning at the time. By providing a believable story as to why it's different this time and how we are about to embark on a “changing of global leadership,” the book had a very “toppy” feel to it and was eerily reminiscent of Japan as Number One.[50]

A final indicator of (over)confidence and easy money coming together in a potentially unsustainable manner can be found by looking at the previously discussed skyscraper indicator. A simple look at the world's tallest buildings then under construction would have helped identify where speculative juices were flowing, credit was easy, and confidence was high.

In the midst of this investment bubble, five of the 10 largest skyscrapers in the world were under construction in China, according to Skyscraperpage.com. Shortly after the first edition of this book was published, plans were announced to not only build the world's tallest building, but to do it in 90 days. The building, Sky City, was to be located in the city of Changsha, the most populous city in central Hunan Province. Tallying at 150 stories, the developer pledged to construct it a rate of five stories per day using prefabricated parts. By mid-2013, after the property bubble had burst, Sky City was cancelled, officially for environmental and safety concerns. In July 2015, the South China Morning Post reported that the 2.6 hectare foundations were filled with water and were being used by local villagers as a place to farm fish.

Rights, Moral Hazard, and Political Distortion

Charles Goodhart, former advisor to the Bank of England and Emeritus Professor at the London School of Economics, has noted that economic indicators lose their informational content if used as a target of economic policy. Known as Goodhart's law, this perspective captures one of the primary problems rampant in China at the height of the credit-fueled investment bubble. Whereas GDP had historically been a useful indicator (i.e. it was an outcome of economic activity), it has now become a policy target subject to manipulation in the quest to achieve the “number.” Economic activity no longer drives GDP; rather, GDP drives economic activity.

What might the impact of “GDP targeting” be upon economic activity? Consider the fact that government-mandated lending has driven local governments to demolish usable roads and dynamite functioning bridges in a quest to develop projects through which to deploy investment dollars and generate GDP.[51] These actions, while illogical to most observers, actually make a great deal of sense from the perspective of incentives at the local level—not only does the creation of new infrastructure generate economic activity (i.e. GDP), but so too does the act of destruction! Given that local officials are often evaluated on GDP growth in their geography, such infrastructure destruction and re-creation might also accelerate their climb up the communist party's ladder.

The tension between local objectives of rapid growth and the national desire for sustainability of growth resulted in Beijing pronouncements asking the provinces to “slow down.” In early January 2011, Zhang Ping, director of the National Development and Reform Commission, asked China's provinces to lower growth targets and to consider environmental impacts. Stratfor analysts noted that “the top technicians in control of the country's financial system face the dilemma of making forceful demands to slow the economy at the risk of driving it into the ground—or continuing with small adjustments and thereby revealing their weak will and emboldening the provincial warlords.”[52] Further, the analysts noted that “the provinces show no self-restraint because they are profiting from the easy credit and endless economic boom …”[53]

In his provocative book The Next 100 Years: A Forecast for the 21st Century, George Friedman of Stratfor notes that China may be a paper tiger by the year 2020. One of his central arguments rests on the contradiction of the political reality (communist, centrally managed) with the economic perception (market oriented), and the enormous misallocation of capital that may be taking place:

China appears to be a capitalist society with private property, banks, and all the other accoutrements of capitalism. But it is not truly capitalist in the sense that the markets do not determine capital allocation. Who you know counts for much more than whether you have a good business plan. Between Asian systems of family and social ties and the communist systems of political relationships, loans have been given out for a host of reasons, none of them having much to do with the merits of the business. As a result, not surprisingly, a remarkably large number of these loans have gone bad—“nonperforming” in the jargon of banking. The amount is estimated at somewhere between $600 billion and $900 billion, or between a quarter and a third of China's GDP, a staggering amount.[54]

How might this have occurred? As our study of previous booms and busts found, moral hazard and a belief in government backing often lead to significant confidence on the part of depositors, with a correspondingly robust confidence manifested within banks through brisk and voluminous lending. When thinking about the presence of moral hazard in China and its banking system, we need only stop to think about the all-powerful role of the government in terms of owning, running, and regulating most of the large financial institutions in the country. In addition, the communist, top-down approach to managing most matters implies not only that traditional moral hazard (i.e. depositors willing to give their money to any bank, confident that the government will protect them in the event of a bank failure) exists, but we might go so far as to suggest the Chinese system creates moral hazard on steroids.

In addition to de facto deposit insurance that banks need not pay a premium for, the China Banking Regulatory Commission also imposes several constraints (such as a capital adequacy ratio, a debt–assets ratio, and limitations on nonperforming loans) on Chinese banks, intended to ensure their safety, liquidity, and so forth. Further, regulators visit, audit, and monitor banks to ensure that banks are in compliance with these requirements.

Northwestern University professor Victor Shih noted the implicit deposit guarantee and “intrusive regulations make the system less secure, not more.”[55] Shih noted, “Profits from risky behavior can be so high that banks are willing to share some of the spoils with corrupt regulators who can help them circumvent bothersome rules.” He then followed up his argument with examples involving corruption related to lending practices; in one case, bankers bribed police to arrest auditors investigating a bank's books, and in another case, the vice president of a large commercial bank was convicted of receiving bribes to grant loans against lending policies and regulations.

Observing that such behavior is tougher to pull off in states with a free media, independent judiciary, and rule of law, Shih concluded that “small crises are not allowed to emerge to inform the public of accumulating systemic risks,” thereby allowing the covert building of large problems.[56]

In addition to such politically motivated bank lending and the moral hazard that accompanies it, even the casual observer of Chinese property markets cannot help but be overwhelmed by the magnitude of policy intervention that affects supply and demand dynamics. Just over the past 20 years, for instance, property-related policy objectives in China have shifted from stimulating demand (1998–1999) to stabilizing supply (2002–2004) to suppressing demand (2005–2007) to simulating demand (2008) to suppressing demand (2009–2014) to stimulating demand (2015–2017) and then suppressing demand (2018).[57] For instance, in 1998, housing reform allowed for the transfer of property-use rights to nongovernment entities and individuals; and, to stimulate demand, in 1999 mortgages with a 30-year term were introduced and down payment requirements lowered from 30 to 20%. By 2005, however, the required down payment was raised back to 30%, and lending rates rose six times in 2007 alone. But by the time the global economy began slowing in 2008, the Chinese authorities felt the need to lower the down payment requirement to 20% again, as well as to reduce property transaction-related taxes.

Lots of policies have been implemented since 2010 in an effort to control the property market, including policy restrictions on developers, limitations on banks’ lending to the sector, and so forth. By 2018, larger cities such as Beijing and Shanghai had firmly placed curbs to discourage speculation. Smaller cities hadn't implemented such measures and in fact were being stimulated by central government incentives, leading to a fresh property boom in which prices were in some cases growing at double-digit figures per month, according to The Wall Street Journal.

Under a national program, municipal governments borrowed money to tear down older properties and then gave money to residents to buy new apartments, effectively bailing out developers who had too much supply. Some $463 billion of these types of loans were outstanding as of mid-2018. By late summer 2018, state news reports were indicating that Beijing wanted to reign in the lending program to prevent local governments from overborrowing.[58]

Consensus, Silent Leadership, and Epidemics

Like most Asian cultures and most Asian nations, China comprises a quite homogenous group of people. Although variations exist, most citizens are Han Chinese and think of themselves as similar. Asian cultural values are still said to be consensus-oriented and tend to shun individualistic behavior or accomplishment. As noted in the case studies presented in Chapters 8 and 9, a social harmony–seeking, consensus-oriented society is one that is particularly prone to swarm and herd behavior.

China's stock market, effectively closed to foreign investors by regulatory mandates, has more investors than any other market in the world. There are about 200 million retail investors in the country, with some 81% of them placing a trade every month, according to a 2018 report by CNBC.[59] Former Morgan Stanley chief economist Andy Xie, a Shanghai-based economist, noted in the late 2000s how prevalent the stock market had become as a topic of conversation among ordinary Chinese citizens. In one conversation with a bartender, Xie found that his bartender was convinced that the Communist party would make sure the Shanghai market would rise to over 8,000 points in the next five years (it was 2,600 at the time and peaked near 5,200 in 2015). The logic was as revealing as the conviction expressed: “Look, the Hong Kong market is at 20,000 … . Shanghai at 8,000 would be very reasonable.” Similarly, when it came to real estate and property prices during this period, the Chinese seemed not to be seriously entertaining the idea that prices could meaningfully fall.

An August 2010 cover story of Bloomberg Markets magazine profiled Zhang Xin, the billionaire real estate developer who founded Soho China, one of the country's most successful real estate companies. The essence of her message: “I don't see any bubbles. … . The next few months will be a fantastic time to buy.”[60] If knowledgeable investors such as she are bullish on Chinese real estate, surely the uninformed will consider deferring to her judgment. In this way, large groups of investors can be led as a swarm into buying assets at perhaps exactly the wrong time. In addition to specialized media such as Bloomberg providing high-profile, consensus-generating stories, the publishing of popular media such as the Martin Jacques book can have a substantial effect on directing consensus beliefs. Perhaps the countrywide real estate obsession is driven by the fact that few Chinese real estate investors have experienced a bust. Might this collective (lack of) experience have generated herd-like behavior among the inexperienced?

On top of the “property is a sure thing” belief on the part of individual investors during this bubble was the “property is a sure thing” belief on the part of large, state-owned businesses in mundane industrial sectors. The New York Times reported in August 2010 that approximately 82% of all land auctions in Beijing had been won by big state-owned companies outbidding private real estate developers.[61] Based on Bank of International settlement data, this was the very peak of the Chinese property bubble. Between 2003 and the first quarter of 2010, Beijing real estate prices rose between 350% and 900%.[62]

In behavior similar to the 1980s Japanese bubble we examined in Chapter 8, Chinese defense equipment manufacturers, salt miners, railway groups, oil companies, chemical processers, shipbuilders, and telecom companies were all heavily active in the property development business in this period. Anhui Salt Industry Corporation developed Platinum Bay, a complex of luxury high-rise apartment condominiums. China Ordinance Group, a defense contractor, paid $260 million for land to develop, while Sino Ocean, a large state-owned shipbuilder, paid $1.3 billion for land in Beijing to build residential communities. By 2018, the South China Morning Post reported that the apartments built weren't exciting buyers.[63]

Finally, in a reflection of the national obsession with real estate, a TV series called Snail House airing on many major stations became “the hottest primetime soap opera in China.”[64] The primary theme of the show: real estate and the burdens of rapidly rising prices on a young family.[65] In a story titled “The Soap Opera of China's Housing Boom,” The Financial Times provided a quick summary:

The most talked-about television programme in China at the moment is a soap opera called Snail House, which offers the viewer sex, corruption, and political intrigue. Really, however, it is about house prices. One character becomes the mistress of a party official to help her buy a flat, while another young couple struggles unsuccessfully to raise the deposit for an apartment in a city that looks suspiciously like Shanghai …[66]

Given the immense popularity of the show and the potential it had to create social unrest, Chinese authorities took it off the air at the end of 2009[67] —thereby providing strong confirmatory evidence for the thesis that the housing frenzy had infected a large percentage of the population.

The Unsustainable Chinese Story

All of this slowly came to an end, as our five-lens approach suggested it would in the first edition of this book. By the start of 2011, the government was instituting policies to try to curb the easy credit fueling the property bubble, including raising the minimum down payment for second mortgages to 60% and banning the sale of homes to people who hadn't lived in Beijing for five years. Such measures seemed to cap the rise in borrowing to some extent: by the first quarter of 2012, the amount of residential mortgages outstanding remained at 16% of GDP, about even with 2010 levels.[68] The tightening behavior did negatively affect GDP, as 2012 GDP came in below the target rate of 8% growth and then slipped to a 26-year low of 6.7% by 2016.[69]

Predictably, too, the bust resulted in developers becoming as desperate to sell as the buyers once were desperate to sign on the dotted line. News reports were filled with tales of developers holding jumping contests with thousands of dollars in rebates as prizes to excite potential buyers, while real estate agents were reported to be getting into fist fights over customers.[70] As we noted earlier, the Chinese government reacted to the deflating of the bubble by reinflating housing markets—and then trying, as of this writing, to cool them again. The cycle of bubbles inflating and then bursting is a function of a central planning model attempting to stimulate and control demand and supply in the face of a shrinking working age populace in an economy with tight controls against capital flowing in and out. The ramifications on both China and the global economy from these reflexive fluctuations will continue to be dramatic.

In 2016, China made the landmark decision to reverse its one-child policy by allowing married couples to have two children, after over 30 years of restrictions. This move was spurred by the failure of previous attempts to stimulate childbirth and by the slowdown in the Chinese economy sparked by the property bubble. China's one-child policy created a demographic wave that is starting to crash on the economy.

Let's remember that there are three primary sources of growth in any economy: (i) labor, (ii) capital, and (iii) productivity. Labor-driven growth originates from adding more workers to an economy. Capital-driven growth comes from deploying more equipment. And last, productivity-driven growth is the result of squeezing more output from existing labor and capital.

Labor in China is unlikely to be a meaningful source of growth for the economy for decades to come. Young people entering the workforce—who are almost invariably only-children, due to the one-child policy—are already complaining about the pressures of taking care of aging parents on their own; they've been dubbed by the Chinese media “the loneliest generation.”[71] Estimates from the UN and the Population Reference Bureau suggest that the removal of the one-child policy will add 23.4 million extra people to the population by 2050 (Chinese officials put the number at 30 million).[72] Even so, with the dependency ratio on the rise, labor may soon emerge as an economic drag. And while technology offers the prospect of labor-replacing robots, that might exacerbate already-problematic domestic inequality, because the returns to capital would skyrocket as returns to labor were pinched. (If this transpires, it would be quite ironic to have the social fabric of a communist nation torn apart by capitalist tendencies.)

It's also not beyond the realm of possibility that the excess capacity caused by the property bubble may be due in part to a lack of buyers from this one-child policy. And as far as productivity, it is probable that much of the productivity growth fueled by urbanization has already happened. The most likely age band to migrate is 16–24 year-olds, and that age band shrunk by 25% between 2010 and 2015—another impact of the one-child policy. Third, China's hukou residency permit system classifies individuals according to where they live, not where they actually are (and there has already been lots of migration). Thus, there are those working in actual cities who are deemed rural, or working in factories but listed as farmers, and there are fewer potential migrants!

The Boombustology of China's Credit-Fueled Investment Boom and Bust

Does China's policy behavior lead to alternating bubbles? Prior to the property bubble detailed here, China's stock market experienced a bubble in 2006–2007, when the Shanghai Composite Index more than tripled in 13 months. It then burst and surrendered all the gains during the following 12 months ending October 2008. The appearance of another bubble in China so soon after a prior bubble is an interesting example of how reactions to one bubble lay the groundwork for the next.

In the years since, China's household debt has skyrocketed, reaching 49.1% of debt-to-income in 2017, up by one-fifth in the previous five years, according to a report by Allianz, a German-based insurance company. Consumption in China has been the economy's largest driver of growth in recent years, fueled in part by recent access to various forms of financing that consumers in the West have enjoyed for decades. Rising housing prices (again) are also encouraging consumers to take on more debt to afford a place to live, if they don't have one, and, for those who do own property, to spend more of their paper gains on luxury goods, as noted in a September 2018 report in the Financial Times.

Chinese laws, based on traditional beliefs that inflows into the country are good and outflows are bad, have long limited the overseas investment options available to wealthy Chinese citizens. As a result, two primary venues dominate Chinese thinking when the wealthy plan to invest: the domestic stock market and the domestic property market. It's not surprising after the bust of the Chinese equity market that investor interest in property grew dramatically between 2007 and 2011.

Christina Larson of the New America Foundation noted that “stock markets in China are unstable and immature, and there are few tax incentives for philanthropy. As a result, the wealthiest in China are faced with a problem unimaginable a generation ago: what to do with their money.” For many, the answer became real estate, an asset that appeared to be a store of value, at least until the massive overbuilding and speculation that emerged.

As noted throughout this chapter, Beijing's central-planning focus leads it to dictate policy and then have to respond in a manner that quickly deflates, be it encouraging and then discouraging property development, or endorsing outflows of capital to buy English soccer teams and then largely forbidding it. Even though China is a market-focused communist economy, it remains subject to the same tendencies toward disequilibrium that we noted in Chapter 4 the communist economies of Venezuela and the Soviet Union succumbed to.

The Multilens Look

The credit-fueled Chinese investment bubble of the period around 2007–2011 is an example that shows booms and busts can happen in political environments without representative government and with a restricted capitalist economic structure. In its efforts to create jobs and economic growth, the Chinese government created the conditions for the property bubble through tinkering with interest rates and credit and mortgage regulations, and by creating unintended incentives for state-owned entities to themselves speculate in property development. The problem was compounded by restrictions on capital flows, both domestically and internationally.

Table 11.1 summarizes the Boombustology of the Chinese credit-fueled investment bubble that led to a property boom and bust.

Table 11.1 The Five-Lens Approach to China's Property Boom and Bust

Lens

Notes

Microeconomic

Reflexive credit/collateral dynamic.

Mandated lending.

Rise in property prices and credit availability.

Macroeconomic

Local government funding vehicles to allow municipalities to borrow for development.

Negative real interest rates

Psychology

Conspicuous consumption in art, garlic, tea, and pigeons.

Overconfidence/Skyscraper indicator.

New era belief: When China Takes Over the World .

Politics

State-owned banks, mandated lending.

Supply of bank loans mandated by the state and controlled by regulatory fiat.

Down payment requirements fluctuating.

Biology

Snail House as prime-time TV hit.

Bartender chitchat, state-owned enterprise activity in real estate.

We have thoroughly explored bubbles and busts of the distant and more recent past. Let's now shift our focus to implementing our methodology in a forward-looking manner and then examining whether there are bubbles developing or in force in our world at this moment.

Notes

1. Andy Xie, “I'll Tell You When Chinese Bubble Is About to Burst,” Bloomberg (April 25, 2010).

2. Ibid.

3. David Barboza, “Market Defies Fear of Real Estate Bubble in China,” The New York Times (March 4, 2010).

4. Ibid.

5. Luo Jun, “Sizzling Shanghai Homes Defy Tax, Bubble Concerns.” Bloomberg News (December 17, 2009).

6. Andy Xie, “Fear Empties Flats in China's Property Bubble,” Caixin online (August 3, 2010), https://www.marketwatch.com/story/chinas-overhang-of-empty-apartments-2010-08-03.

7. Robert A. Mundell, “Capital Mobility and Stabilization Policy under Fixed and Flexible Exchange Rates,” Canadian Journal of Economic and Political Science 29, no. 4 (1963); Marcus J. Fleming, “Domestic Financial Policies Under Fixed and Floating Exchange Rates,” IMF Staff Papers no. 9 (1962).

8. Sebastian Edwards, “A Capital Idea? Reconsidering a Quick Fix.” Foreign Affairs (May/June 1999).

9. David Barboza, “Contrarian Investor Sees Economic Crash in China,” The New York Times (January 7, 2010).

10. Arthur Budaghyan, “Don't Fight the PBOC,” BCA Emerging Markets Research (November 16, 2010).

11. Kevin Hamlin, “Hugh Hendry Shorts China, Betting on 1920s Style Crash” Bloomberg Businessweek (November 26, 2010).

12. Charlene Chu, Chunling Wen, and Hiddy He, “Chinese Banks: No Pause in Credit Growth, Still on Pace with 1999,” FitchRatings (December 2, 2010); Charlene Chu, Chunling Wen, and Hiddy He, “Chinese Banks: Informal Securitization Increasingly Distorting Credit Data,” FitchRatings (July 14, 2009).

13. Ibid.

14. Michael Pettis, “QE2 and the Titanic,” http://mpettis.com.

15. Giovanni Ferri and Li-Gang Liu, “Honor Thy Creditors Before Thy Shareholders: Are the Profits of Chinese State Owned Enterprises Real,” Hong Kong Institute for Monetary Research (working paper, April 2009).

16. Gao Xu, “State Owned Enterprises in China, East Asia & Pacific on the rise,” http://blogs.worldbank.org/eastasiapacific/state-owned-enterprises-in-China-how-profitable-are-they.

17. Steel Statistical Yearbook 2009 (Brussels, Belgium: Worldsteel Committee on Economic Studies, 2010); Crude Steel Production by Month; www.worldsteel.org.

18. Kuni Chen, Jason Fairclough, Felipe Hirai, et al., Merrill Lynch Equity Research, Metals and Mining Industry Primer (April 22, 2010).

19. Pivot Capital Management, “China's Investment Boom: the Great Leap into the Unknown” (report, 2009).

20. Daniel F. Runde, “An Economic Crisis in Pakistan Again: What's Different This Time?” Center for Strategic & International Studies (October 31, 2018), https://www.csis.org/analysis/economic-crisis-pakistan-again-whats-different-time; Joe Bavier and Christian Shepherd, “Despite Debt Woes, Africa Still Sees China as Best Bet for Financing,” Reuters (August 30, 2018), https://www.reuters.com/article/us-china-africa/despite-debt-woes-africa-still-sees-china-as-best-bet-for-financing-idUSKCN1LF2RM; and Blake H. Berger, “Malaysia's Canceled Belt and Road Initiative Projects and the Implications for China,” The Diplomat (August 27, 2018), https://thediplomat.com/2018/08/malaysias-canceled-belt-and-road-initiative-projects-and-the-implications-for-china/.

21. Bill Powell, “Inside China's Runaway Building Boom,” Time (April 5, 2010), http://content.time.com/time/magazine/article/0,9171,1975336,00.html.

22. Wade Shepard, “An Update on China's Largest Ghost City - What Ordos Kangbashi Is Like Today,” Forbes (April 19, 2016), https://www.forbes.com/sites/wadeshepard/2016/04/19/an-update-on-chinas-largest-ghost-city-what-ordos-kangbashi-is-like-today/#5e0b5d4f2327.

23. Ibid.

24. Michael Donohue, “Mall of Misfortune,” The National (June 12, 2008), https://www.thenational.ae/world/asia/the-south-china-mall-of-misfortune-1.227884.

25. As noted in the PBS video Utopia, Part 3 (August 18, 2009), https://www.pbs.org/pov/utopia.

26. Ibid.

27. Johan Nylander, “Chinese ‘Ghost Mall’ Back from the Dead?” CNN (June 24, 2015), https://www.cnn.com/2015/04/28/asia/china-ghost-mall-return-to-life/index.html.

28. Donohue, “Mall of Misfortune.”

29. PBS, Utopia.

30. Yasheng Huang, “Chinomics: The Fallacy of the Beijing Consensus,” The Wall Street Journal (June 19, 2010).

31. Bijal Shah, “Why China's Growth Rate May Halve,” Cycles Strategy (MainFirst Bank AG research report, October 8, 2010).

32. W. Raphael Lam, Xiaoguang Liu, and Alfred Schipke, “China's Labor Market in the ‘New Normal,’” (IMF working paper, July 2015), https://www.imf.org/external/pubs/ft/wp/2015/wp15151.pdf.

33. “China's Chicago: A Giant City in the Southwest Is a Microcosm of China's Struggle to Move Millions from Rural to Urban Areas,” The Economist (July 26, 2007).

34. Lam, Liu, and Schipke, “China's Labor Market.’”

35. Yanrui Wu, “The Role of Productivity in China's Growth: New Estimates,” Journal of Chinese Economic and Business Studies 6, no. 2 (May 2008).

36. Wang Xia, Zhang Min, and Yu Fusheng, “Managerial Overconfidence and Over-investment: Empirical Evidence from China,” Frontiers of Business Research in China 3 no. 3 (2009), 453–469.

37. Souren Melikan, “Chinese Bidders Conquer Market,” The New York Times (April 2, 2010).

38. Andrew Moody, “Chinese Take the Art Market by Storm,” China Daily (May 24, 2010).

39. Ibid.

40. Ibid.

41. John F. Burns, “This Old Thing? It's Worth Only, Oh, Close to $70 Million,” The New York Times (November 12, 2010).

42. Rob Gifford, “Wild for Wine, China Pumps Up Demand, Prices,” National Public Radio (December 12, 2010), https://www.npr.org/2010/12/12/131973813.

43. Scott Reyburn, “Lafite Sets Auction Wine Record at $230,000 a Bottle,” Bloomberg Businessweek (October 29, 2010).

44. Tania Branigan, “Garlic Bubble Leaves Bad Taste in Chinese Mouths,” The Guardian (July 30, 2010), http://www.guardian.co.uk/world/2010/jul/30/garlic-chinese-commodity-bubble.

45. Peter Foster, “Tea Bubble Brews in China,” The Telegraph (October 1, 2010).

46. Andrew Jacobs, “Jade from China's West Surpasses Gold in Value,” The New York Times (September 20, 2010).

47. Robert Frank, “Pay $200,000 for a Pigeon?” The Wall Street Journal (January 15, 2011).

48. “Reds to Buy the Reds? Chinese Government Backs Bid to Take Over Liverpool FC,” The Daily Mail (August 5, 2010); https://www.dailymail.co.uk/news/article-1300558/Communist-China-funds-bid-buy-Liverpool-Football-Club.html.

49. Associated Press, “Chinese Investors to Buy Into Cavs” (May 24, 2009), http://www.espn.com/nba/playoffs/2009/news/story?id=4202661.

50. Ezra Vogel, Japan as Number One: Lessons for America (New York: Harper Collins, 1983).

51. Tim Wang, “Whither China's Construction Boom?” Forbes (May 21, 2009).

52. “Beijing Tells the Provinces to Slow Down” Geopolitical Diary (Stratfor.com email, January 7, 2011).

53. Ibid.

54. George Friedman, The Next 100 Years: A Forecast for the 21st Century (New York: Anchor Books, 2010).

55. Victor Shih, “Moral Hazard and China's Banks: Beijing Could Face Its Own Banking Crisis Unless More Market Discipline Is Introduced,” The Wall Street Journal Asia (June 29, 2010).

56. Ibid.

57. Jinsong Du et al., China Property Policy Outlook 1: “Property Tax: Would it Crash the Market?” Credit Suisse Hong Kong Ltd. (June 14, 2010), https://www.reuters.com/article/us-china-property-poll/china-house-prices-to-rise-faster-in-2018-in-boost-for-cooling-economy-reuters-poll-idUSKCN1LQ0TA.

58. Dominique Fong, “Smaller Cities Are a Big Worry in China's Too-Hot Property Market,” The Wall Street Journal (August 15, 2018), https://www.wsj.com/articles/smaller-cities-are-a-big-worry-for-chinas-too-hot-property-market-1534327255.

59. Mark Fahey and Eric Chemi, “Three charts explaining China's strange stock market,” CNBC (July 9, 2015), https://www.cnbc.com/2015/07/09/three-charts-explaining-chinas-strange-stock-market.html.

60. William Mellor, “Beijing Billionaire Who Grew Up with Mao Sees No Housing Bubble,” Bloomberg Markets (August 2010).

61. David Barboza, “State-Owned Bidders Fuel China's Land Boom,” The New York Times (August 1, 2010).

62. Ilan Noy, “China's Real Estate Bubble.” UHERO (November 22, 2010).

63. Zheng Yangpeng, “New Beijing Price-Capped Homes Fail to Excite Homebuyers,” South China Morning Post (August 1, 2018), https://www.scmp.com/property/hong-kong-china/article/2157619/new-beijing-price-capped-homes-fail-excite-homebuyers.

64. Scott Tong, “Chinese Soap Opera Highlights Housing Issue,” National Public Radio (January 19, 2010), https://www.marketplace.org/2010/01/19/world/chinese-soap-highlights-housing-issue.

65. Su Feng, “Hit TV Series Strikes Chord with China's ‘House Slaves,’” The Wall Street Journal (November 26, 2009), http://blogs.wsj.com/chinarealtime/2009/11/26/hit-tv-series-strikes-chord-with-chinas-house-slaves.

66. Geoff Dyer, “The Soap Opera of China's Housing Boom,” Financial Times (January 6, 2010).

67. Ibid.

68. James R. Barth, Michael Lea, and Tong Li, “China's Housing Market: Is a Bubble About to Burst?” Milken Institute (report, December, 2012), https://assets1c.milkeninstitute.org/assets/Publication/ResearchReport/PDF/China-HousingMarket.pdf.

69. Annual data, National Bureau of Statistics of China, http://www.stats.gov.cn/english/Statisticaldata/AnnualData/.

70. Michael Schuman and Chencheng Jiang, “China's Great Property Boom May Be Coming to a Desperate End,” Time (May 15, 2014), http://time.com/98814/china-property-apartments-developers.

71. Javier C. Hernández and Amy Qin, “End of China's One-Child Policy Stings Its ‘Loneliest Generation,’” The New York Times (November 13, 2015), https://www.nytimes.com/2015/11/14/world/asia/china-one-child-policy-loneliest-generation.html.

72. Chris Buckley, “China Ends One-Child Policy, Allowing Families Two Children,” The New York Times (October 29, 2015), https://www.nytimes.com/2015/10/30/world/asia/china-end-one-child-policy.html.

PART III

Looking Ahead

Part I presented various disciplinary lenses through which to evaluate financial extremes, and Part II illustrated the use of those lenses on several historical cases. Part III brings together the lenses of Part I and the case study evidence of Part II into a framework for identifying bubbles before they burst. The real power and usefulness of the multi-lens approach lies in its application to unresolved mysteries, and so the last chapter evaluates the sustainability of the Indian economic boom

CHAPTER 12

Spotting Bubbles Before They Burst: A METHOD FOR IDENTIFYING UNSUSTAINABLE BOOMS

When a person with money meets a person with experience, the person with experience winds up with the money and the person with the money ends up with the experience.

—Harvey MacKay

Although studying booms and busts provides significant fodder for academic discussions and intellectual debates, it is perhaps most useful if it helps us to make money, avoid losses, or, ideally, both. In order to be useful, then, our study of booms and busts must provide tools for proactively understanding if we are investing in an unsustainable boom with the corresponding risks of an imminent bust. As mentioned in the preface, this book does not provide a map of the market. It also does not attempt to provide any insight into market timing. Rather, it provides a probabilistic framework for understanding the likelihood of being in a bubble. The underlying belief of this approach is that, although asset markets may be well behaved and efficient most of the time, they do on occasion stray to extremes. These extremes matter a great deal, and this chapter provides a seismograph to identify the tremors that precede a quake.

Many an academic has made a career out of explaining events from a historical perspective. Few practitioners have had the luxury of living in a world of 20/20 vision comparable to that provided by hindsight. As such, this chapter provides a tool—in the form of a checklist—that individuals and institutions might use to spot bubbles before they burst. Findings of the previous chapters have been coalesced into a framework to recognize unsustainable booms.

The booms and busts discussed in Part II demonstrate many similar characteristics. Most exhibited reflexive dynamics, excessive and unsustainable leverage, overconfidence and biased decision-making, policies distorting price-discovery processes, and herdlike behavior. This chapter summarizes each of these characteristics and concludes with a checklist of the common indicators associated with being in the midst of bubble.

Reflexivity and Self-Fulfilling Dynamics

The first lens presented in Chapter 1 focused on the forces of supply and demand and questioned whether they generate an equilibrium. This physics-inspired approach to microeconomics suggests that supply and demand meet to create a stable price. A rise in prices generates supply, which in turn offsets the price rise. Likewise, similar logic suggests that a fall in prices generates demand, which provides upward pressure on prices. Chapter 1 concluded that although supply and demand dynamics usually generate a stable equilibrium, they occasionally do not. There are instances in which higher prices stimulate additional demand, not additional supply—a situation that often characterizes a boom. Likewise, busts might be characterized by situations in which lower prices stimulate additional supply, not demand. The theory of reflexivity provides an alternative to the equilibrium-oriented efficient market hypothesis.

The five Cs framework captures the essence of the reflexive dynamics that dominate most boom and bust sequences. By definition, booms and busts are events that deviate significantly from equilibrium, and it is often highly likely that reflexive dynamics are responsible for these deviations. Because the five Cs framework has broad applicability to the study of booms and busts, we instead focus in this section on more specific reflexivity signposts that involve an interplay with credit and leverage—namely, the prevalence of asset-based lending and the simultaneous growth of credit volumes and asset prices.

In the case studies presented, the most prominent examples of reflexive dynamics at work involved the self-reinforcing, pro-cyclical dynamic between credit and collateral. This often occurred in times of extreme optimism when lenders modified their lending criteria from income-oriented toward asset-focused approaches. When the primary criterion for extending credit switches from income-based affordability to collateral value, watch out. This is a spectacular early warning sign of a powerful reflexive dynamic being unleashed.

Because increased collateral values inspire more credit, reflexive dynamics can often be identified by the concomitant growth of credit and collateral values. If credit is rising rapidly along with asset prices, there is a high probability that reflexive dynamics are under way. These dynamics were prominent in the Florida, Japan, U.S. housing, and China property cases studied in previous chapters. As these dynamics unfolded, they were accompanied by a similar boom in confidence at banks as they felt they were increasingly secure in their loans, not realizing that when the music slowed (if not stopped), then they would suddenly find that they were less intelligent than they themselves once thought.

A change in lending standards and a simultaneous growth in lending and asset prices are both indicators of what might also be termed easy or loose money. Let's review selected events that transpired in the cases to evaluate the prevalence of reflexive dynamics and their interactions with loose money.

Tulipomania

The tulip mania discussed in Chapter 6 exhibited many of the characteristics typical of a reflexive dynamic. Higher prices generated more demand, and, following the bursting of the bubble, lower prices generated more supply. Aside from these price-based indicators of reflexive dynamics, there appears to have been the possibility of foreign inflows (i.e. hot money) into the Netherlands as it became the financial center of the world. Foreign capital inflows seeking higher returns often cause the very returns they are seeking, thereby creating a very unstable situation.

The Great Depression

The Florida land boom that preceded the Great Depression provides a spectacular example of the two reflexivity indicators in action. As is typical in most property-related booms, credit provided the fuel for the asset boom with banks. Liquidity was rampant and credit ubiquitous. Remember the total U.S. money supply during the 1920s? We can also note that credit was expanding at the same time that asset prices were rising, and although evidence on the methodology of bank approvals is hard to come by, it seems conceivable that banks were finding comfort that as asset prices rose, the collateral pledged against their loans made the loans (seem) less risky. It was only once the music stopped that banks realized their comfort was unwarranted.

The Japanese Boom and Bust

Given that land was the centerpiece of the Japanese boom and bust sequence, it should come as no surprise that reflexive credit and collateral dynamics were present. The mere existence of mortgage products with a 100-year term (see the following section on financial innovation) indicates that leverage was being extended on terms other than ability to repay, because banks could not have reasonably expected individuals to live long enough to repay their loans. Many of these loans simply must have been extended by banks operating under the assumption that asset values would protect them against this inability to repay the loan from income. Further, Japanese credit grew rapidly during the 1980s, simultaneous with the boom in asset prices. It seems highly unlikely that this was coincidental.

The Asian Financial Crisis

The Asian financial crisis was in many ways a story of hot money inflows and outflows. The reflexive dynamic created by such money flows was not dissimilar to those generated in other unsustainable booms. Money came into East Asia seeking higher returns, but the very arrival of such money at least partially created those returns. As long as the flows continued, the story remained intact. Once the money flows stopped and reversed, the virtuous cycle rapidly turned vicious. This is exactly the dynamic that took place in Thailand and the rest of Southeast Asia during the Asian financial crisis.

The U.S. Housing Boom and Bust

The housing boom that took place in the early-to-mid 2000s in the United States definitively exhibited telltale signs of reflexive dynamics. To begin, the multitude of mortgage innovations that took place were specifically designed to extend leverage and mortgage power to those who had been previously unable to access such credit. Such innovations included subprime, Alt-A, and NINJA (No-Income, No-Job or Assets) loans. Securitization (see the following financial innovation section) was the enabling force behind banks’ willingness to look beyond historical warning signs (i.e. bad credit in the case of subprime and Alt-A mortgages, or unemployment in the case of NINJA loans) of potential losses. By not having to (directly) worry about the ramifications of extending inappropriate credit, banks felt empowered to change the historical basis upon which mortgages were extended. Additional confidence was gained by rapidly rising collateral values. Further, it should come as no surprise that the amount of outstanding credit rose alongside asset values.

The Chinese Investment Bubble

The Chinese boom profiled earlier exhibited classic reflexive dynamics in that collateral values rose simultaneously with lending, creating a credit-fueled investment bubble. Rapidly rising housing prices emboldened bankers and others to continue lending, giving them confidence that rising mortgage values were not generating any additional credit risk. What they failed to realize was that their lending was in fact the primary driver of the rising housing prices. In a related—and disturbingly comical—dynamic, developments in the steel industry captured the reflexivity of asset bubbles almost perfectly: the construction of steel mills increasingly became a driving source of demand for steel.

Red Flags of Reflexivity

Two of the primary criteria for identifying the onset of destabilizing reflexive dynamics in asset markets thus seem to be the modification of the standards by which credit has historically been granted (i.e. affordability) toward collateral-based lending and the simultaneous growth in credit and asset values. A third indicator is the presence and growth of hot-money inflows seeking outsized returns, which usually enable the very returns sought. Table 12.1 summarizes these indicators.

Table 12.1 Signs Revealing Reflexive Dynamics Toward Disequilibrium

Indicator

Examples

Change in Criteria Used to Evaluate and Extend Credit from Income-Oriented Toward Collateral-Focused

100-year mortgages issued during the Japanese property boom

Concomitant Growth in Credit and Collateral Value

Simultaneous boom in mortgage values and housing prices during the U.S. housing boom and Chinese property boom

Hot Money Inflows Seeking (and creating) Outsized Returns

Foreign capital flowing into the Netherlands; Foreign capital flowing into Thailand; Bitcoin drawing speculative investments

Leverage, Financial Innovation, and Cheap Money

Chapter 2 introduced several nontraditional macroeconomic approaches to thinking about booms and busts. Specifically, the dynamics of debt and the ability of a borrower to repay form the heart of Hyman Minsky's financial instability hypothesis. The nasty impact of deflation on debt (increasing the real burden of debt) was then considered before evaluating the Austrian school of economics and their “cheap money as the root of all evil” explanation for boom and bust cycles.

A common adage about banking and leverage captures the spirit of the boom and bust dynamic quite eloquently: “If you owe the bank $100 and can't pay, then you have a problem. If you owe the bank $100 million and can't pay, then the bank has a problem.” Financial innovation, which often creates effective leverage, is an enabling culprit in the credit game and, when combined with cheap money, creates a dynamic that is particularly fragile and prone to instability. Credit is inherently destabilizing, and misunderstood credit can be lethal. In a recent article titled “Financial Innovation and Financial Fragility,” Nicola Gennaioli and two colleagues noted the following sequence of events through which financial innovation can become destabilizing:

Many recent episodes of financial innovation share a common narrative. It begins with a strong demand from investors for a particular, often safe, pattern of cash flows. Some traditional securities available in the market offer this pattern, but investors demand more (so prices are high). In response to demand, financial intermediaries create new securities offering the sought after pattern of cash flows, usually by carving them out of existing projects or other securities that are more risky. By virtue of diversification, tranching, insurance, and other forms of financial engineering, the new securities are believed by the investors, and often by the intermediaries themselves, to be good substitutes for the traditional ones, and are consequently issued and bought in great volumes.

At some point, news reveals that new securities are vulnerable to some unattended risks, and in particular are not good substitutes for the traditional securities. Both investors and intermediaries are surprised by the news, and investors sell these “false substitutes,” moving back to the traditional securities with the cash flows they seek. As investors fly for safety, financial institutions are stuck holding the supply of the new securities (or worse yet, having to dump them as well in a fire sale because they are leveraged). The prices of traditional securities rise while those of the new ones fall sharply.[1]

Although this quote is clearly referencing the AAA-rated debt derivatives that were a false substitute for U.S. Treasuries, we should not lose track of the fact that financial innovation has the ability to create instability. Indeed, whether it was the development of futures contracts in 1630s Holland or the sliced-and-diced mortgage-backed securities of the 2000s makes no difference: financial innovation often embeds or conceals leverage.

Tulipomania

The structure of the tulip market in the 1630s was one based on futures. Because the actual tradable bulb market only existed for several months, most trading that took place in tulip bulbs took place in derivative markets that effectively enabled (via down payments that enabled control of larger values) leverage. Richard Bookstaber, while reviewing the perils of financial innovation, notes that the Tulipomania “reached full bloom only with the innovation of forward contracts and the leverage these contracts afforded, which allowed traders to buy and sell commodities they did not own, had no intention of owning, and indeed did not even have the money to purchase outright.”[2] Further, ubiquitous money and liquidity (see Table 6.1) provided monetary fuel for tulip prices.

The Great Depression

Here again, the use of effective leverage was prevalent. It was the “pay only 10% to get economic exposure to 100% of an asset” mindset. By simply putting down a deposit that was 90% less than the actual price, speculators in Florida were able to achieve 10× leverage on the price movement of the properties they had supposedly committed to purchasing. Not dissimilar to the Bookstaber quote, it was later revealed that many of these properties were held by individuals who had no intention of owning them and likely lacked adequate resources to own them outright. The result, like the events that had transpired almost 290 years prior, was identical. The house of cards, which was built on a precarious foundation of extreme leverage, eventually imploded.

In a series of articles published in the Saturday Evening Post, Garrett described the bubble of the 1920s as follows: “An ephemeral, whirling, upside-down pyramid, doomed in its own velocity. Yet it devours credit in an uncontrollable manner, more and more to the very end; credit feeds its velocity.”[3] From brokerage margin provided to eager speculators to credit fueling the purchases of automobiles, radios, and other new devices, much of the boom of the 1920s took place with borrowed funds. Ultimately, as noted in “Whirlwinds of Speculation,” a 1931 article published in The Atlantic magazine, the credit fueled its own demise by producing a supply response in the “units of speculation” (i.e. stocks, bonds, and developments in Florida), creating the inevitable crash.

Finally, the lowering of interest rates in 1925 to assist the British in fighting the outflow of gold from their country was unintended fuel on top of the already-existing speculative fire. This spurred credit growth, and by the early 1930s, U.S. existing credit to GDP reached an all-time high of 299%.[4]

The Japanese Boom and Bust

Macroeconomic policy during the Japanese boom was marked by tremendous growth in credit. In fact, it has only been in the years following the bust that the magnitude of the credit overhang has been made patently evident. By encouraging the use of credit to facilitate asset-gain wealth effects, the monetary authorities of Japan were hoping to alleviate the pain that would likely be felt by exporters due to currency appreciation. The result was a cheap-money credit explosion that resulted in the final vertical ascent of Japanese asset markets.

The lethal combination of debt and deflation has since plagued the island nation and resulted in what has been labeled by Richard Koo as a “balance sheet recession.”[5] Traditional economic incentives like profit maximization were effectively brushed aside in a quest for balance sheet repair. This act of deleveraging generated a reflexive dynamic of its own, in which debt deflation firmly took hold.

The Asian Financial Crisis

Perhaps the most dramatic innovation that enabled the inflow of portfolio investments into Southeast Asia was the emergence of emerging-markets funds. As highlighted in Chapter 9, the fact that increasing pools of global capital were organized into common funds focused on emerging economies enabled the rapid transmission of economic hardships in one country into capital flight from another. As fund managers began taking significant losses in Thailand, they found themselves overly exposed to other countries and hence began selling them as well. Further, redemptions by the fund's investors led to a general selling pressure across all emerging markets as fund managers indiscriminately sold stocks in an effort to meet requests for the return of capital.

Although the topic of cultural homogeneity and crony capitalism will be touched on later, it is worth noting here that the development of financial intermediaries such as the Thai finance companies spurred the disbursement of credit. The crony capitalism that led to inefficient and risky lending made for the ultimate in moral hazard. Banks and others freely lent inappropriate amounts of capital to connected individuals with the belief that relationships in the government would ultimately protect them. Thus, capital flowed more freely than it otherwise might have.

The U.S. Housing Boom and Bust

If we had to focus on only one cause of the U.S. housing boom and bust, it would likely be the ease of leverage and credit that enabled a slew of individuals—unable to afford the homes they purchased—to enter the market and thereby bid up the prices of real estate. There were many contributing factors to this plot, but central to it are the innovations in securitization and collateralization that created the mortgage-backed securities, collateralized debt obligations, and a host of other credit-derived securities that were served up to yield-hungry global investors seeking the comfort of highly rated and supposedly safe securities.

Another enabling factor in the housing boom was the inappropriately low interest rates that were in effect to combat the ramifications of the internet bubble bursting. By keeping rates extraordinarily low for an extended period of time, the U.S. Federal Reserve unintentionally fueled the next bubble, a far larger and wide-reaching credit-fueled housing boom with significantly greater ramifications.

Finally, the predominant role played by government-sponsored enterprises such as Fannie Mae and Freddie Mac in the housing markets created a sense of comfort on the part of those buying housing securities guaranteed by these organizations. After all, the U.S. government would not allow a default on mortgage-backed securities, would it?

The Chinese Investment Bubble

Mispriced money often leads to misallocated capital, and by many measures, Chinese money was mispriced. Extraordinarily cheap money was the result of pegging the yuan to the U.S. dollar. This dynamic created powerful incentives to purchase real estate and dive into more speculative investing with second and third homes. The desire of central planners not to cause a shock to the economy allowed the period of easy credit to extend itself, allowing prices to go ever higher. In a classic sign of macroeconomic misallocation, borrowed money was used to construct additional capacity in industries that already suffered from overcapacity. Empty malls, ghost cities, and underutilized assets in many industries captured this dynamic.

Signs of Unsustainable Credit Conditions

The signposts for unsustainable credit conditions fall under three primary indicators: financial innovations that enhance or enable leverage, inappropriately (and often unintended) cheap money, and the issuance of credit to non-creditworthy borrowers due to implicit government guarantees (moral hazard–inspired lending). See Table 12.2 for a summary.

Table 12.2 Signs Revealing Leverage Excesses

Indicator

Examples

Financial Innovation(s) that enhance or enable leverage

Forward contracts for tulips, 10% down binders during the Florida land boom, 100-year mortgages in Japan, pooled funds for diversified emerging markets investing, securitization and the boom in credit derivatives

Cheap/Excessive Money

1925 lowering of interest rates to facilitate British gold retention, Japanese-engineered asset boom to mitigate currency-driven export slowdown, lowering rates to combat bursting of the internet bubble, riskless return below the rate of inflation

Moral Hazard–Motivated Lending

Crony capitalism, Fannie/Freddie, state-owned enterprises shifting in property development, incentivized GDP targets

Skyscrapers as a Bubble Indicator

One of the first skyscrapers was designed and built by Bradford Lee Gilbert in 1887. It was designed to solve a problem of extremely limited space resulting from the ownership of an awkwardly shaped plot of land on Broadway in New York City. Gilbert chose to maximize the value (and potential occupancy) of the small plot by building vertically. His 160-foot structure was ridiculed in the press, with journalists hypothesizing that it might fall over in a strong wind. Friends, lawyers, and even structural engineers firmly discouraged the idea, warning that if the building did fall over, the legal bills alone would ruin him. To overcome the skepticism of both the press and his advisors, Gilbert took the top two floors for his personal offices.[6] From then on, the skyscraper has been a symbol of economic and financial success, the mark of one's ascent.

It has also been one of the most robust bubble indicators over long time periods—specifically, the world's tallest skyscraper has been. You may be aware of the Burj Dubai's ascent as the world's tallest structure, and the corresponding global credit crunch that soon followed, but few are as familiar with the consistency of the pattern. Consider the following table, which lists the world's tallest skyscrapers (at the time) and the accompanying financial crisis that struck the market in which it was built.

World's Tallest Skyscrapers and Related Busts

Building

Location (Completed)

Spire Height

Financial Crisis

Singer

New York (1908)

187 meters

Panic of 1907

Metropolitan Life

New York (1909)

247 meters

Panic of 1907

40 Wall Street

New York (1929)

283 meters

Great Depression

Chrysler

New York (1929)

319 meters

Great Depression

Empire State

New York (1931)

443 meters

Great Depression

World Trade Center

New York (1973)

526 meters

70’s Stagflation

Sears Tower

Chicago (1974)

527 meters

70’s Stagflation

Petronas Towers

Kuala Lumpur (1997)

452 meters

Asian Financial Crisis

Taipei 101

Taipei (2004) [*]

509 meters

Tech Bubble

Burj Dubai

Dubai (2008/9) [**]

828 meters

Global Credit Crunch

Jeddah Tower

Jeddah (2020e)

1000 meters

?

Dubai Creek Tower

Dubai (2021e)

1300 meters

?

[*] Taipei 101 was financed and construction began in 1999, quite near the peak of the technology boom.

[**] It is interesting to note that the uncompleted Burj Dubai tower was classified as the world's tallest structure on July 21, 2007, right around the peak of the U.S. market before the financial meltdown.

Why might this indicator be so consistently useful? While there are many likely reasons, three seem particularly striking.

First, skyscrapers are inherently speculative ventures, in that they are rarely, if ever, built by their intended occupants or with committed tenants. “Build it and they will come” captures the prevailing spirit.

Second, because speculators rarely build such structures with their own money, skyscrapers are powerful evidence of “easy money.” Banks and lenders must have faith in a better future, one in which the developer will be able to repay the loan.

Third, we've been around long enough and have built high enough that to be the “world's tallest” is a spectacular act of hubris, if not overconfidence. I often joke that the view from the top of the Middle Eastern skyscrapers is the same, regardless of height. It's sand, followed by water. Building higher is really about saying one is the world's tallest.

These three reasons are reflective of a fertile context for bubble formation, making the world's tallest skyscrapers a spectacular indicator of bubbly conditions. The economist Mark Thornton eloquently summarized the context surrounding the construction of the world's tallest skyscrapers: “First, a period of easy money leads to a rapid expansion of the economy and a boom in the stock market … credit fuels a substantial increase in capital expenditures … [and] this is when the world's tallest buildings are begun.”[7]

While the thought of the Burj Khalifa (it was renamed after the financial crisis) being surpassed in height may seem irrational, we can look at the tallest skyscrapers under construction to see where money is easiest, speculative juices are flowing, and confidence is high. The source I prefer for future tall buildings is Skyscraperpage.com, a site that tracks the world's tallest buildings in the future—based on current building plans. In 2011, when the first edition of this book was published, five of the 10 tallest buildings under construction were in China. The site estimated then that Chinese skyscrapers would occupy spots 2, 3, 5, 9, and 10 of the tallest buildings in the world by 2015. At the time, this was another sign of risks in the Chinese credit-fueled investment boom.

Looking forward from 2018 toward 2022, we find that the Burj Khalifa is, as of this writing, supposed to be outdone by a tower in China (Suzhou Zhongnan Center, a project that was, in late 2018 still “on hold”), which in turn is expected to be surpassed by the Jeddah Tower in Saudi Arabia, a skyscraper expected to be 1km tall and completed by 2020. Not to be outdone by their Arabian neighbors, the UAE is scheduled to retake the title with the planned 1.3km Dubai Creek Tower. Whoa! What does this mean for the riskiness of the Middle East? Might economic or other chaos be forthcoming? The dynamic is not unlike what transpired in the late 1920s in the United States, in which three towers in NYC competed for the world's tallest title … and what followed was the Great Depression.

Despite the allure of “it's different this time” explanations of why every skyscraper and each time is unique, it sure seems that construction of the world's tallest skyscrapers is reflective of bubbly conditions. With respect to Saudi Arabia and the Middle East more generally, caution seems warranted. The Boombustology seismograph is generating lots of activity, indicating that a forthcoming quake may be imminent. Investors and policymakers alike should exercise extreme caution, because chances are high that it's probably not different this time.

Overconfidence

Chapter 3 evaluated several decision-making biases that consistently impact rational behavior. Several rules of thumb were considered, and other forms of individual-level behavior that appear contrary to economic theory were evaluated. Overconfidence surfaced as perhaps the most relevant of the decision-making issues that emerge as a product of anchoring, insufficient adjustment, and reliance on the availability and representativeness heuristics.

Among the manifestations of overconfidence, perhaps the most revealing is the belief in the onset of a new era where “this time is different” pervades popular sentiment. This new-era belief often gains traction because there is believability to the story that things are genuinely different. Classic manifestations of this new-era inspired confidence often include conspicuous consumption and the purchase of trophy assets and art.

Tulipomania

The 1600s in Amsterdam felt like they were indeed the beginning of a new era. As noted in Chapter 6, the political economic context supported such a view, and the virtual domination of world trade by the Dutch basically confirmed this new-era belief for even the most ardent of doubters. Holland had just won a hard-fought war, was enjoying fantastic economic gains from the redeployment of resources from military pursuits to commercial endeavors, and was now reaping the economic rewards of a dominating position in world trade. This perspective, combined with the conspicuous display of rare bulbs and the broad participation of amateur investors, provided ample evidence of a broad, Holland-wide (over)confidence in the existence of a new era. Indeed, to many participants and observers, the time seemed to have come for a Dutch domination of the world in matters of economic and non-economic consideration. Why wouldn't the world's wealthy flock to Amsterdam to procure bulbs of the most beautiful flower?

The Great Depression

The Western world had just won World War I, and new technologies were promising a new era in American convenience and modern living. Just as the war had accelerated the mobilization of rural resources into industrial efforts, the ending of the war resulted in the redeployment of resources from military to economic pursuits. Automobiles and radios, both newly accessible by the presence of previously unavailable consumer credit, promised a different life in the years and decades ahead. Roads were connecting previously faraway places, and a burgeoning aerospace industry enabling human flight was being formed. Lindbergh's solo crossing of the Atlantic Ocean made this new-era belief all the more real.

In a spectacular manifestation of the confidence (and hubris) of the times, speculative and competitive juices were flowing rapidly in the development of New York City's skyscrapers. Shortly after 40 Wall Street was completed and deemed the world's tallest tower, the Chrysler building chose to erect a spire tall enough to claim the title. Not to be outdone, the Empire State Building opened shortly thereafter, transferring the “world's tallest tower” title for a third time in less than three years.

The Japanese Boom and Bust

The postwar economic miracle exhibited by the transformation of a completely devastated Japan into a world-leading economic superpower provided the new-era belief so representative of overconfidence. The comparisons of real estate values in Japan and the United States, several of which were detailed in Chapter 8, highlight one manifestation of this confidence.

Trophy asset purchases such as those described in Chapter 8 also typify the extreme overconfidence that accompanied the Japanese economic success story. Paying hundreds of millions of dollars more than asking price for a trophy building in Manhattan simply to get into the Guinness Book of World Records is the quintessential embodiment of overconfidence and hubris. Equally reflective of the times was the Japanese role in driving the art market to new heights.

The 1979 publishing of Harvard professor Ezra Vogel's book Japan as Number One provided an argument that a new era of prosperity was coming. Although the book seemed correct in the 1980s, it has since come to represent folly. Interestingly, Jon Woronoff published a compelling Japan as (Anything But) Number One in 1991, very close to the top of the Japanese bubble. But as is so often the case in situations of overconfidence, contrary evidence to the prevailing “wisdom” was not well received at the time.

The Asian Financial Crisis

For a decade-long period leading up to the mid-1990s, Thailand was one of the fastest-growing economies in the world. Similar dynamics took place across many of the Southeast Asian emerging economies. Despite the fact that much of this increase in economic output came from a growth in inputs, virtually no commentators (Paul Krugman being a notable exception) were willing to question the region's economic sustainability. Popular books on the subject included the World Bank's The East Asian Miracle and Ezra Vogel's Four Little Dragons. With such global infatuation with the export-led development model and the success of Southeast Asia, overconfidence was effectively preordained.

Another manifestation of overconfidence was the rampant and widespread currency mismatches that left Thai (and other) borrowers extremely vulnerable to currency movements. By earning in local currency but borrowing in U.S. dollars (or other currencies), currency depreciations created a debt–deflation-like dynamic in which the effective (i.e. local currency–valued) amount of debt ballooned. The dynamics that inspired so many borrowers to undertake such a risk are at least partially clarified through a behavioral decision-making lens. As noted in Chapter 9, perhaps the likelihood of currency moves was underestimated due to a lack of recently available data regarding adverse currency moves (i.e. availability bias). Or perhaps the analogy of another country borrowing in a different currency proved more vivid (i.e. representative bias). Regardless, it does seem that borrower beliefs underweighted the probability of adverse outcomes.

As a final indicator of overconfidence (and easy money), Malaysia took the crown for the world's tallest skyscraper with the completion of the twin Petronas Towers in Kuala Lumpur in 1997.

The U.S. Housing Boom and Bust

Aided in great part by low-cost financing and increasingly flexible mortgage terms, many homebuyers and investor participants in the U.S. housing boom and bust were operating under the false belief that “real estate prices don't fall.” Despite the elevated levels of objective measures of value (measures such as median price to median income, percent of income for debt service, etc.), many borrowers (and lenders, for that matter) failed to note the rising risks.

Consider the art market of 2006, thought by many to be the year during which the U.S. housing market peaked. The three highest prices ever paid for paintings took place during 2006 art auctions. In June 2006, cosmetics magnate Ronald Lauder paid a record-breaking $135 million for a Gustav Klimt painting. Only months later, a new record was set when U.S. hedge fund mogul Steven A. Cohen of SAC Capital paid roughly $137.5 million for a de Kooning painting titled “Woman” in November 2006.[8]

The Chinese Investment Bubble

The emergence of China has been a hotbed of new-era thinking, with a belief that its population and potential made it immune to the gravity that prevents economies from growing indefinitely. And the very fact that China self-describes its centrally planned economic system as “Capitalism with Chinese Characteristics” points to a new-era philosophy of suggesting “it's different this time.”

Yet we know that China is still prone to bubble-and-bust dynamics. The book When China Rules the World is an example of exterior belief in this narrative.[9] Internally in China, overconfidence was fed by the belief the government could simply mandate higher asset prices. It showed itself in the rapacious hunger for art collecting, wine purchases, trophy purchases such as stakes in basketball and soccer teams, and the planning and building of a number of world's tallest buildings.

China's dominance in the art markets was clearly visible in 2011 when Asian Art Week in New York City resulted in estimated auction prices being shattered. In one particularly stunning example, a vase estimated to sell for around US$1000 sold for millions of dollars as Chinese buyers bid up the price.

It's Different This Time

Table 12.3 provides a summary of ways in which overconfidence can signal the presence of an unsustainable boom.

Table 12.3 Overconfidence Indicators

Indicators

Examples

Conspicuous Consumption

Trophy purchases, record art prices, world's tallest skyscrapers, Lamborghinis at cryptocurrency conferences

New-Era Thinking

War victories (Spain, WWI), economic champion status (Japan, East Asian miracle, China), technological developments (radio, car, airplane, blockchain)

Policy-Driven Distortions

Chapter 4 introduced a political lens. Two big-picture issues dominated the discussion: property rights and the price mechanism. After establishing the need for protected property rights, the chapter considered the ways government actions meddle with the price mechanism—namely, mandated prices and tax distortions affecting buy/sell decisions. Price ceilings and floors were also addressed.

Most tax or price-affecting policies are meant to incentivize a certain politically desirable behavior. Unfortunately, most policies are accompanied by unintended consequences, many of which distort the price mechanism and therefore negatively affect the stability of asset markets. Three primary signposts highlight an increased likelihood of an unsustainable boom. The first, moral hazard, effectively creates a dynamic in which lenders are willing to underweight the risk of failure because they believe (rightly or wrongly) that governments do not have the political will to allow the failure of certain key stakeholders. The second involves active government manipulation of supply and demand dynamics via price controls, tax policies, and/or direct government actions. The third indicator, shifting regulations, is usually a sign of flux in the rules of play vis-à-vis business that allows for the emergence of a new paradigm and industry structure by destabilizing established industrial patterns.

Let's now turn to each of the cases to briefly consider the policy distortions that manifested themselves as the unsustainable boom was under way.

Tulipomania

Recall from Chapter 6 that the political dynamic of having prominent public officials suffering substantial personal financial losses led to a modification of the “rules” and the proposed conversion of futures contracts (in which the buyer had purchased the asset for future delivery) into call options (in which the buyer had the right but not the obligation to purchase the asset in the future). By modifying property rights as transactions were taking place, government inspired investor reevaluations of the risk–reward trade-offs.

It was this action that whipped speculation into a powerful fury and led to the last upward surge in tulip prices. Thus, proposed political legislation and the subsequent political theater in which planters and buyers negotiated prices were among the key causal factors driving the extreme volatility in tulip prices.

The Great Depression

The flow of money into Florida during the land boom may in fact have been the unintended consequence of Prohibition. Banks were flush with money from the brisk commerce in liquor that was present in Florida due to its somewhat porous borders with countries that did not prohibit alcohol. In many ways, we can therefore think of American money flying into Florida as a form of interstate hot money that, due to the fact that many of the banks had state (rather than national) charters, resulted in excessive lending within Florida. Might some of this capital have helped fuel the Florida land boom?

Although several policy distortions, such as the adherence to the gold standard, confounded policy actions during the early stages of the Great Depression, few policy distortions were responsible for the onset of the bubble. Many policy shifts took place in the aftermath of the bust that laid the groundwork for future busts (consider the fact that the homeownership society was a political goal set out during the 1930s in America, or that FDIC insurance on bank deposits—a prototypical example of policy-induced moral hazard—was a policy that emerged from the Great Depression).

The Japanese Boom and Bust

Chapter 8 described in detail the punitive taxation on short-term real estate sales in Japan. By artificially suppressing supply from hitting the market and decreasing the effectiveness of the price mechanism due to depressed liquidity, policy makers had inadvertently provided pricing support while intending to minimize speculative short-term trading. It was precisely by seeking to minimize speculation that they created price dynamics that attracted speculators and led to dangerously high levels of debt and real estate values. Additional complications arose from the inheritance tax and the demand it created for highly leveraged transactions, as discussed in Chapter 8. Given that Japanese tax policies effectively decreased supply and increased demand, might the government have inadvertently fueled the real estate boom?

Further, the rapid deregulation of the financial sector led to significant competition in risk-taking by banks. Credit boomed in no small part because of these shifting regulations. Further, credit controls specifically implemented to cool the housing market contributed to the ultimate bursting of the property bubble.

The U.S. Housing Boom and Bust

Among the many policy distortions that ended up fueling the fire of the U.S. housing boom and bust were the mortgage interest deduction and an overarching political belief that homeownership was a right to which every American was entitled. The involvement of government-sponsored enterprises such as Fannie Mae and Freddie Mac further bolstered the belief that the government was prepared to underwrite mortgages to facilitate the conversion of this objective into reality. The Community Reinvestment Act (CRA) went on to further encourage lending to the least creditworthy of borrowers.

A virtuous (which later turned vicious) cycle soon ensued, with Freddie and Fannie issuing bonds to global investors seeking effective U.S. government guarantees with greater reward than U.S. Treasuries. The proceeds of these offerings were then used to purchase mortgage-backed securities. This facilitated banks lending more money, knowing a ready and willing market existed for unloading the mortgages to pseudo-government entities. This enabled banks to worry less about the quality of the mortgages because they were not going to bear the risk of default or nonpayment. Thus, the whole house of cards was built on a belief that the government would not tolerate a failure of Freddie or Fannie.

Further fueling the housing boom were advantageous tax policies available to those who utilized mortgages to finance the purchase of their property. Because interest is deductible against earned income, the net result of mortgage interest deductibility is a subsidy to homeowners in the form of reduced taxes. Thus, in the case of an individual in a 35% tax bracket (and assuming no other special circumstances), the government agreed to pay 35% of the individual's interest payments.

The Chinese Investment Bubble

China's emphasis on a targeted GDP incentivized government agencies and state-owned enterprises to enter the property development and redevelopment business, adding large buying influence in the market. The state's insistence on planning first and then shifting policies to spark demand to solve problems of overcapacity led to widespread redevelopment. The restrictions on shifting domestic capital out of the country left investors few alternatives for their funds which then flowed into property after the bust of the Chinese stock market in 2008.

Further, the method by which local governments funded their operations was based upon land sales as there were no property taxes. This led to a de facto breaking down of the price mechanism, as state-owned enterprises sought funding from state-owned banks to purchase land from the … state. What do prices mean in such a scenario?

Unintended Consequences of Policy Developments

Table 12.4 summarizes the findings of this subsection. Although policy is usually a politically motivated outcome of legislative processes, it is unfortunately often accompanied by unintended consequences—many of which are likely to enhance the probability of booms and busts.

Table 12.4 Policy Distortion Indicators

Indicators

Examples

Supply/Demand Manipulation

150% property transaction tax, inheritance tax, mortgage interest deduction, Community Reinvestment Act, ghost cities, fluctuating mortgage down-payment requirements

Regulatory Shift

Conversion of futures contracts into call options, Prohibition-related hot money inflows into Florida, deregulation of Japanese banking sector, curbs on capital leaving China

Epidemics and Emergence

The biological framework presented in Chapter 5 focused on two primary lenses: epidemics and emergence. The power of the epidemic lens is in its ability to shed light on the relative maturity of a boom (i.e. to provide some guidepost as to how imminently the sustainability of the boom will be questioned). The participation of amateur investors provides that guidepost, as it is analogous to the final stages of an epidemic (who else is left to be infected?).

The emergence of group order (and consensus) from seemingly chaotic individual efforts and beliefs was the second biological perspective introduced in Chapter 5. The behaviors of locusts, bees, and ants illustrated how swarm logic via silent leadership can lead to consensus and herdlike behavior. Although it probably goes without saying, such herd mentality increases the likelihood of rapid changes in sentiment (and therefore the prices in asset markets).

Tulipomania

As discussed in Chapter 6, a telltale indicator of the boom's unsustainable progression was found in the mass participation of “nobles, citizens, farmers, mechanics, seamen, footmen, maid-servants, even chimney sweeps and old clotheswomen”[10] in the tulip markets. Further, the involvement of prominent—and therefore seemingly knowledgeable—individuals in the market for tulips provided the impetus for beelike silent leadership of the speculating swarm.

The Great Depression

According to Lewis Allen in his contemporaneous account of the Depression, participation in the stock market in 1929 was characterized not only by professional and traditional investors, but also by “grocers, motormen, plumbers, seamstresses, and speakeasy waiters.”[11] Allen goes on to note how the “Big Bull Market had become a national mania … . The speculative fever was infecting the whole country. Stories of fortunes being made overnight were on everybody's lips.” If the entire population of potential investors was already active in the market, who else might be attracted to it so as to help propel it higher?

The silent leadership of seemingly informed individuals in leading the uninformed is perhaps best demonstrated by Yale professor Irving Fisher, who is unfortunately[12] most remembered for noting that “stocks have reached what looks like a permanently high plateau” just weeks before the 1929 stock market crash. Further, the involvement of JP Morgan and others on the Thursday before the market crash helped bring a swarm of speculators “back to the honey” that afternoon.

The Japanese Boom and Bust

Once again, we find the relative maturity of the Japanese boom revealed quite blatantly in 1988 with a notation by the Far Eastern Economic Review that “stocks have become a national street-level preoccupation.” The broad participation of Japanese housewives in the market also indicated that the universe of individuals yet to be infected had dramatically shrunk.

Further, the consensus-oriented nature of Japanese society led to a particular susceptibility to herd behavior. Combined with popular media such as the economy-focused comic book bestseller or the publication of Japan as Number One, the collectivist Japanese instincts were channeled into a herd focused exclusively on upside. Just as the dramatic shift from a long-term oriented savings-focused society into a collective of speculators demonstrated emergent group behavior, so too did the rapid dejection that ensued following the bust.

The Asian Financial Crisis

Although the epidemic model did not provide immediate or obvious stories to highlight the maturity of the boom, the opening of many capital markets to hot money or portfolio inflows might be seen as one extreme form of infection. If global risk appetites had clearly extended to the least developed economies of the world, how much greater might risk appetite grow?

As for herd behavior emanating from consensus beliefs, here too the Asian philosophical mind-set is a factor as it has been deemed to be more collectivist and consensus oriented than the individualistic Western models. If everyone else was borrowing in U.S. dollars, why shouldn't I? Further, collective confidence was surely bolstered by the publication of books such as the World Bank's The East Asian Miracle.

The most important form of herd behavior, however, might have originated from outside the region with the proliferation of emerging markets funds. By diversifying exposure to many countries in a common pool, managers would indiscriminately sell if one country suffered economic woes. Further, because emerging-markets managers were compared against a common benchmark (such as the MSCI Emerging Markets Index), many behaved quite similarly in effectively replicating the index and mimicking each other. Emerging-markets funds thus became an efficient mechanism through which the economic woes of one country were transmitted to others.

The U.S. Housing Boom and Bust

An epidemic interpretation of the U.S. housing boom logically begins with a focus on the quality of mortgages (i.e. prime vs. subprime). In the quest to “infect” more and more potential homeowners with mortgages, several financial innovations enabled the participation of a greater population of infectable individuals. Using this lens, subprime and other mortgages to individuals with poor credit history represent the epidemic equivalent of a very mature stage. The rapid rise of risky mortgages as a percentage of total mortgage lending, as shown in Chapter 10, provided powerful evidence that the pool of potential participants was being exhausted. After those with good and bad credit histories have mortgages, to whom else might a mortgage be sold? Further evidence of the boom's maturity was found in the boom in real estate salespeople.

From a consensus-oriented perspective, the political goals of broad homeownership were manifested into a common belief that everyone had a right to buy a home. Further, history suggested that home prices did not fall nationally and as such, money was to be made. Low interest rates provided a spectacular tailwind to this belief, and soon shows like Flip This House and Flip That House were popular on television. Magazine covers highlighted the ease with which real estate fortunes were being made, and the formerly bankrupt property mogul Donald Trump reemerged as a popular business and real estate investing personality.

The Chinese Investment Bubble

A historic tendency to place a great value on property inclined the Chinese to see real estate as an inherently beneficial investment. A belief that rising property values could never fall was reinforced by high-visibility property developer opinions and the popularity of a television show about property investing. Visible signs of people waiting hours to get access to purchase property added to the apparent urgency to join the property-buying frenzy.

It became such a “sure thing” that even businesses that were not focused on real estate entered the business of developing property. The mania grew to obviously unsustainable levels, resulting in ghost cities, empty malls, and other signs that the universe of the “potentially infectable” had dwindled, implying an inevitable slowdown.

Searching for Bubbles

Google Trends is fabulous tool to gauge the relative popularity of search terms, and although historically it was not possible to do so (and hence the concept has little applicability to prior bubbles), it offers tremendous power regarding future speculative outbursts. Consider the following chart, which shows the relative search popularity for the term cryptocurrency from 2008, when Satoshi Nakamoto first started writing about the planned digital currency. Google applies a scores to the popularity for a term, with its most popular month being scored 100 and all other periods relative to that. The most popular period for cryptocurrency searches was January 2018, with a score almost four times as high as November 2017 (which Google scores at 27 relative to the 100 January 2018 received) and 100 times as high as a year earlier, in January 2017. The first time there was enough search data to even register cryptocurrencies was July 2010, according to Google.

Google Searches for “Cryptocurrency”

Source: Google Trends.

It's probably not a surprise that the peak in searches for cryptocurrency came in the weeks after December 17, when the best-known cryptocurrency, Bitcoin, reached its peak value, as measured in U.S. dollars: $19,435 per Bitcoin. That was a 2,364% increase over the price of Bitcoin one year earlier and a 149% increase in just one month.

Interest dropped off dramatically as prices fell, however. By August 2018, Google rated searches for cryptocurrency at 14, compared to the peak of December, with weeks where searches ranked a zero, with too little volume to be comparable.

Maturity and Consensus

Table 12.5 lists several indicators related to the biological lens. The table attempts to provide specific examples illustrating the evidence.

Table 12.5 Indicators of Emergence and Epidemics

Indicators

Examples

Amateur Investors

Chimney sweeps, old clotheswomen, grocers, motormen, plumbers, Japanese housewives, subprime borrowers

Silent Leadership

Pompeius de Angelis, JP Morgan, Irving Fisher

Popular Media

Japan as Number One, The East Asian Miracle, Flip This House, Flip That House, Snail House, Google Trends

Conclusions

There seems to be a great deal of overlap when it comes to the various signposts that telegraph an unsustainable boom. Table 12.6 summarizes the various indicators and their frequency. The shaded lines are deemed particularly important as those indicators were present in each of the studied cases.

Table 12.6 Boombustology Road Map

Tulipomania

Great Depression

Japanese Boom and Bust

Asian Financial Crisis

U.S. Housing Boom and Bust

Chinese Property Bubble

#

Reflexive Dynamics

Credit Criteria

X

X

X

X

4

Collateral/Credit

X

X

X

X

X

X

6

Hot Money

X

X

X

3

Leverage/Deflation

Financial Innovation

X

X

X

X

X

5

Cheap/Excessive Money

X

X

X

X

X

X

6

Moral Hazard

X

X

X

X

X

X

6

Overconfidence

Conspicuous Consumption

X

X

X

X

X

X

6

New-Era Thinking

X

X

X

X

X

X

6

Policy Distortion

Supply/Demand Manipulation

X

X

X

X

X

5

Regulatory Shift

X

X

X

X

X

5

Consensus/Herd

Amateur Investors

X

X

X

X

X

X

6

Silent Leadership

X

X

X

X

X

X

6

Popular Media

X

X

X

X

4

Several preliminary conclusions emerge from this framework. First, although reflexive dynamics relating to confidence seem ever present, a reflexive dynamic between collateral values and access to credit seems particularly problematic. Thus, asset-based lending without regard to affordability is reminiscent of Minsky's Ponzi financing. Second, excessive money is a big warning sign as capital seeks a home. Combined with leverage-enhancing financial innovations and moral hazard, easy money creates a toxic combination of lofty asset prices and an unsustainable foundation. Third, overconfidence in the form of “this time is different” and new-era thinking seems consistently present and somehow provides a believability to the otherwise clear extremes. Conspicuous consumption in the form of trophy asset purchases and the world's tallest skyscraper seemed consistent as well. Fourth, policy distortion manifests itself in numerous ways, but most problematically in the belief that a meaningful fall in asset values or the failure of certain institutions is politically unacceptable. While such perspectives usually surface in excessive lending and risk-taking (i.e. easy money), regulatory shifts and the unintended consequences of tax policies also seem to exacerbate the likelihood of unsustainable booms. Finally, the participation of amateur or marginal participants in an asset boom seems quite revelatory in determining the relative maturity of the boom and approaching bust. Silent leadership (either informed or uninformed) was consistently present with respect to forming consensus thinking and herd behavior, and popular media usually revealed this collective thinking.

The frequency data in Table 12.6 also provides additional insight into the relative importance of the indicators. Within the five lenses, leverage appears to be the most important and should be weighted heavily in any analysis of forthcoming busts, for as noted by one of the most successful bubble hunters of all time, James Chanos, “Most of the time that there are bubbles that burst, they can be laid at the feet of excess credit creation.”[13]

Notes

1. Nicola Gennaioli, Andrei Shleifer, and Robert W. Vishny, “Neglected Risks, Financial Innovation, and Financial Fragility,” NBER Working Paper Series, working paper #16068 (Cambridge, MA: National Bureau of Economic Research, June 2010).

2. Richard Bookstaber, A Demon of Our Own Design: Markets, Hedge Funds, and The Perils of Financial Innovation (Hoboken, NJ: John Wiley & Sons, 2007).

3. Garet Garrett, “Anatomy of the Bubble,” in A Bubble that Broke the World (Boston: Little Brown & Company, 1932).

4. Christopher Wood, “Global and Asia Investment Strategy” (presentation to the 17th Annual CLSA Investors Forum, Hong Kong, September 2010).

5. Richard Koo, The Holy Grail of Macroeconomics: Lessons from Japan's Great Recession (Hoboken, NJ: Wiley, 2009).

6. Phil Anderson, “A Recession Indicator That's Hard to Miss,” Moneyweek (February 11, 2008).

7. Mark Thornton, “Skyscrapers and Business Cycles,” The Quarterly Journal of Austrian Economics 8, no. 1 (Spring 2005).

8. Carol Vogel, “Landmark De Kooning Crowns Collection,” The New York Times (November 18, 2006).

9. Martin Jacques, When China Rules The World: The End of the Western World and the Birth of a New Global Order (New York: Penguin Press, 2009).

10. Charles Mackay, Extraordinary Popular Delusions and the Madness of Crowds (New York: John Wiley & Sons, 1996).

11. Frederick Lewis Allen, Only Yesterday: An Informal History of the 1920s (New York: Harper & Row, 1931).

12. Unfortunate because he contributed a great deal to our understanding of booms and busts, most notably via his debt-deflation theories.

13. James Chanos (speech at the University of Oxford).

CHAPTER 13

Boombustology in Action: IS INDIA NEXT?

There are two kinds of forecasters: Those who don't know and those who don't know they don't know.

—John Kenneth Galbraith

As part of his high-profile efforts to promote an ambitious development program labeled “Make in India,” Indian Prime Minister Narendra Modi met with politicians and local executives at a showcase in Germany in 2016. The “Make in India” campaign is his signature undertaking to make India a global manufacturing hub, and it has received disproportionate attention from Modi and his team since his election. It's one of the hallmarks of his economic reform program, one in which he expects to create 100 million new manufacturing jobs by 2020, six years after he first announced it. These jobs are critical to his effort to develop a middle class that will power the Indian economy for the foreseeable future.

This 2016 event was notable for one particular attendee Modi met there. It was not the CEO of a powerful multinational, the prime minister of a major European country, or even an accomplished development economist. It was a robot.

Modi met YuMi, “a collaborative, dual arm, small parts assembly robot solution that includes flexible hands, parts feeding systems, camera-based part location and state-of-the-art robot control.”[1] Robots like YuMi are the future of manufacturing and, in an ironic twist of fate, may prevent Modi from achieving his “Make in India” objectives.

Of course, robots of a sort have been in manufacturing for more than a century. Consider the story of Henry Ford II, who, on a tour of a partially automated factory with United Auto Workers chief Walter Reuther, smugly asked, “How are you going to get these robots to pay your dues?” Without missing a beat, the story goes, the UAW boss responded, “How are you going to get them to buy your cars?”

The story is telling because of the essential need for workers to have incomes adequate to buy the very goods their efforts create. It also makes clear the sometimes-conflicting dynamic between production and labor: labor costs to a manufacturer aren't just costs, they are the very source of future revenues as workers spend their money on goods they make. And when you're trying to create jobs, a productivity boost may be precisely the opposite of what is useful. Strictly speaking, productivity is the ability to produce more with the same or fewer inputs. In this sense, productivity and technological substitution of labor are the exact opposite of what India needs.

Distinguishing Potential and Reality

Modi's vision of India as a manufacturing powerhouse producing goods that global markets seek is inspirational. It has motivated lots of bullish articles and strong investor interest in the country. By taking farmers to the factories, the theory goes, Modi will drive exponential growth in the middle class that will make Indian consumers a globally powerful force, driving economic activity. His aspiration is to replicate the Chinese success story.

I'm not convinced. Robots like YuMi are one major reason. It may very well be that technological advancements like YuMi have now made it impossible to create a middle class through manufacturing methods. As will be discussed through this chapter, the hurdles to India advancing its economy in any significant way for the majority of its citizens are many. China may be the last country we see do so through the manufacturing-based approach. The window to modernize through industrialization is closing; India has likely missed its chance.

This is a driving reason I believe the love affair between the investment community and India is about to end. A multiple-lens analysis leads me to conclude that the odds of a bust in India are high, meaning investor expectations remain unfulfilled, leading to a mass resetting of capital allocation and investment priorities.

Although India appears to be in the middle of an unsustainable boom—or at least a boom anticipated by investing flows—the timing of a bust is incredibly difficult to predict. A common saying among economists captures the treacherous nature of making predictions: “Economists have predicted nine of the last five recessions.” But the challenge of identifying bubbles doesn't preclude making a probabilistic determination from which we can then build a reasonable expectation of what investments can and cannot work for the long term.

There's a joke that India's prospects look brighter the farther away you are. Certainly, from the perspective of a Westerner, India appears a more logical candidate for a sustained boom than China. It's a democracy in which most Indians seem to have a good understanding of Western institutions. The former British colony has lots of educated English speakers. It's also younger and has a cosmopolitan leader in Modi, a reform- and markets-minded head of state who has been actively encouraging foreign nations to invest in India (in direct contrast to India's historical preference as inward looking). This has led many prognosticators to suggest India will be the world's next great growth story. Even I wasn't immune. In 2015, I predicted Modi's reforms would make India the fastest-growing large economy in the world. Like others, I recognized India's potential: it has a huge population, poverty is declining, and the middle class is growing. Combined with Modi's many market-oriented reforms, these facts pointed to an imminent unleashing of the Indian economic lion.

By 2016, I reevaluated my assumptions and conclusions. I used the Boombustology framework and noticed a different picture. My conclusion: the chance that India will emerge to be the next great growth market is extremely low, and the lofty expectations of it being the next economic superpower were misguided.

Capital Markets Considerations

Returns on investments seem to provide proof of India's emergence. Indian small cap stocks, for instance, were among the best performers worldwide in 2017. The Market Vectors India small cap index was up more than 65%, and the Columbia India consumer index of stocks was up more than 50%. Broader India-focused funds, including mid- and large-caps, have been nearly as impressive, tallying double-digit price gains for the past half decade. Analysts pegged Indian bonds as the best fixed-income sector to be headed into 2018, at least in emerging markets, according to Nomura.[2] India was expected to be among the best-performing economies in 2018, in terms of expected GDP growth, according to The Economist.[3] Its 7.7% projected rise placed second on the magazine's forecast in a top five consisting of Caribbean islands Dominica and Anguilla along with Bhutan and Ethiopia.

What dynamics have been driving the current India boom and those fund returns? It is impossible to provide a certain answer, but it appears there is a distortion of investment flows that has shifted the market away from finding equilibrium.

For example, this is reflected in exchange-traded funds (ETFs) focused on India. ETFs are a pretty good proxy for broad investor enthusiasm about the country. Three ETFs hold more than 87% of the ETF assets focused solely on India. All three have seen their price, AUM, and shares outstanding rise dramatically in a five-year period. That is, as demand was growing, the price was rising. This is a prototypical sign of reflexivity in action.

Beyond ETFs, there has been increased foreign investor interest in the country over the past decade or so. Figure 13.1 shows direct foreign investment into India, in millions of dollars. As we can see, there was a huge spike in capital inflows into India starting around 2007. The world seemed to wake up and notice the promise of India's likely middle-class boom. Surely this is a very strong vote of confidence in India's prospects, correct?

Figure 13.1 Foreign Direct Investment into India, by Month

Source: Reserve Bank of India.

Well, that's unclear. It turns out the inflows into India weren't an India-specific occurrence but rather were part of a widespread capital stampede into emerging and promising markets. Around the same time, the Shanghai stock market tripled as massive investor interest in the BRICs—Brazil, Russia, India, China—accelerated. India in particular was seen as a potential leader in growth on a level akin—or even superior—to China. There don't appear to have been any positive India-specific economic activities, performance, or reforms in 2007 to spark this influx. But India had then, as it has today, the type of story that attracts investors. It's a seductive story, for sure, with a massive market and huge potential base of middle-class consumers.

In a 2007 report, Goldman Sachs framed India's long-running mediocre economic performance (the country contributed 3% of world output the year prior) as “a historical aberration.”[4] India, the investment bank noted, was once 20% of global output at the turn of the Industrial Revolution. In 2007, India was unwinding its post-independence managed economy, and so, the firm argued, its return to prominence was inevitable. According to the report, India will surpass the U.S. economy in terms of GDP by 2050, making it the second-largest economy in the world after China.

India has missed many of the benchmarks Goldman Sachs set out for it. But we don't need to reach back to 1770 to paint India as an economic giant in waiting. In addition to positive portfolio returns, direct foreign investment is more than $2 billion per month—almost $9 billion in one recent month—certainly enough to ignore the down periods we see in Figure 13.1. Modi is plastered on the cover of numerous global magazines. There is a lot of evidence to tell India bulls they're correct. Unfortunately for India, there are signs this is largely reflexive, as foreign money inflows are creating the very environment that spurs further inflows.

The exuberance hasn't stopped with FDI and portfolio inflows. It has also spilled into venture capital. Some have suggested that domestic India venture capital funds have attracted too much capital. In January 2018, an anonymous executive at a top-five VC firm in India told financial site Livemint as much:

It's clear now that most funds have raised too much money. Anything more than $150-200 million and you're going to struggle to give returns. But most VC firms have raised funds that are two-to-three times bigger. The fact is that India is just not a deep enough market to absorb that kind of capital.[5]

The siren's song of future profits sure seems too alluring to resist.

Population, Debt, and Investment

It was an opportunity deemed too good to pass up. In 2004, a fund run by a politically connected Indian businessman, a former Goldman Sachs partner and onetime principal at Michael Dell's investment firm, offered foreigners the opportunity to invest in the opaque but highly appreciating Indian real estate sector. The smart money jumped in: billionaire Kenneth Griffin's Citadel and Axon Capital, part of the TPG Group run by billionaires James Coulter and David Bonderman, were two high-profile investors. The University of Notre Dame, Stanford, and Children's Investment Fund (one of the U.K. largest nonprofits) also invested. The fund, Ireo, raised more than $1.5 billion to invest in Indian real estate by 2010.

Yet within a few short years, Ireo began to fall apart as investors started to sense that some of the deals were fronts for Ireo managers to siphon off millions (some believed it was as much as $150 million) for themselves. By 2016, after a $16 million capital call, which included $11 million in management fees, investors had had enough—and the lawsuits began. The fraud, said Dinakar Singh, the head of Axon, is “brazen … If we do nothing, we will largely bleed dry and get nothing back.”[6]

As of this writing, the case is still being litigated, but the fact that the “smart money” is caught in a situation where they've locked up hundreds of millions of dollars in what they now describe as a scam is a sign of the overexuberance of investors to get in on the action. It is reminiscent of Warren Buffett's 2001 line that “only when the tide goes out do you see who has been swimming naked.” The tide appears to be going out.

Right now, let's center on one aspect of India's real estate market that helps conceal a credit-fueled asset bubble as a sustainable growth story. As is often the case, it begins with a reflexive dynamic. In this case, banks chose to use collateral values to justify lending larger sums, which led to higher prices, additional collateral value, and … you guessed it, more lending.

Indian lenders perform what's called lending against property (LAP), in which the collateral for the loan is the property the borrower is looking to build or improve. It's a metric that should remind Western readers of asset-based lending terms like loan-to-value (LTV), a metric used by American and other banks to size loans against the value of the collateral. LAPs need an appropriate appraisal of a property's value to reduce risk—recall that overvaluing real estate's expected value was a key mistake of the U.S. housing bubble. But it is not unusual for LAPs to be as high as 90% of the expected value of the property, a percentage so high that even a conservative appraisal could still be off enough to render a loan problematic.

There has been enough demand that the number of banks that offer these LAPs has been expanding dramatically. In mid-2016, there were about 70 housing finance companies (HFCs) in India, a level that grew to 82 in the spring of 2018, with a further 14 HFCs waiting on their approvals from the government to start lending, according to The Economic Times.[7] And just as we witnessed in China, higher collateral values helped fuel lender confidence, resulting in more lending and higher collateral values … which in turn led to greater confidence as perceived risk fell.

But as the number of lenders multiplies, there have been increasing signs that the quality of the loans is starting to degrade. In 2017, the percentage of nonperforming LAPs was around half a percent. That had more than doubled in less than a year to 1.2%, with some lenders reporting nonperforming loans of more than 4% and 5% of their loan book. But the motivation for the lending was the “sure thing” psychology that often characterizes these dynamics. It wasn't government support driving the boom; it was private, for-profit motivations running wild.

From 2010 to 2016, the percentage of real estate funding from non-government-backed institutions rose from 25% of the market to 75%, according to Kapil Mahotra, the principal of India real estate advisory firm Square Yards.[8] The trend is only accelerating, Mahotra asserts, writing in the Financial Express, “foreign investment and private equity funds have become a cool source for cash for developers who cannot rely on the banks to bail them out anymore, owing to a pre-existent, fat ledger.”

Total loans outstanding in the Indian residential real estate market stood at about 4 trillion rupee, or $58 billion, in early 2018.[9] There were signs in the first half of 2018 that the residential real estate market was slowing, in part as a result of the government crackdown on unreported all-cash deals. Year-over-year prices were lower in most major regions, including Mumbai, which probably will affect the likelihood that outstanding loans are repaid in full on time. And still Mumbai remains among the world's most expensive cities to live in. It would cost the average Indian 67 months of gross income to buy a residence there.[10] Might it be conceivable India's real estate market will undergo an implosion similar to what transpired in the overheated European and U.S. markets during the last decade? Could lower prices generate more sellers, creating a vicious price-supply cycle?

It's also worth noting that at the time of this writing, India's shadow banking sector was facing a credit crunch, spurred by one of the country's largest financial firm's defaulting on approximately $13 billion of its debt.[11] When combined with the plethora of nonperforming loans plaguing the state-owned banking sector, the plunge in the availability of credit is likely to hit the economy quite hard—leading Prime Minister Modi to pressure the Reserve Bank of India toward easing credit conditions. The resulting spat over the central bank's independence led to a “clash of egos” that caused former Governor Raghuram Rajan to resign.[12]

Given that four out of every 10 consumer loans made between 2015 and 2018 originated from the non-bank, private lending sector, the impact is likely to be felt across the Indian economy[13] —but especially in housing, because nearly a third of all shadow loans in India were for purchasing homes. It's precisely this dynamic that led The Economist magazine to question whether India was heading for its own “Lehman Moment.”[14]

India's Versailles

Overconfidence is running high in matters related to India, and nowhere is this fact more obvious than in the building of skyscrapers. Recall the earlier description suggesting that skyscrapers tend to telegraph the unsustainable and toxic cocktail of speculative tendencies, easy money from confident banks, and hubris. Now consider that India's 15 tallest buildings have all been built in the past eight years.

In a continuing sign of at least hubris, if not overconfidence, an Indian minister expressed his desires for India to be seen on the world stage: “I want a structure taller than the Burj Khalifa, one of the tallest buildings of the world, to come up in Mumbai near the seas. It will be called Chhatrapati Shivaji Maharaj tower,” Nitkin Gadkari, the minister for road transport and highways said at a 2017 conference.[15] It's a proposal he's been pitching regularly for a few years …

And what are we to make of the curious case of the world's tallest personal skyscraper? A decade ago, Mukesh Ambani, the chairman of Reliance Industries and one of the world's richest people, with a fortune over $40 billion, according to Forbes, started building Antilla, a 568-foot-tall personal residence. It is a tribute to excess that only the superwealthy can embrace. Each floor is unique, with no layout or materials the same—there are six floors of parking, enough for 168 cars, and a service station on the seventh floor. There is a 50-person movie theater, a ballroom with 80% of the ceiling covered with crystal chandeliers,[16] and a snow room that spits out snowflakes on demand.[17] Seemingly everything about the home is spectacular, from the reputed $1 billion price tag, to the (incorrect) belief that it has more square footage than Versailles. There are terraced gardens, panic rooms, a gym for every family member, swimming pools, yoga rooms, and, of course, a helipad. Reading about Antilla in the popular press is reminiscent of the pastiche of coverage given to Charles Foster Kane's Xanadu estate in Citizen Kane. The only difference is that the fictional Kane had a few dozen servants—Ambani and Antilla have more than 600. No wonder many have labeled it a monument to inequality.

Culture, Vocation, and Underdevelopment

In his bestseller The End of Poverty: Economic Possibilities for Our Time, Jeffrey D. Sachs makes his case for India's ability to break free from its history of poverty and the government's tendency to stifle growth with a myriad of licensing requirements, known as the License Raj. “As with China, the twenty-first century is likely to be the era when centuries of relative economic decline in India are substantially reversed. I have made that optimistic point since the early 1990s, and events have proven it correct,” Sachs writes.[18] He even draws geographic parallels with India and China: the coasts benefit, first while the hinterlands—the western deserts in China, the Ganges plains in India—are slower to follow.

One of Sachs’ most compelling points is that poverty has been reduced by great lengths in India. In 1981, almost 60% of the Indian population was living in poverty, on the equivalent of less than $1 a day, the international poverty level as determined by the World Bank. By 2001, that had been reduced to about 35%.[19]

A 2017 World Bank report, which uses 2011 data—the last time a large data set was able to be collected—showed that the poverty rate had fallen again to 21.9%.[20] I don't want to dismiss the fundamental decency of reducing poverty, for it's an undeniable good thing. It's just not a reason to believe India is the next China. Why is this likely to be the case? About 87% of Indians live below the upper-middle-class poverty level. In China, that level is 36%, with just 1.9% living at the international poverty level.

In the United States, being upper middle class means having more than $125,000 a year of income, according to the Pew Research Center. In China, members of the upper middle class earn between $16,000 and $34,000 per year. Obviously that's much less, but it's certainly enough to afford an iPhone if someone really wanted one. Upper-middle-class income in India? It's $2,008. No wonder Apple has only 2% market share of the India mobile phone market.[21] For the vast majority of the nation, it would cost more than half a year's pay to buy the latest iPhone. That doesn't stop Apple CEO Tim Cook from selling the belief India may be the next driver of the company's growth, with India as the next China. “We're bringing all of our energies to bear there. I see a lot of similarities to where China was several years ago,” Cook told investors in an August 2017 phone call.[22] Incidentally, the company has also replaced its India country head two times within the 2017–2018 timeframe as it has consistently struggled to break into what may mistakenly be seen as a big market. But perhaps India will eventually raise incomes high enough to have a large consumer market for Apple to penetrate.

Is that even possible?

One reason India is seen so widely as an inevitable economic giant is its youth. Youth is valued because of the assumption of a larger future workforce, which should both increase GDP and help a country grow out of debts. But youth has a potential dark side, economically. As a too-young population floods the labor force, it has the potential to drag down per capita economic gains. Growing an economy is probably a good thing, but what if the population grows faster? Per capita income will fall. Instead of the much-celebrated demographic dividend arising from lots of labor, India may be facing a demographic millstone because about half the population is under the age of 25. That makes for some unsettling numbers. In the spring of 2018, more than 90,000 jobs at the state-owned railways were open for applications. The number of applicants in the first month: 25 million.[23] This isn't an isolated event. In 2015, the state of Uttar Pradesh advertised for 368 tea servers and night guards, and 2.3 million people applied. Elsewhere, 19,000 people applied for one of 199 street-sweeper jobs.[24]

All told, there are 31 million Indians unemployed and looking for work, as of the spring of 2018.[25] That's still a relatively modest 6.1% of the labor force, but the real rate for young workers has consistently been much higher—it hasn't been below 10% since 2006, according to World Bank data.[26]

Efforts to increase female workforce participation will probably exacerbate the employment challenge as well. India has one of the world's largest gender gaps in workforce participation, with about 29% of women participants in the workforce (and women workers have had a consistent unemployment rate about 0.7 of a percentage point higher than national unemployment in recent years, according to the World Bank[27]). Bringing in even more workers could compound the wage and employment pressures, leaving India with a choice of a larger pool of low earners or a smaller pool of middle-class consumers. Greater inequality or smaller middle class? Not a great choice to face.

Although there is some validity to the expectation that additional workers will bring greater GDP growth, it is possible the additional GDP comes in increments that reduce per capita GDP: that is, more people, each working for less money and each producing less.

I think it's possible the drumbeat of India's pending economic maturation may also be driven by a willful overestimation on the part of companies. As the world's largest company by market cap, Apple and its CEO Tim Cook surely want India to develop into a large and important market for the company, but they also need investors to believe that it is going to happen, in order to maintain Apple's image as a growth story. How might investors react if Apple announced to Wall Street that it had penetrated most of the world and would not be growing its sales of iPhones beyond normal replacement demand? What if it said India was unlikely to be a sizable market for decades?

The story of growth sells, and many publicly traded companies are out there selling the India opportunity. Walmart, for instance, has in recent years exited the $4 trillion German market altogether and also sold the majority of its U.K.-based Asda supermarket chain. Asda had sales of more than $29 billion and, in 2017—which was a down-year for the chain—profits of $950 million. Walmart now has no appreciable presence in Europe, having determined that the return prospects would be suboptimal. Shortly after exiting the U.K., it purchased a majority stake in Flipkart, an India e-commerce company, for $15 billion. Flipkart sold $2.95 billion worth of goods in the latest year before its Walmart acquisition, losing $740 million in the process.

I fully understand that these aren't similar businesses: Asda is a traditional grocery store model, whereas Flipkart is an Amazon-like model, which would be expected to have a higher growth rate. But since the Asda sale was timed right before the Flipkart purchase, they are perceived to be related. The U.K. is a market where the average consumer has household income of $36,000. Recall that India's upper middle class earns around $2,000 a year. Almost 1.1 billion of India's population don't have internet access, according to a 2016 World Bank report.[28] They can't even begin to think about using Flipkart. At the very least, there is reason for investors to wonder if it's a sound decision to sell lots of revenues and profits to buy less revenue and large losses. How could such logic be justified? By the story of growth. Internet penetration will grow. Revenues will grow. And hopefully, someday, losses will give way to profits.

What do the Asda and Flipkart transactions provide? They give Walmart CEO Doug McMillon talking points to push a growth story to Wall Street analysts. “India is a priority market for us. We're taking action to position the company for the future,”[29] he said about news of the Flipkart acquisition. Analysts agreed.

“This is a brilliant long term move,” said Susquehanna analyst Bill Dreher.[30] Baird analyst Peter S. Benedict liked it, too: “Today's Flipkart deal (WMT's largest ever) is CEO Doug McMillon's boldest move yet as he continues to transform the company to compete/succeed in the future of retail.”[31] The immediate investor reaction was conflicted: Walmart shares declined on the Flipkart news, apparently in reaction to the large purchase price. Yet they rose on news of the Asda sale a week earlier.

Automation: Why It's Too Late for India

In early July 2018, India Prime Minister Narendra Modi and South Korean President Moon Jae-in cut the ribbon at the official opening of the world's largest mobile phone factory, a Samsung plant outside New Delhi. It is another highlight of Modi's ambitious “Make in India” campaign. For all the high-profile ribbon-cutting ceremonies Modi attends, I worry that productivity gains arising from automation have made manufacturing-driven development an antiquated approach, meaning India is too late to reap the benefits of this strategy. Simply put, technology has progressed so rapidly over the past few decades that only very cheap wages can compete with capital equipment. And to be competitive with the rise of robotics, those wages must stay low—exactly the opposite of what India's economy needs. Those low wages might prevent the emergence of the middle class Modi seeks.

At first glance, the “Make in India” strategy seems to be working. Modi's plan has, after all, attracted significant foreign investment. In the summer of 2017, electronics manufacturer Foxconn announced plans to invest $5 billion in a factory in western India, while GM unveiled $1 billion in planned investments on the subcontinent. Other companies, including Airbus and Hyundai, also announced major investments in India to expand their global manufacturing footprint. The Samsung plant Modi and Jae-in opened was an expansion of an existing factory as part of $715 million in new investments in India by the Korean conglomerate. Overall, foreign direct investment increased 16% from 2014.[32]

This is all great news for India. But it may not create the jobs Modi needs to achieve his goal. Bluntly put, the outlook for the “Make in India” campaign is not as rosy as such headline investments suggest. Overall foreign direct investment is the same as a decade ago and is still less than 2% of India's investing capital.[33] And the value of stalled projects has risen almost 50% under Modi, to 7.63 trillion rupees, or $110 billion. Much of the reason is that Indian industry invested heavily in the years prior to Modi's election in 2012 and now sits on too much capacity. Investing in the face of overcapacity? Uh oh.

Further, the country created only 4 million manufacturing jobs between 2010 and 2014. It's doubtful “Make in India” has made much headway toward its goal of 100 million jobs since then—even though the government says it doesn't collect data on “Make in India” job creation.[34] Manufacturing jobs as a percentage of total Indian employment are 15%, compared to 15.1% in 2014 and 17.4% in 2006, the recent peak, according to World Bank data.[35]

And that brings us back to YuMi, the robot Modi met in Germany.

Thanks to rapid technological progress and the consistently falling prices of robots, production processes are becoming increasingly automated. As the Boston Consulting Group put it, “Many industries are reaching an inflection point at which, for the first time, an attractive return on investment is possible for replacing manual labor with machines on a wide scale.” As the manufacturing jobs data indicates, India is swimming against a receding tide.

The future of global manufacturing is clear: more robots, fewer workers. And this means a huge pool of low-cost labor is no longer a competitive advantage in development. The extreme automation represented by YuMi may seem new, but the dynamic of diminishing returns to industrialization has been in place for decades. Researchers have noted that the share of manufacturing jobs in industrializing economies has been consistently peaking at lower income levels. In America, manufacturing employment peaked when per capita income was at $17,700; decades later, South Korean peaked at around $12,700; and in Brazil, the level was $8,700. More recently, some African countries have seen manufacturing employment peak with income levels around $2,000 per capita. As The Economist bluntly concluded, “The technological transformation now under way appears to be permanently changing the economics of development … China may be among the last economies to be able to ride industrialisation to middle-income status.”[36]

Researchers from M.I.T. and Boston University studying the effect of robots on the U.S. labor market between 1990 and 2007 believe robotics cost hundreds of thousands of jobs, mainly in manufacturing. The appearance of a single new industrial robot appears to lead to the employment drop of 5.6 workers, and a reduction in wages for the workers who remain of one-quarter to half a percentage point, after controlling for Chinese and Mexican imports, offshoring, and other factors.[37]

This dynamic was recently explained to me by the CEO of a large U.S. manufacturer of consumer products. Fifteen years ago, this company had 16 factories across the country. Since then, this company cut its factory count to six and then trimmed the number of employees in the remaining factories by one-third. Yet the whole company produces 50% more than it did 10 years ago. Its profitability has also surged. Frankly, it seems as though technology may be displacing more workers than globalization ever did.

“Make in India”: The Wrong Playbook for Development

If manufacturing cannot create the much-needed jobs, it's hard to imagine how the “Make in India” campaign will generate the broad-based, sustainable growth the country seeks. Even if India becomes a global production hub, the declining labor-intensity of manufacturing suggests the rewards from this accomplishment will be highly concentrated among very few. The economic tide may rise, but not everyone will have a boat. Most will struggle to keep their heads above water.

Further, remember that per capita calculations are affected by demographics. Given that India is slated to become the world's most populous country by 2022, the pressures to create jobs and increase incomes are rising. An additional 150 million Indians will enter the workforce over the next 12 years[38] —roughly the population of Russia, the world's ninth-largest country. Will these new workers find jobs? And how much will those jobs pay? The reality is that productivity-enhancing technologies allow economies to produce more with less. India needs to produce more with more.

Highly productive robots are a fabulous method to address worker scarcity. But India has plenty of potential workers. What India needs is jobs, and lots of them. The country's labor pool is currently growing by more than 1 million workers each month. This is a remarkable figure, one not seen in other major economies. By comparison, it took the U.S. labor pool a year over the course of late summer 2017 through August 2018 to add about 1 million workers—and that was during an economic boom.[39] It even drove unemployment down to an 18-year low. China's labor pool is shrinking as it feels the ripples of its now-scrapped one-child policy.

The problem with productivity is that it exacerbates issues arising from abundance. Automated manufacturing is very similar to adding workers to an already-large pool of available workers, which India is also doing. Bluntly, this is the opposite of what India needs. When the supply of workers rises, the wages each earn will fall. Lower wages dampen the likelihood of a large middle class.

It's hard to imagine that anyone—in India or elsewhere—will succeed at putting the productivity genie back in the bottle. But focusing on what worked in China is a recipe for disappointment. It's time to rethink the “Make in India” campaign, and it's best to do so soon. Without a new strategy, Modi's manufacturing mantra may mean malaise for millions. And worse, it may mean India never emerges into a developed nation. Along the way, economic weakness can lead Modi and others to whip up nationalism as a means to “wag the dog” and redirect the ire of the economically disgruntled towards another nation. Saber-rattling or even an escalation of military tensions with China or Pakistan would be increasingly likely.

India’s Elusive Middle Class: Disappointment Ahead?

Starbucks is often seen as an informal economic indicator, given its ubiquity and the “affordable luxury” nature of its product. The better the economy is performing for the middle class, the more Starbucks locations are opened, and the more venti coffees it sells. The opposite is true, as well: during the financial meltdown of 2008, Starbucks shut more than 900 stores in the United States.

The coffee purveyor has been heavily focused on international markets as the source of its future growth. The company opened its first shop in China in 1999, expanding its operations in India 13 years later. It's not a fair comparison, therefore, to look at store count; but looking at Starbucks's growth metrics in each nation shows that India's economy continues to lack a jolt.

According to an internal 2014 Starbucks story on its India and China operations, in early 2014, Starbucks had 40 locations in India and planned to double that number in a year.[40] At the same time, Starbucks planned to have 1,500 stores in China in 2015. In China, Starbucks ended up overshooting that estimate by a third, with 2,000 locations by the end of 2015. Efforts in India fell short, however, with just 74 outlets a year later.

Even as we would expect the pace of store openings to slow in China, they have in fact accelerated. In 2018, Starbucks stepped up its pace of openings in China to one store every 15 hours![41] That will give the Seattle company 6,000 stores in China by the end of 2022.

In contrast, Starbucks's rate of openings in India has gotten slower. The company just opened the doors of its 100th India store in autumn 2017 and opened three more stores through mid-2018, all in Kolkata. Over the course of Starbucks's presence in India, that's a pace of one store every 23 days. “We believe that one day India will be one of the top five markets. I won't put a time frame to it. It's over the long haul,” Starbucks International President John Culver told BloombergQuint in 2017.[42] At the present pace, it wouldn't surpass current number five, South Korea, for more than 63 years. Given current trajectories, that means the ribbon cutting for Starbucks India store number 1,109 would probably happen in 2081.

This is a sign that the Indian middle class everyone expects to appear remains as elusive as a Bengal tiger. Still, the financial and economic worlds wait, eagerly anticipating that an impressive and aggressive middle class will emerge from the forests.

Consulting firm Deloitte opened a global outlook piece in late 2017 with an optimistic note: “Two centuries ago Napoléon Bonaparte noted, ‘China is a sleeping giant … when she wakes, she will move the world.’ He was right. But now two other sleeping giants are stirring.”[43] One of those giants for Deloitte is India. (The other is the aging population of much of Asia, mainly China.)

The reality is that very few of us, save perhaps the world's best-endowed colleges, have an investment perspective that would have profited from Napoleon's prediction. But such statements resonate with investors who look longingly at the returns they could have gotten if they had invested in China in the mid-2000s.

In fact, those who disagree with the statement that India will be the next China are more likely to argue that India won't be the next China because India is going to be bigger than China as an investment story—India's population is younger than China's, India has a desperate need for infrastructure and doesn't risk overcapacity, and India's democracy makes it more vibrant than China's one-party state.[44] Yet none of those things is a guarantee of economic growth. And we could contend that China's emergence as an economic superpower was as unlikely as it was likely. The fact that China emerged as an economic power implies that it was inevitable, but that's only because it happened—our frame of reference is just the one global economic experiment we were able to witness. The timing for a manufacturing-led development strategy happened to work within the long arc of history. India doesn't appear to be as lucky.

It's possible too that there's some North American misapplication of seemingly easy parallels to India. One of these is a misperception of English as a widely spoken language in the country. One reason comes from the shift of many English-speaking jobs from North America to the subcontinent, a hot topic of media reports in the United States for much of the prior decade. The attention on India also reflects American angst over being left behind in the globalization of the economic system (including a mildly successful film and an unsuccessful follow-on TV comedy, both titled Outsourced, about an outsourced American call center in Mumbai).

For obvious reasons, any similarity to the United States would make India appear to be well-suited for sustained economic outperformance. Some have also suggested that the Irish experience may provide insight into India's future trajectory. Ireland, like India, was a colony that had its own native language. Today in Ireland, the English language is spoken by 99% of the population, almost all of whom use it as their first language. Western investors, partially due to their own behavioral biases (availability), assume that English is spoken by all, or at least a majority of, Indians. There is no shortage of commentators on investor prognostication site Seeking Alpha who fill out their bull cases by noting that one of India's primary languages is English, which makes it easier for business to thrive.

Is Ireland—or any other poor county that became rich, for that matter—an effective parallel for India? Long seen as backward and economically bereft, Ireland went from the poorest member of the European Community in 1973 to the “Celtic Tiger” economic miracle of the 1990s, in the space of a generation. From afar, the two countries tick many of the same boxes, down to both being largely seen as economically backward in the lifetimes of many. There are a lot of similes for India bulls to weave into their narratives.[45]

Yet for all the ways India is like another country, the analogies start to break down the closer we look at the details. Only about 10% of Indians actually speak English, and only about 2% speak English as their first language.[46] Of course, English isn't a requirement for an economic boom, as China and any other number of economies can attest. But herd behavior in investing includes attraction to what we find familiar, including language bias. Studies have found consistently that investors have a home-country bias to their portfolios.[47] In fact, there is not only a bias to investments in a person's own country, but a further bias to investments in their native language, according to a 2017 study of investors in Belgium, where some of the population are native French speakers and others are native Dutch speakers.

How about in economic growth? The Celtic Tiger was no mirage in many ways (although Ireland suffered its own asset-bubble bust in 2009, with much of the West). In Ireland's case, the European Union acted as a sort of financial Deus ex Machina, descending in with billions in aid and infrastructure infusions. From 1973–2015, Ireland received more than $120 billion in E.U. investment in infrastructure and other projects—an amount more than 50% greater than the country's total budget for 2017. Nothing like that is coming to India, especially given that its population is 350 times as large as Ireland and a full 1 billion people more than the United States.

Recall the discussion of investing in India ETFs and bonds from foreign investors. It's not just foreign money moving herdlike into Indian equities. In 2017, India-based equity mutual funds saw domestic inflows of $20 billion, double the level of the prior year, because Indians wanted higher returns than had been seen in gold. The expectation is that foreign money will continue to push stocks higher even as people recognize that Indian stocks are expensive compared to their historical ratios.

An article in the India edition of Quartz, usually one of the more sober news commentary sites, ended its 2017 Indian stock review breathlessly: “While it is widely believed that Indian stocks are expensive by their own historical standards, if there is a flush of dollars towards emerging markets, some of it will reach India and keep the party on!”[48]

Market cheerleading is bolstered by the India edition of Forbes, which a decade after its formation now has a million monthly web readers and a million Twitter followers. India placed 121 billionaires on Forbes’ billionaires list,[49] easily the magazine's most widely read issue each year. In 2017, it termed India a “billionaire factory,” churning out dozens of fresh billionaires each year.[50] If India produces billionaires by the bushel, why not hop on their coattails? Rather than billionaires, it's really buyers of middle-class goods and services who will shed light on India's true sustainable prospects. A better question may be, how many Starbucks drinkers has the country produced? Or how many iPhone buyers? Recent evidence suggests these buyers may be more elusive than the world seems to believe.

Table 13.1 below summarizes the Boombustology framework as applied to the India case. Note that all of the most powerful indicators (i.e. the shaded ones) are flashing warning signs with respect to India.

Table 13.1 Troubling Indicators Suggest India May Disappoint Expectations

India

Notes

Reflexive Dynamics

Credit Criteria

Collateral/Credit

x

Lending Against Property (LAP) schemes

Hot Money

x

Indian inflows driven by global investor appetite for BRIC and emerging markets

Leverage/Deflation

Financial Innovation

x

Emergence of domestic bond market and shadow banking sources of capital

Cheap/Excess Money

x

Explosion in the number of Housing Finance Companies (HFCs); debt levels

Moral Hazard

x

State-banks and implied “safety-net”

Overconfidence

Conspicuous Consumption

x

Antilla; Indian quest for World's Tallest Building

New-Era Thinking

x

India as “The Next China”

Policy Distortion

Supply/Demand Manipulation

x

“Make in India” as the development strategy to create jobs

Regulatory Shift

Concensus/Herd

Amateur Investors

x

FDI inflows; BRIC-oriented investors; foreigners entering Real Estate

Silent Leadership

x

India's past economic sluggishness as “an aberration.”

Popular Media

x

Modi on magazine covers; Forbes  describing India as a “Bilionaire Factory”

Notes

1. “PM Modi meets robot YuMi at “Make in India” event, The Times of India (February 13, 2016), https://timesofindia.indiatimes.com/business/india-business/PM-Modi-meets-robot-YuMi-at-Make-in-India-event/articleshow/50977653.cms.

2. Yumi Teso, “Indian Bonds Look Set to Outshine Emerging-Market Peers, Nomura Asset Says,” Bloomberg (July 26, 2010), https://www.bloomberg.com/news/articles/2018-07-26/nomura-asset-sees-indian-bonds-outshining-emerging-market-peers.

3. “Arrested Development,” The Economist special report (October 4, 2014), https://www.economist.com/special-report/2014/10/04/arrested-development.

4. Tushar Poddar and Eva Yi, “India's Rising Growth Potential,” in BRICs and Beyond (Goldman Sachs: November 2007), https://www.goldmansachs.com/insights/archive/archive-pdfs/brics-book/brics-chap-1.pdf.

5. Mihir Dalal, “Exit Blues,” Livemint (January 12, 2018), https://www.livemint.com/Companies/6vSCdFRsDvkbijPwt9AFnO/Exit-blues.html.

6. Olivia Carville, “Hedge Funds Alleging $1.5 Billion Fraud Fear They'll ‘Bleed Dry,’” Blomberg (June 25, 2018), https://www.bloomberg.com/news/articles/2018-06-25/tci-axon-accuse-india-real-estate-fund-of-1-5-billion-fraud.

7. Shailesh Menon and Deepali Gupta, “Home Truths: Signs Are Ominous for Housing Finance Companies, The Economic Times (April 3, 2018), https://economictimes.indiatimes.com/news/economy/finance/home-truths-signs-are-ominous-for-housing-finance-companies/articleshow/63589551.cms.

8. Kapil Malhotra, “5 Trends That Will Shape Real Estate Sector in India,” Financial Express (May 28, 2018), https://www.financialexpress.com/money/5-trends-that-will-shape-real-estate-sector-in-india/1183968.

9. Pankaj Agarwal, Rahil Shah, and Ravi Singh, “Real Estate Lending – NBFCs Most Exposed,” Ambit (February 2018), https://www.propstack.com/news/wp-content/uploads/2018/02/Ambit_BFSI_Thematic_Real-Estate-NBFCs-most-exposed_15Feb2018.pdf.

10. Madhura Karnik, “The Reasons Behind Mumbai's Ever Increasing, Unaffordable Home Prices,” Quartz India (May 11, 2016), https://qz.com/540983/the-reasons-behind-mumbais-ever-increasing-unaffordable-home-prices.

11. “India's Banking System Is Flirting With a Lehman Moment,” The Economist, November 8, 2018.

12. Mundy, Simon and Sender, Henny. “How the rise of shadow banking fed India's ‘clash of egos’” The Financial Times. November 22, 2018.

13. Anirban Nag and Suresh Aashika, “Shadow Banking Crisis Is Starting to Hit India's Consumers,” https://www.bloomberg.com/news/articles/2018-11-13/cash-starved-lenders-shackle-consumers-in-fastest-growing-nation.

14. “India's Banking System,” The Economist.

15. “Nitin Gadkari Wishes to Build Structure Taller than Burj Khalifa on Mumbai's Waterfront,” Firstpost (April 17, 2017), https://www.firstpost.com/business/nitin-gadkari-wishes-to-build-structure-taller-than-burj-khalifa-on-mumbais-waterfront-3387718.html.

16. Matt Woolsey, “Inside the World's First Billion-Dollar Home,” Forbes (April 30, 2008), https://www.forbes.com/2008/04/30/home-india-billion-forbeslife-cx_mw_0430realestate.html#5cc5249a5856.

17. Rajini Vaidyanathan, “Ambanis Give First View Inside ‘World's Priciest House’ in Mumbai,” BBC News (May 18, 2012), https://www.bbc.com/news/world-asia-india-18113969.

18. Jeffrey Sachs, The End of Poverty: Economic Possibilities for Our Time (New York: Penguin, 2006).

19. Ibid.

20. World Bank, Country Poverty Brief: India (October 2017), http://databank.worldbank.org/data/download/poverty/33EF03BB-9722-4AE2-ABC7-AA2972D68AFE/Archives-2017/Global_POV_SP_CPB_IND.pdf.

21. Manish Singh, “Why Apple Sells Just 2.5% of India's Smartphones,” CNBC (January 29, 2018), https://www.cnbc.com/2018/01/29/why-apple-sells-just-2-point-5-percent-of-indias-smartphones.html.

22. Alnoor Peermohamed, “Tim Cook Explains Why India Could Be the Next China for Apple,” Business Standard (August 3, 2017), https://www.business-standard.com/article/companies/tim-cook-explains-why-india-could-be-the-next-china-for-apple-117080200265_1.html.

23. Neha Dasgupta, “More than 25 Million People Apply for Indian Railway Vacancies,” Reuters (March 29, 2018), https://www.reuters.com/article/us-india-unemployment-railways/more-than-25-million-people-apply-for-indian-railway-vacancies-idUSKBN1H524C.

24. Ian Jack, “India Has 600 Million Young People – And They're Set to Change Our World,” The Guardian (January 13, 2018), https://www.theguardian.com/commentisfree/2018/jan/13/india-600-million-young-people-world-cities-internet.

25. “Unemployment Rate in India: Nearly 31 Million Indians are Jobless,” The Times of India (March 8, 2018), https://timesofindia.indiatimes.com/home/education/news/unemployment-rate-in-india-nearly-31-million-indians-are-jobless/articleshow/63182015.cms.

26. World Bank, Jobs Data: India, http://datatopics.worldbank.org/jobs/country/india.

27. Ibid.

28. World Bank, “Digital Dividends” (World Development Report, 2016), 8, http://documents.worldbank.org/curated/en/896971468194972881/pdf/102725-PUB-Replacement-PUBLIC.pdf.

29. Nathaniel Meyersohn, “What Is Walmart's Global Plan?” CNN (May 9, 2018), https://money.cnn.com/2018/05/09/news/companies/walmart-flipkart-india-amazon/index.html.

30. Ibid.

31. Krista Gmelich and Austin Weinstein, “Here's What Wall Street Is Saying About Walmart's Flipkart Deal,” Bloomberg (May 9, 2018), https://www.bloomberg.com/news/articles/2018-05-09/here-s-what-wall-street-is-saying-about-walmart-s-flipkart-deal.

32. OECD, FDI Flows, https://data.oecd.org/fdi/fdi-flows.htm.

33. Iain Marlow and Vrishti Beniwal, “Biggest Mobile-Phone Plant Masks Modi's ‘Make in India’ Struggle,” Bloomberg (July 18, 2018), https://www.bloomberg.com/news/articles/2018-07-18/biggest-mobile-phone-plant-masks-modi-s-make-in-india-struggle.

34. “Government Does Not Maintain Job Creation Data Under Make in India: Chaudhary,” The Economic Times (July 30, 2018), https://economictimes.indiatimes.com/news/economy/policy/government-does-not-maintain-job-creation-data-under-make-in-india-chaudhary/articleshow/65199133.cms.

35. Marlow and Beniwal, “Biggest Mobile-Phone Plant.”

36. “Arrested Development,” The Economist.

37. Jay Fitzgerald, “Robots and Jobs in the U.S. Labor Market,” The NBER Digest (May 2017), http://www.nber.org/digest/may17/w23285.shtml.

38. Vincent Del Giudice and Wei Lu, “India Seen Topping Global Labor Force in Next Decade, Data Show,” Bloomberg (August 31, 2017), https://www.bloomberg.com/news/articles/2017-08-31/india-seen-topping-global-labor-force-in-next-decade-data-show.

39. United States Labor Force Statistics, Bureau of Labor Statistics, http://www.dlt.ri.gov/lmi/pdf/usadj.pdf.

40. “A Tale of Two Countries: Starbucks in India and China,” Starbucks Stories (March 27, 2014), https://news.starbucks.com/news/a-tale-of-two-countries-starbucks-growth-in-india-and-china.

41. “Starbucks Has an Eye-Popping New China Plan: Open a Store Every 15 Hours for 4 Years,” Fortune (May 16, 2018), http://fortune.com/2018/05/16/starbucks-china-plan-store-15-hours.

42. Sharleen Dsouza, “Starbucks Sees India Among Its Top Five Markets, Bloomberg (October 24, 2017), https://www.bloombergquint.com/business/starbucks-sees-india-among-its-top-five-markets-by-2022#gs.uepAcFdd.

43. “Demographics Fuelling Asia's Shifting Balance of Power,” Voice of Asia, 3rd ed. (Deloitte Insights, September 2017), https://www2.deloitte.com/insights/us/en/economy/voice-of-asia/sept-2017/demographics-executive-summary.html.

44. Geoffrey Garrett, “3 Reasons India Isn't the ‘Next China,’” Wharton Magazine (December 3, 2015), http://whartonmagazine.com/blogs/3-reasons-india-isnt-the-next-china/#sthash.hDyPPh9K.dpbs.

45. Anthony Bellofatto, “How Does Language Impact Foreign Investing in a Multilingual Country?” SSRN (June 14, 2017), https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3001825.

46. Sajith Pai, “India Has a New Caste for Native English Speakers Only,” Quartz India (February 5, 2018), https://qz.com/1198086/india-has-a-new-caste-for-native-english-speakers-only.

47. Bellofatto, “How Does Language Impact.”

48. Sriram Iyer, “Why the Indian Stock Market Is on an All-Time High,” Quartz India (January 8, 2018), https://qz.com/1174189/despite-slowing-gdp-growth-whys-the-indian-stock-market-on-an-all-time-high.

49. Luisa Kroll and Kerry A. Doland, eds., “The Billionaire Factory: Roaring Markets, Sudden Fortunes,” Forbes (March 17, 2011), https://www.forbes.com/global/2011/0328/billionaires-11-balwa-bangur-gaur-goenka-billionaire-factory.html#7cf129b617e8.

50. Ananya Bhattacharya, “India Is Adding Billionaires at an Astonishing Rate,” Quartz India (April 24, 2017), https://qz.com/india/967146/india-is-adding-billionaires-at-an-astonishing-rate/.

CONCLUSION

Hedgehogs, Foxes, and the Dangers of Making Predictions

The fox knows many things, but the hedgehog knows one big thing.

—Archilochos

Within the fragments of ancient Greek poetry found among archaeological remnants was the short but insightful line by the poet Archilochos, quoted here.[1] In the 2,700 or so years since those words were written, the distinction between foxes and hedgehogs has been used countless times, perhaps made most recently famous by Isaiah Berlin's entertaining essay titled “The Hedgehog and the Fox,” which was written in 1953.

Berlin expands on the ancient Greek poet's insight to develop the difference between the ideological hedgehog and generalist fox:

There exists a great chasm between those, on one side, [the hedgehogs] who relate everything to a single central vision, one system, less or more coherent or articulate, in terms of which they understand, think and feel—a single, universal, organizing principle in terms of which alone all that they are and say has significance—and, on the other side, those [the foxes] who pursue many ends, often unrelated and even contradictory, connected, if at all, only in some de facto way, for some psychological or physiological cause, related by no moral or aesthetic principle.[2]

The basic underlying logic of Boombustology has been that, when it comes to spotting financial bubbles before they burst, it is better to be a fox. Foxes are more suited to attack mysteries. Hedgehogs, with their depth of understanding, are more effective in solving puzzles.

Ultimately, we adopt a framework for thinking about uncertain future events in the hope that we might be able to gain insight into the probabilities of various scenarios. Is there any way to tell if foxes are genuinely better than hedgehogs in making predictions? Actually, recent research conducted by Philip Tetlock does just that. Tetlock conducted a 201-year study of 284 professional forecasters and asked them to predict the probability of various occurrences both within and outside of their areas of expertise. By 2003, Tetlock had accumulated data on more than 80,000 forecasts.

The results indicate that experts are less accurate predictors than non-experts vis-à-vis predictions in their area of expertise.[3] Two members of the faculty at The Wharton School note that this finding “assaults common sense with evidence.”[4] Tetlock himself uses the language of hedgehogs and foxes to summarize his results:

If we want realistic odds on what will happen next, coupled with a willingness to admit mistakes, we are better off turning to experts who embody the intellectual traits of Isaiah Berlin's prototypical fox—those who “know many little things,” draw from an eclectic array of traditions, and accept ambiguity and contradiction as inevitable features of life—than we are turning to Berlin's hedgehogs—those who “know one big thing,” toil devotedly within one tradition, and reach for formulaic solutions to ill-defined problems.[5]

Tetlock's conclusion that those with the ability to incorporate new information, to update their beliefs, and to adapt to a changing reality by employing multiple perspectives are better predictors provides strong support for the Boombustology approach.

The frameworks presented in this book are explicitly designed to avoid the “one big thing” approach and to generate a “many little things” approach. Ideological reliance on any single lens might prove, as suggested by Tetlock, detrimental to our ability to navigate uncertain, vague, or poorly defined situations. The multidisciplinary approach presented in this book mitigates the likelihood of falling into the “hedgehog trap.”

“So what?” you may say. Let's say you're a fox, and you've identified a bubble using the multi-lens approach. Now what?

Getting to the right answer starts with seemingly boilerplate language that is no doubt heard all the time from investment professionals: it depends. It depends because everyone's situation is different. The reason this phrase is repeated in one form or another is that it's true. You and your (human) financial advisor, if you use one, are the best people to determine your risk tolerance and the need for cash you may or may not have at various points in the future. No book or article, however well-considered, can give you a definitive answer to your needs.

And on top of that is another variable we have to manage: it's impossible to know everything and to be certain of what will happen. As the saying goes, s%*t happens. Life is probabilistic, and certainty is forever elusive.

But just as the five-lens Boombustology focus can elevate your confidence level that you are in fact witnessing a bubble, so too can we make decisions with a higher level of confidence about what to do with this insight.

Although we all make mistakes, we all also have a choice about which mistake we make: an error of omission (not doing something) or an error of commission (doing something). In situations you may deem “bubbly” using the framework proposed in this book, I suggest you make the error of omission. Resist the urge to ride to the top for that last bit of profit. If you recognize that the market has worked its way to the unsustainable conditions determined through application of the five-lens approach, it is better to get out early than late. This may seem to be a perfectly sensible and obvious statement, but imagine the situation in which all of your friends are excited about their daily gains and are chalking up some impressive weekly and monthly returns. The urge to stay invested is like a siren's song drawing you in.

No less a mind than Isaac Newton succumbed to this pressure. In the early 1700s, a British company called South Sea Co. was given a monopoly on South Seas trade by the British government in exchange for assuming debt from a war against Spain. In March 1720, Newton bought some shares of South Sea in the high £100 range. A couple of months later, after the shares had rallied almost threefold for Newton, he sold his stake and exited happy, with a handsome profit. The problem for Newton was that his friends remained invested in South Sea. His envy at their continued success pulled him back into the market. As shares hit £700 in July of that year, Newton invested three times as much money as he originally invested. Doing so (briefly) seemed wise, as company shares peaked at £950 shortly thereafter.

Unfortunately, South Sea Co. was a classic bubble, driven by investor mania stoked by false stories (planted by company management) of fantastical discoveries in the Pacific. Four months later, Newton exited his position at a £20,000 loss—all his savings. For the rest of his life, it is said, he forbade anyone to mention South Sea in his presence. “I can calculate the movement of stars, but not the madness of men,” Newton noted.

Legendary trader Paul Tudor Jones made his reputation by successfully calling the Black Monday crash of 1987, making a then-unheard-of $100 million profit from it. He makes no secret that a core of his investing strategy includes constant worry that he is overconfident and therefore on the wrong side of a market. In a 2000 interview, he explained that his focus begins with protecting what he has: “At the end of the day, the most important thing is how good you are at risk control—99% of any great trade is going to be the risk control.”[6]

Like Jones, it's best to focus on capital preservation rather than accumulation during times of bubbly dynamics. The reason is the law of percentages, which is simply this: whatever percentage decline you experience, it will take a greater percentage rise to get you back to break-even.

Let's say the Carefree Carrolls of Chapter 2 decide to invest $1,000 in the stock of a very attractive, if purely theoretical, farming enterprise: Unicorn Dairy, Inc. Despite the obvious appealing characteristics of the farm, it has a terrible year, and the value of the investment falls 50% during their first year of holding it.

Luckily for the Carefree Carrolls, the depressed share price allows for a takeover by another publicly traded company, a successful poultry business named Phoenix Farms, Ltd., which can leverage its existing relationships with suppliers and retailers to turn Unicorn Dairy around very quickly. The business combination is a great success, which the market soon recognizes. Just one year after the merger, the Carrolls are thrilled to open their brokerage statement and see the value of their investment has risen 50%! Imagine their disappointment when they see the dollar value listed as $750.

The harsh reality hits them: a 50% fall requires a 100% gain to get back to break-even. For the next three years, Phoenix Farms executes its dairy-poultry–driven business well, and the stock appreciates 10% per year (about the historical average S&P 500 annualized return). Another blunt fact: the Carefree Carrolls won't see their investment value return to its original $1,000 until the fifth year after their horrible first negative year (see Table C.1).

Table C.1 Unicorn Dairy Inc. Hypothetical Performance

Performance

Balance

Opening

$1,000.00

Year 1

–50%

$500.00

Year 2

+50%

$750.00

Year 3

+10%

$825.00

Year 4

+10%

$907.50

Year 5

+10%

$998.08

Year 6

+10%

$1,098.08

The math of losses confirms that it is better to make the error of omission in times of elevated risk, and the Boombustology framework is designed to help you identify times of elevated risks. It takes discipline to do this, but it can be done. It's worth noting that in December 1999, an authority no less than Barron's declared that Warren Buffett, who had sat out the dotcom boom, was “losing his magic touch” by refusing to invest in internet stocks.[7] He opted to err on the side of omission rather than risk making an error of commission. After the dotcom bubble burst and technology investments plunged in value through 2003, Berkshire Hathaway gained 25%.

In the aftermath of the Great Recession, a team of managers from one of the world's largest sovereign wealth funds came to Boston to speak with a handful of investors and academics, and I had an opportunity to talk with them. The managers had read the first edition of this book, which came out in early 2011. Here's a quick summary of their thoughts (paraphrased): “We have read the academic literature that says bubbles don't exist,” they said to me, referring back to the efficient market hypothesis. “The bursting of the supposedly non-existent U.S. real estate bubble was very painful and disruptive to our investment plans. We lost a ton of money. We'd like your help thinking about how to navigate some of the uncertainty.”

The advice I offered was the same as what I've offered here: in times of elevated risks (defined by using multiple lenses to confirm a high probability of bubbly dynamics), make the error of omission. I suggested they consider surrendering upside when they grew concerned. The key, I said, was to increase the odds of avoiding a catastrophic error of commission.

As Jeremy Grantham of GMO once told me, when you see a bright light coming toward you (and you happen to be in a tunnel) and it appears to be approaching rapidly, it's smart to step off the train tracks. You're allowed to use common sense in the face of investing orthodoxy that commands otherwise. While this may not be an exact quote, the sentiment of his message to me was, “You don't need to get run over by the train to prove you're a long-term investor.”

Blinded by Focus

Recall the distinction made earlier in the book between mysteries and puzzles. The ideal approach to addressing one is usually ineffective in addressing the other. We learned that focus and expertise were essential to address puzzles, but that they could be detrimental in the domain of mysteries. The key, when addressing mysteries, is to utilize multiple perspectives and connect, rather than generate, the proverbial dots.

Yet think about the incentives our world has presented us. Business thinkers, organizational psychologists, and leadership trainers point to domain expertise as an enduring source of advantage in today's competitive environment. The logic is straightforward: learn more about your job and acquire expert status, and you'll go further in your career.

A 2015 analysis[8] by Georgetown University of employment data in the United States confirmed that specialization pays. A new computer science college graduate makes about 50% more than the average made by recent graduates with humanities and liberal arts degrees. In this environment, what would you want your child to study?

Yet there are many examples of generalists who made groundbreaking contributions to a very specialized field. Alexander Graham Bell was a speech therapist and amateur telegraph user who pursued the telephone, whereas telegraph specialist competitor Elisha Gray didn't. Or what about the fact that many of today's leading tech companies (Facebook, Microsoft, Apple) were founded by college drop-outs who never had the time to specialize? Beyond such anecdotal evidence, academic research on everything from jazz to theoretical mathematics has found extensive evidence that specialization hinders creative thinking by embedding certain thought patterns and heuristics into a person's approach.[9]

New ideas emerge from a combination of seemingly unrelated information. Highly organized approaches work in training the brain to react in normal circumstances but make us less apt to recognize and react to potentially informative anomalies. And given that bubbles generally are not normal-course events, the need for mold-breaking approaches is essential to spot them before they burst.

Creative thinking—seeing more possibilities from the pieces of information you are provided with—makes you more likely to piece together an unexpected answer. It will help you identify times of elevated risks when others are lulled into complacency by all the good news.

As the saying goes, to a man with a hammer, every problem appears to be a nail. Globalization and economic and financial interconnectedness are the reality of today's socio-political-economic existence. This means you need to adapt your thinking to be a toolbox, rather than a specific tool. National economies and large corporations are increasingly in need of dynamic, flexible human capital that knows how to think, not what to think.

The five-lens methodology of identifying bubbles before they burst is built on the philosophical foundations of liberal education. Richard Levin, president of Yale University, noted that “it is not subject-specific knowledge but the ability to assimilate new information and solve problems that is the most important characteristic of a well-educated person.”[10] He goes on to explain the rationale for this belief: “The logic behind this approach is that exposing students to multiple disciplines gives them alternative perspectives on the world, which prepares them for new and unexpected problems.”[11] Given that few financial bubbles are “expected” or are certain, a liberal arts approach is ideally suited to thinking about such ambiguous developments.

As you utilize the frameworks presented in this book, remember that the only thing that appears certain is uncertainty. Remember that the dynamic, interconnected world in which we live is likely to present situations that transcend the tools provided by single disciplinary lenses. Remember that there are inherent differences between puzzles and mysteries. Last, but not least, remember that despite the allure of “it's different this time” explanations, it's usually not different.

Notes

1. Fragment 201, trans. Douglas E. Gerber, in Greek Iambic Poetry: From the Seventh to Fifth Centuries BC (Cambridge, MA: Harvard University Press, 1999).

2. Isaiah Berlin, The Hedgehog and the Fox (Chicago: Elephant Paperbacks, 1953).

3. Philip Tetlock, Expert Political Judgment: How Good Is It? How Can We Know? (Princeton, NJ: Princeton University Press, 2005).

4. Adrian Tschoegl and J. Scott Armstrong, “Review of Tetlock's Expert Political Judgment,” International Journal of Forecasting (2007), 339–342.

5. Tetlock, Expert Political Judgment.

6. Larissa Fernand, “Paul Tudor Jones: Don't Focus on Making Money, but Protecting What You Have,” Morningstar (September 26, 2016), https://www.morningstar.in/posts/38127/1/dont-focus-on-making-money,-but-protecting-what-you-have.aspx.

7. Andrew, Barry. “What's Wrong, Warren?” Barron's (December 27, 1999), https://www.barrons.com/articles/SB945992010127068546.

8. Anthony P. Carnevale, Ban Cheah, and Andrew W. Hanson, “The Economic Value of College Majors” (Georgetown University Center on Education and the Workforce, May 7, 2015), https://cew.georgetown.edu/cew-reports/valueofcollegemajors/.

9. Florenta Teodoridis, Keyvan Vakili, and Michaël Bikard, “Can Specialization Foster Creativity? Mathematics and the Collapse of the Soviet Union” (January 2017), https://msbfile03.usc.edu/digitalmeasures/teodorid/intellcont/Can%20Specialization%20Foster%20Creativity%20MarshallWebsiteJan2017-1.pdf.

10. Richard Levin, “Top of the Class: The Rise of Asia's Universities,” Foreign Affairs (May/June 2010).

11. Ibid.

ADDENDUMA

Passive Investing Bubble?

If everyone indexed, the only word you could use is chaos, catastrophe. The markets would fail.

—John Bogle

One of the biggest battles in the investing world over the past two decades has been between active and passive approaches to investing. The efficient market hypothesis, which we described in Chapter 1, contends that market prices reflect all available information; therefore, there is no advantage whatsoever to doing research or conducting analysis of any sort. Better instead, the argument goes, to take prices as given and minimize investment fees. In recent years, the boom in passive investing suggests that allocators tend to agree with the efficient market hypothesis.

Is this a problem? Well, as noted in the quote from John Bogle, if everyone were doing it, the markets would fail. Bogle, arguably the father of passive and index investing as founder of Vanguard, is being literal. That is, he says if literally everyone were investing passively, through an index fund, no one would be trading individual securities or helping to determine appropriate prices for securities. The price-discovery mechanism would break down, and prices would no longer reflect a negotiated agreement between buyers and sellers. Capital allocation would grow increasingly inefficient, and securities prices would deviate further and further from fundamentals.

Of course, the odds of everyone actually investing passively are probably very low. But is there a point at which passive investing becomes a big enough percentage of investing activity that prices grow increasingly distorted, dramatically enough to call the result “a bubble”? I believe there is, and we may be arriving at that level soon if we aren't already there. There plausibly may be more than $24 trillion assets worldwide invested passively. That's one-third of global equity assets now playing follow-the-leader, supposedly acting as price-takers. The proposition passive investing offers clients is simple: prices are right, so we'll just take those prices and minimize expenses.

Many react cynically to any suggestion that passive, index-based investing is dangerous. People argue that active managers are simply being self-interested in trying to shoo people away from passive investing in order to charge them higher fees. I don't think that's true, although active managers often feel it is precisely their hard work that enables passive investors. After all, how are prices actually determined, if not by the buying and selling decisions of market participants?

Howard Marks, the founder of Oaktree Capital Management and a very successful investor, explained the situation this way:

The wisdom of passive investing stems from the belief that the efforts of active investors cause assets to be fairly priced—that's why there are no bargains to find …. The irony is that it's active investors—so derided by the passive investing crowd—who set the prices that index investors pay for stocks and bonds, and thus who establish the market capitalizations that determine the index weightings of securities that index funds emulate. If active investors are so devoid of insight, does it really make sense for passive investors to follow their dictates?[1]

Yet who hasn't heard the drumbeat that active managers probably won't beat “the market” because they face trading costs and taxes that indexes don't? On top of that, active managers have higher costs because their price-discovery efforts come from research efforts and related expenses—from their subscription to The Wall Street Journal, to the salaries of the analysts digging into business fundamentals and potential profits.

Essentially, passive investors piggyback on the price-discovery efforts made by active investors. The “right prices” are determined by lots of forces, including fundamental equity analysts, data-crunching quants, and tips from the uncle who tells you at Thanksgiving that he “knows a guy” with unique insights. Valuing a company is subject to many assumptions and judgments, most of which are formed by active investors digging into industry and corporate dynamics.

The foundation upon which the passive-investing castle has been built is a valid price mechanism. Passive investing is sensible when prices are “right” (i.e. when only a portion of the market is passive and there is a vibrant segment of active investors setting prices). If a stock is overvalued, someone will sell it. If it's undervalued, someone will buy it. These buys and sells drive securities toward their accurate prices, insofar as the word accurate has any meaning. Chapter 1 covers this logic more fully.

With passive investing, price is based on flows, all else being equal. All stocks in an index move up with inflows; all stocks in the index move down with outflows. The individual company characteristics don't matter. Buying and selling pressure negotiating the price of an individual security is lost as passive investing gains share. There are definitely undervalued companies outside the index. There are likely companies that are overvalued within the index that have appreciated on the back of strong inflows. This creates confusing dynamics and lots of distortions to underlying prices.

Distorted prices lead to misallocated capital via a classic reflexive dynamic. And that creates bubbles. Passive investors, believing they are price takers, fail to realize they've increasingly become price makers.

At some point, the level of passive investing is so large that there is no true price-discovery mechanism taking place, and the markets can, in fact, fail: perhaps not total failure, but at a level equivalent to past bubbles we have examined, maybe larger. What is this level?

Bogle says passive could be as much as 90% of the market without affecting the market efficiencies.[2] I think the level is lower than that, although where exactly is more difficult to say. There is evidence that indexing is already skewing market dynamics, with trends pointing to passive investors already having a disproportionate share of incremental investment flows. Even if it is not, there is little doubt that in a downward-trending market, passive investing might greatly increase volatility. Recall how Long Term Capital Management's strategy worked, until it didn't. I fear passive will “work” until it also doesn't.

Feedback Loops and Reflexivity

There's a concept in physics called the Heisenberg uncertainty principle. First theorized by a German physicist in the early twentieth century, it has to do with the practical problem of determining an atom's velocity and position. Essentially, the principle says that you can never know the true velocity and position of an atom because the act of observing it has an impact on it. The broad idea is simple: the very act of observing a phenomenon affects it. You cannot help but be a participant, and participation can't help but be an action that alters. You can never know what dynamics might have transpired had you not looked.

This is also true of passive investors. The actions of market participants change the market. No matter how passive an investor you are, the act of investing means you have changed something—a security's price, the way market participants react to the price, the way related securities react to the original security's price, and so forth. Time, volume, perception, and price all change, no matter how hands-off you intend to be. This is the logic of Soros's reflexivity theory. The supposed “price-taking” actions of passive investors is, to debatable degrees, “price-making.”

So how big is the impact passive investors are having on the market? Very. And I believe the levels of distortion to market dynamics are large enough that they are creating significant market risks. Similar to what has transpired in other bubble dynamics, passive investing is creating a dynamic where inflows into index-based products become increasingly responsible for the gains the indexes see, causing a reflexive disequilibrium in the markets as higher prices generate more demand. The vast majority of indexes weight their holdings by market capitalization, which means as indexes buy their components, they cause the market caps to rise, which then amplifies their weighting in their own index.

What happens to stocks that are added and dropped from the most popular indexes? Indexing proponents say there is no effect on stock prices, or, illogically, that there is only a positive effective, regardless of whether a stock is added to or dropped from an index. The debate is muddled because of market complexity; it's not obvious how to isolate the effect that entering and exiting an index has on a stock. One thing is clear: index membership is a driver of flows into a security, and these flows have an impact on prices. Researchers with the National Bureau of Economic Research confirmed that there is, in fact, an index effect by studying securities that were shifted in and out of indices between 1996 and 2012.[3] Being added to an index generates inflows.

Let's look at it another way, to see how blind following of other people's decisions can lead to massive distortions. While the story that follows is fictional, it's intended to illustrate how passive dynamics distort markets.

There's a local farmer's market selling a wide variety of fruits, vegetables, and herbs every week. It's where villagers go for the ingredients for their big Sunday meal. Everyone in this village takes great pride in their Sunday meal, and everyone has their own specialty: some prepare steak and potatoes, other make a risotto, and still others prepare elaborate salads. One person, Julia, makes tomato sauce. Over time, Julia gets a reputation for having the best Sunday meal—people want to be invited over to her house for dinner, her sauce is a favorite when she makes it for the annual fair, and everyone in town agrees it's the tastiest and most nutritious. Occasionally, someone else makes a meal or their own sauce that is better, but over time Julia's is consistently the best.

One day, a fellow cook, John, who has long been frustrated in his attempts to make a meal as tasty as Julia's, gets the idea to follow her at the market and write down everything she buys. Julia buys two ounces of capers, one bunch of basil, a yellow onion, four cloves of garlic, coriander, thyme, tomato paste, bay leaves, dried orange peel, a few saffron threads, and, of course, tomatoes. That's the most important ingredient for a tomato sauce and the most voluminous.

Julia takes a long time at each stand, scrutinizing every branch of basil, measuring each onion she chooses, and eying each tomato for ripeness and size (that takes the most time of all); John can't believe how long! But he takes careful notes on how much she bought from which vendors, and starts buying the same things every week, saving a lot of time and effort. Pretty soon, people think John's sauce is on par with Julia's. So, other people start copying what Julia is buying, and their sauces get pretty good, too. Soon the residents aren't making their other dishes any longer, but rather are making the great Julia-style sauce. In fact, this village gets a reputation for having the best tomato sauce in the region—you can't help but have a good meal, no matter where you go.

This affects how much the farmers are selling at the market. The basil farmer who used to sell many, many bunches to a host of people who loved to make pesto finds she now has more customers, but everyone is buying just one bunch. Her business is about the same, maybe a little less overall. The farmer who sells tomatoes, which are by far the largest ingredient in Julia's sauce, finds a lot more people are buying—he's in danger of selling out. The under-ripe and water-swollen tomatoes he never used to sell are fetching as much as the best he has on offer, thanks to all these new customers. To meet demand, he starts having his workers pull every tomato they can from the field each week. Other farmers notice this, too, and begin to switch crops, grow their own tomatoes, and bring them to market. Tomato demand is surging as more and more people abandon their own recipes and start making Julia's. Farmers realize it's cheaper to grow poor-quality tomatoes quickly, since the price at the market keeps going up. They abandon very tasty but fragile Black Kim and Kumato tomato varietals for hybrid ones that can be picked fast and handled roughly. Tomatoes used to cost $1 a pound but now cost $10 a pound. Everyone makes Julia's tomato sauce.

And John? He's busily selling a pamphlet with the recipe to the out-of-towners who come to the market themselves, and he is paying bus drivers to let their passengers off at a corner he hangs out at near the market. The bank is making loans for people to buy tomatoes, and enterprising farmers are letting people put $3 down to buy $100 worth of tomatoes. You can't even get steak anymore, and certainly not rice for risotto, but no one really notices.

Until, one day, Julia just can't bring herself to buy the ingredients for her famed tomato sauce. The prices of onions, basil, and garlic seem higher than they should be, and tomatoes are the priciest—all for hard, part-green, tasteless tomatoes that result in a watery sauce. She decides to make something else. She checks the prices from one small farmer in an empty corner of the market who has held out on his own and continued to raise chickens and some root vegetables for the odd person here and there who doesn't make tomato sauce.

Julia sees that whole chickens and some vegetables are very cheap. So she buys a chicken and some parsnips, potatoes, and carrots and makes a roasted chicken for dinner. Her guests that night adore it: it's quickly a legendary meal, as word gets around. Next week, no one wants tomatoes; everyone wants chicken. Quickly there are dozens and dozens of farmers saddled with tons of unwanted tomatoes—they slash the price but can't sell them all. Farmers at the market can't afford to pay for their stalls, and there's even the possibility that the market will have to shut down because it can't pay its rent. The tour buses stop coming because it just isn't worth the price to pay for very expensive, mediocre sauces that don't live up to the village's past reputation. Plus the fields on the way into town are rife with flies and smell terrible from all the unpicked rotten tomatoes.

My market allegory may be a bit overdone, but you can see my point. And there are more similarities between it and the way the stock market is currently forming than you may think. Table A.1 shows the 10 largest ETFs by assets. As the largest company by market cap, Apple Inc. not surprisingly is represented in most of them. I've tallied the weight and value of each ETF’s Apple holdings. The funds without Apple exclude it because they are either internationally focused or restricted to small-capitalization companies.

Table A.1 The World's 10 Largest ETFs and Their Ownership of Apple

Note: Data is from ETF fact sheets as of December 31, 2018. https://us.spdrs.com/en/etf/spdr-sp-500-etf-SPY QQQ data from Bloomberg.

Symbol

ETF Name

AUM

($Billions)

Apple

%

Apple Value ($Billions)

SPY

SPDR S&P 500 ETF

$249.9

3.38%

$8.4

IVV

iShares Core S&P 500 ETF

$149.4

3.38%

$5.0

VTI

Vanguard Total Stock Market ETF

$94.8

2.70%

$2.6

VOO

Vanguard S&P 500 ETF

$90.6

3.40%

$3.1

QQQ

Invesco QQQ

$60.6

9.73%

$5.9

EFA

iShares MSCI EAFE ETF

$62.3

–

$0.00

VEA

Vanguard FSE Developed Market ETF

$65.1

–

$0.00

IEFA

iShares Core MSCI EAFE ETF

$52.3

–

$0.00

VWO

Vanguard FTSE Emerging Market ETF

$55.2

–

$0.00

IJH

iShares Core S&P Mid-Cap

$41.7

–

$0.00

Total Value

$922.0

$25.04

As the table shows, more than $25 billion of Apple securities are directly attributed to being held by these ETFs, among the five equity funds holding Apple. This is around 3.5% of Apple's market cap, which is notable, but probably not cause for concern.

Let's widen our gaze a bit. The most popular equity index—in terms of investment products based on it—is the S&P 500 Index. There are $3.4 trillion assets indexed to the S&P 500 as of December 2018.[4] Apple is 3.42% of the index, which in turn means about $116.3 billion of Apple stock is held just in S&P 500 Index funds alone. That's more than 15% of Apple's market cap.

Beyond the S&P, there is almost $7 trillion in equity index ETFs and mutual funds. Apple is approximately 3% of the 10 ETFs in the table. If that's representative of the larger indexing universe, then $210 billion of Apple stock, or around 28% of its market cap, is held because of the company's inclusion in an index. That's a lot of follow-the-leader money. And I believe it's possible that percentage is even larger in practice, as discussed next.

Distortion of the Market

Equity indexes have been around since 1896, when Charles Dow developed the Dow Jones Industrial Average and the Dow Jones Railroad Average, which later became the Transportation Average. As a pair, they reflected the economic activity of the United States. Indexes didn't really become a proxy for how to invest until the 1970s, when the first index mutual funds were offered, followed by Exchange Traded Funds in the 1990s. It took about a generation for these products to really catch on: actively managed equity strategies drew in $1 trillion of investor money from 1988–2006. Since 2007, about the same amount has flowed out of active and into passive, according to AllianceBernstein research. In 2006, there were 800 indexes. A decade later, in 2016, there were about 5,000 indexes in existence.

At the same time, the number of public companies has been shrinking. In 1996, there were 7,322 U.S. stocks. By 2012, the number was less than half that.[5] Many of the listing losses have been in small market caps. Two decades ago, some 4,000 companies would have been below inclusion of the Russell 2000 index of small-cap companies. Today there are fewer than 1,000, meaning stock pickers have fewer places to go to find mispriced stocks. The Wilshire 5000 Index, widely considered the best reflection of the total U.S. stock market, is down to 3,485 components. The last time the Wilshire 5000 actually had 5,000 stocks was in December 2005.[6] Oops.

What is potentially happening here is a shift from securities markets that allocate capital efficiently through a free economy to a type of “serfdom,” to quote a thoughtful 2016 paper on the effects of passive investing from a group of analysts at AllianceBernstein.[7] As the authors explain: “The commonality between both active market management and the Marxist approach is that in both cases there are a set of agents trying—at least in principle—to optimize the flows of capital in the real economy. It is just such a feature that is lacking in passive investment management.” Passive investing, they argue, is worse than Marxism.

The logic is as follows: any “real world” industry will find its way to a fair price, meaning the price of an asset, such as a commodity, will bring together the optimal combination of demand and supply—at some point. Active management allows this to happen much more quickly—indeed, the commodities markets are the clearest example of how active buyers and sellers adjust their price expectations to future anticipated demand and supply. Central planning, as we saw in the decades-long real-world application in the USSR, is much slower, but we could still argue it was forward looking.

Passive investing, however, is driven by past performance, rather than future expectations. The market will correct for that as stock prices of some components underperform and others overperform, but not without a longer time horizon, which means much less efficient allocation of capital, and much more volatility.

Again, Howard Marks eloquently captures the spirit: “When everyone decides to refrain from performing the functions of analysis, price discovery and capital allocation, the appropriateness of market prices can go out the window (as a result of passive investing, just as it does in a mindless boom or bust).”[8]

By buying in bulk, and without discriminating between its components, index investing blends the rise of the exceptional alongside the mediocre. And the index premium rises as markets scale fresh heights with new inflows, as has happened in this decade, so we further disconnect valuations of individual stocks from their fundamentals. A financial innovation that lowered costs to investors while also gaining them wider exposure to economic activity now drives the market higher (rather than following the fundamentals). Price-taking has turned into price-making, which fuels confidence in the brilliance of the strategy. Flows are driving returns, which in turn spur more flows.

Oversimplification as Overconfidence

Investors who see gains in the stock market are more likely to buy at higher prices based on psychological biases in their abilities and the stock market outlook.[9] Good times are expected to continue, leading investors to insufficiently adjust their expectations to a range of possible futures. Human behavior like this has been a rule of thumb among professional investors for more than a century. Passive investing encourages this overconfidence in a much broader universe of investors with an academic “good housekeeping” approval. As a result, many have taken greater risks than they may realize. Fund flow-driven gains may ultimately prove fleeting.

Ordinarily, investing at ever-higher values should result in diminishing returns on your investment. That is, as the stock moves closer to its “fair” fundamental value, there should be less profit for investors to capture. Yet the shift of that trillion dollars in assets from active management to passive over the past decade is probably in some form rewarding the confidence of investors without a commensurate decline in investment returns. For example, the Shiller PE ratio of the S&P 500 has a mean of 16.6, going back over 100 years, as Figure A.2 shows.

Figure A.2 The Cyclically Adjusted Price-to-Earnings (CAPE) Ratio of the S&P 500

Source:http://www.multpl.com/shiller-pe/table.

The ratio at the burst of the Roaring Twenties bubble was 32.56. It was 44.2 at the peak of the dotcom bubble and was recently 33.54. This doesn't necessarily mean any reversal is imminent—the stock market passed the 1929 PE ratio in 1997, and the dotcom bubble extended its run for another three years. But it might suggest, as noted in the conclusion to this book, that it's time to use caution. Make the error of omission. Sit out the last set of gains. It's after the seventh-inning stretch, but as bubble cheerleaders always argue with me, baseball games can go into extra innings.

Like the dotcom bubble, which dismissed traditional valuation and operating performance metrics as exemplifying an antiquated paradigm, the outright dismissal of active equity (or bond) investing as speculation is a troubling sign of overconfidence.

Take the rise of robo-advisors as an alternative to active managers. Robo-advisors is a florid name for preset formulas that take investor profiles and recommend asset allocation among a series of passive funds. “This is really something that's welcomed by the vast majority of retail investors who find themselves inadvertently … tasked with the responsibility of managing their financial well-being,” University of Pennsylvania law school professor Jill E. Fisch said in a Wharton school post in early 2018. “People don't want to do this and they don't want somebody to give them advice on how to do this. They want somebody to do this for them. That's the space … robo-advisors are going into.”[10]

Robo-advisors work, says financial columnist Felix Salmon in a Wired column, because “they can use modern technology to make it easier than ever to just hand your money over, answer a few questions about your risk appetite, and sit back, safe in the knowledge that no one's going to be gambling your money away.”[11] Active management—in which people study company and market fundamentals—is the equivalent of gambling with your money, he contends. “The passive-investing revolution is, wonderfully, well upon us: Every year, billions of dollars flow out of active managers and into index funds or ETFs,” Salmon crows.

Perhaps most telling is this tidbit from the web in August 2018: “Just remember what the research tells us: no matter how great active management and market timing may sound, it truly is a loser's game,” the author writes.[12] Where was this story posted? Nasdaq.com's homepage. The message is crystal clear: active investing is a fool's game. Anytime anything is taken as absolutely certain, I grow concerned. There are rarely certainties and there are always feedback loops and unintended consequences. And in this case, the fundamental foundation of price taking seems to be cracking as increasing evidence points to passive investing as price making.

Investment and Interest Rates

There also times where a government action of seemingly small import at the time can become like Chaos Theory's butterfly wings that cause a hurricane half a world away.

In 1993, the Securities and Exchange Commission passed a handful of rules meant to improve the explanation of mutual fund performance to shareholders. Among these changes, the SEC required mutual fund reports to include a line graph comparing the fund's performance against that of “an appropriate broad-based securities market index.”[13] A few fund managers argued with no success that their funds were so diverse in strategy and approach that there would be no fair comparable index. The SEC passed the rule, requiring that whatever index is chosen be made and managed by an unaffiliated third party. This is the origin of the “hypothetical $10,000 investment” graphic mutual funds have today as well as well as the nine-box Morningstar fund–style graphic.

These amendments made sense at the time; they helped investors understand the effect fees and expenses have on their portfolio. The unintended consequence is that these benchmarks have led to closet indexing, where ostensibly active managed funds increasingly reflect the index they are compared against. These charts heightened the career risk of investment professionals, leading them to minimize the risk of losing their jobs. As Keynes noted, “worldly wisdom teaches that it is better for reputation to fail conventionally than to succeed unconventionally.” Portfolio managers the world over began to hug indices.

Business journalist Brendan Coffey suggested in a widely read Forbes piece that there were four companies that controlled the 147 companies that owned everything.[14] What was he talking about? Closet indexing and the power of those who decide what constitutes the index. One large U.S.-based fund servicer in 2011 determined through an internal study of actively managed mutual funds that 95% of the holdings of actively managed mutual funds are determined by their benchmark indexes.[15] That means mutual fund managers are only actively deciding on a tiny portion of their fund, far less than generally perceived, and leaving the rest of their portfolio decisions to the indexes that they are judged against. Why is this?

By largely mirroring their benchmark, they strive to really know a few stocks with the aim of just beating the benchmark with marginal bets. This approach gives them downside protection, in that years of poor performance probably won't be too far below the performance of the benchmark. Performing too poorly against their benchmark could have two consequences managers desperately want to avoid: unhappy fundholders redeeming their shares and taking their assets elsewhere, and fund companies deciding someone else should be managing their fund. Better to fail conventionally.

The effect of shadow indexing is enormous. If we assume that fund managers are roughly similar in behavior today as they were at the time of that survey, and that the markets haven't materially changed in terms of structure (two admittedly suspect assumptions), the sums managed actively that tend to hug indices is in the trillions. How big, specifically? At year-end 2017, there were $12.5 trillion of actively managed mutual fund assets. $11.9 trillion, or 95%, may be de facto invested like an index fund, as career risk drives portfolio managers to avoid bearing risks. Of course, actively managed funds don't have to buy or sell based on any indexes’ actions, which may serve as a damping force on any index-generated market turmoil.

You may say the 95% figure is remarkably high, yet it appears somewhat consistent with a trend shown by a 2009 study of equity mutual fund holdings by K.J. Martijn Cremers and Antti Petajisto, then both of Yale. The pair looked at how much of a mutual fund's holdings differed from its benchmark index, whether higher, reflecting the mutual fund manager's bullishness on a stock, or less, which is an implicit bearish stance by the manager. Holdings that were identical to the benchmark index are considered passively managed.

They determined that closet indexers, which they defined as those with 60% or less of assets of a different weighting than their benchmark, managed more than 30% of all mutual fund assets by 2003, the end year of their study period.[16] By their reckoning, 40% of all large-cap, non-index funds were passively managed in 2003—back when the ETF and passive investing phenomenon was just starting to gain significant momentum. This was still early in the rise of index funds. That year, ETFs were 2% of all fund assets, barely a footnote in the financial world.

Later, a separate study of mutual funds between 2002 and 2010 in 32 countries, primarily European but including the United States and Asia Pacific nations, based on the same methodology, found 20% of mutual fund assets examined were closet indexers. Another 22% of assets in the sample countries were explicitly indexed assets in 2010, for a total of 42% of global equity assets directly mirroring a passive investing philosophy.[17]

Thanks Morningstar!

Herd Mentality and Silent Leadership

It's one thing to see individual investors opting to passively invest their assets. Our concern should be heightened when institutional investors catch the fever, too. How deep is in the infection among the so-called smart money, the outliers who have demonstrated exceptional outperformance of the market over long periods of time?

Here there are indications that they are highly influenced by passive investing too, whether or not they recognize the dangers of passive investing. Indeed, those making their living as active managers should, we might imagine, be predisposed against a passive approach. An unpublished study of the equity holdings of billionaire U.S. hedge fund managers shows an apparent herd mentality as well. This group, whose fortunes came from their profits from past (active) market performance, managed U.S. equity assets of $222 billion in the study period, which covered 2015.[18]

Their equity holdings, as reported to the SEC, were largely concentrated in a small group of stocks. Apple constituted about 3.5% of hedge fund billionaire equity portfolios. It was their largest holding (Apple was around 3% of the S&P 500 Index that year). This group's top-10 securities constituted 20% of their holdings. The fifth-biggest holding among the aggregated position of this group of hedge fund billionaires: the S&P 500 Index ETF, one of two ETFs in the top-10 aggregated holdings. In sum, more than 12% of billionaire hedge fund manager portfolios, $27.5 billion, were in ETFs!

While hedge fund billionaires capture headlines, pension fund managers tend to quietly have an outsized influence on markets. And they, too, are increasingly shifting toward passive strategies. As Amin Rajin, writing for Create Research, noted in a 2017 report, Dutch pension plans now have the default stance of investing passively unless there is some compelling reason not to do so.[19] In the U.K. a 2014 report recommended that the government shift pension funds to passive investing.[20]

A separate 2018 report from Create Research based on a poll of pension fund managers overseeing $4 trillion found that 82% use passive investments as an established part of their portfolios or were starting to establish them in their portfolios.[21] Much of their shift is being driven by the low cost of index funds. The passive approach becomes more compelling in times of low interest rates, when return objectives are increasingly difficult to achieve. Overall, of the group studied, 32% of the $4 trillion in assets are passive.

Even as they're loading up on passive investments, this same group of pension fund managers recognize that passive investments could destabilize the markets, with 68% saying passive investing is “just buying yesterday's winners,” 52% seeing the potential for unintentionally overweighting stocks held in various indexes, and 42% predicting more volatile boom and bust cycles.[22] This is from a group whose job it is to generate predictable and sustainable growth. Recall the callout box in Chapter 5 about career risk, herd behavior, and client-driven bubbles.

University endowments are also feeling the pressure to shift to passive investing. In 2016, schools with more than $1 billion in endowments lost an average of 1.9%. Schools under $25 million lost 1%. Houghton College, with an endowment of $46 million, posted the best gains that year, 11.85%, by passively investing its portfolio. Harvard University, which has the largest college endowment—some $37 billion in assets—outsourced management of large portions of its public assets to external managers in 2017. And in early 2018, members of the Harvard Class of 1969 demanded the endowment shift into passive investments, in a letter[23] to the administration. The irate alumni noted: “If half the endowment … had been in the S&P 500 index, where it would have cost literally nothing to manage then Harvard would have saved half the payments to Harvard Management, amounting to $68.8 million.” But it’s worth noting that Harvard's costs were not dramatic: the school's expenses for managing its endowment amounted to 0.57% in 2016[24]—almost exactly equal to the average expenses of U.S. mutual funds and ETFs that year.

For the year ended June 2017, approximately 27% of endowment equities were passively managed (14% of bonds were indexed), according to Kenneth Redd, the senior director of research and policy analysis for the National Association of College and University Business Officers. Institutions participating in the survey held $204 billion in equities, meaning $55 billion was indexed at the time.

Sovereign wealth funds, which control almost $7 trillion in assets, are another factor to consider. How much sovereign wealth funds invest in passive funds is difficult to determine, although judging by the marketing efforts of index providers toward sovereign wealth managers, it is likely seen as a growth area. Abu Dhabi invests half of its $825 billion fund in passive or index-replicating strategies, New Zealand invests a substantial portion of its $28 billion fund passively, and Australia's $150 billion Future Fund uses passive investing as a core component of its strategy.[25] A 2017 Preqin study found that 48% of sovereign wealth funds indicate a preference for gaining equity exposure via index funds.[26]

Too Much of a Good Thing

We have directly identified $8 trillion of global equity assets that are passively invested. As much as another $12 trillion are closet-indexed, and some undefined yet extant level of sovereign wealth fund investments are also passively invested: some level between the $500 billion we identified earlier and $2.7 trillion, the total amount of sovereign wealth fund equity assets as determined by a State Street study.[27] Higher education has $55 billion of passively held stocks, plus some difficult-to-determine level of foundation endowments are passively invested as well.

The market capitalization of all the world's stocks is $73 trillion.[28] We know a minimum of 12% of those assets are passively invested. Perhaps one-third of the world market cap is plausibly passively invested, depending on our confidence in insights into closet indexing.

Clearly, passive investing is not as passive as some believe. It is distorting prices and is increasingly forcing prices further from fundamentals in a dynamic that risks massive disruption. So what will the popping of the passive investing bubble look like? What sparks it could be anything from a shocking miss by Apple on its earnings, to a fiscal crisis in a large European nation such as Italy, a U.S.-China trade or military war, or, most probably, something else.

A shift in market inertia is likely to snowball as outflows beget more outflows. Because a few major indices lie under most passive investment schemes, the companies that have large passive shareholder bases (those that likely benefit the most with inflows) will face regular and possibly severe selling pressure. The selling will appear indiscriminate because investors selling passive strategies will create a ripple of selling in the index components. Just as non-economic buying propelled prices upward, so too will non-economic selling lead prices to plunge. For indices weighted by market capitalization, this means the biggest companies will be sold without regard to price. The selling will likely feed upon itself.

Owners of active mutual funds that have hugged indices (the closet indexers) will see their funds suffering, too. Since open-ended mutual funds don't trade during the day, each day's losses will extend the pain as mutual fund holders place sell orders after the end of trading, seeing their new, lower fund values. All the amplification we've seen to the upside in a bull market will reverse in a bear market.

The bottom line is that passive investing has distorted the information value of prices. Historically, the price-news relationship told investors a great deal about the “market’s mind” and what it was thinking. When companies that reported great news saw their stocks not react, it led to the belief that the positive news was already factored into the price of shares. Likewise, when a company that reported bad news saw its shares fall, the news and price action led to confirmation that the news was not “factored in” to the price of the stock.

But what if passive investing has distorted the informational value of price signals when paired with news? Is it possible that passive investing might be making markets significantly less efficient? Consider what Stanley Druckenmiller, one of the greatest money managers ever, said in a September 2018 interview with Kiril Sokolov on Real Vision TV:

I've also struggled mightily—and this is really concerning to me, it's about the most troubled I've been about my future as a money manager maybe ever, is what you mentioned: the cancelling of price signals…. When you take away price action versus news, from someone who's used price action-news as their major disciplinary tool for 35 years it's tough and it's become very tough. I don't know where all this is all going.

Notes

1. Howard Marks, “Investing without People” (memo to clients, June 18, 2018), https://www.oaktreecapital.com/docs/default-source/memos/investing-without-people.pdf.

2. Eric Platt, “Vanguard's Jack Bogle Predicts Passive Investing Takeover,” Financial Times (October 27, 2017).

3. Yen-cheng Chang, Harrison Hong, and Inessa Liskovich, “Regression Discontinuity and the Price Effects of Stock Market Indexing” (National Bureau of Economic Research working paper 19290, August 2013), http://www.nber.org/papers/w19290.pdf.

4. S&P 500, https://us.spindices.com/indices/equity/sp-500.

5. Craig Doidge, G. Andrew Karolyi, and René M. Stulz, “The U.S. Listing Gap,” https://fisher.osu.edu/sites/default/files/the_us_listing_gap_july_12_final.pdf.

6. Robert J. Waid, “Wilshire 5000: Myths and Misconceptions” (November 2014).

7. Ingio Fraser-Jenkins et al., “The Silent Road to Serfdom: Why Passive Investing Is Worse Than Marxism,” AllianceBernstein (August 23, 2016).

8. Marks, “Investing without People.”

9. University of Missouri-Columbia, “Buying High in the Stock Market Caused by Overconfidence,” Science Daily (April 11, 2016), https://www.sciencedaily.com/releases/2016/04/160411152829.htm.

10. “The Rise of the Robo-advisor: How Fintech Is Disrupting Retirement,” Wharton University of Pennsylvania (June 14, 2018), http://knowledge.wharton.upenn.edu/article/rise-robo-advisor-fintech-disrupting-retirement/.

11. Felix Salmon, “Beware of Roboadvisors Bearing Low Fees,” Wired (March 20, 2018), https://www.wired.com/story/beware-roboadvisors-wealthfront-betterment.

12. Jeff Bernier, “Is Active Management Truly a Loser's Game?” Iris (July 26, 2018), https://www.iris.xyz/advisor/active-management-truly-losers-game.

13. Securities and Exchange Commission, “Disclosure of Mutual Fund Performance and Portfolio Managers” (April 6, 1993), https://www.sec.gov/rules/final/33-6988.pdf.

14. Brendan Coffey, “The Four Companies That Control the 147 Companies That Own Everything,” Forbes (October 26, 2011), https://www.forbes.com/sites/brendancoffey/2011/10/26/the-four-companies-that-control-the-147-companies-that-own-everything/#13cc819f685b.

15. Brendan Coffey, internal study, results provided as not-for-attribution by a U.S. fund servicing firm.

16. K.J. Martijn Cremers and Antti Petajisto, “How Active Is Your Fund Manager?” Yale School of Management (International School of Finance working paper 06-14, March 31, 2009).

17. Martijn Cremers, Miguel A. Ferreira, Pedro Matos, and Laura Stark, “Indexing and Active Fund Management: International Evidence,” Journal of Financial Economics 120, no. 3 (June 2016).

18. Brendan Coffey, 2016 unpublished study based on publicly disclosed quarterly U.S. equity holdings of hedge funds managed by the 15 wealthiest U.S. finance billionaires, as determined by Coffey.

19. Amin Rajan, “Active Investing: Shaping Its Future in a Disruptive Environment,” Create Research (2017).

20. Ibid.

21. Create Research, “Passive Investing: Reshaping the Global Investment Landscape,” 2018.

22. Ibid.

23. Lucas Ward, “Alumni Write to Bacow Urging New HMC Invetsment Strategy,” The Harvard Crimson (February 17, 2018).

24. Rob Kozlowski, “Mutual Fund, ETF Fees Continue to Drop – Morningstar,” Pension & Investments (April 27, 2018).

25. Figures are from the most recently available investment strategy reports from each of the sovereign wealth funds cited.

26. “The 2016 Preqin Sovereign Wealth Fund Review,” http://docs.preqin.com/samples/The-2016-Preqin-Sovereign-Wealth-Fund-Review-Sample-Pages.pdf.

27. Elliot Hentov and Alexander Petrov, “How Do Sovereign Wealth Funds Invest?” (State Street Global Investors: January, 2018), https://www.ssga.com/investment-topics/asset-allocation/2018/how-do-sovereign-wealth-funds-invest.pdf.

28. Jeff Desjardins, “All of the World's Money and Markets in One Visualization,” The Money Project (October 26, 2017), http://money.visualcapitalist.com/worlds-money-markets-one-visualization-2017.

About the Author

Dr. Vikram Mansharamani is a global trend-watcher and equity investor who shows people how to anticipate the future, manage risk, and spot opportunities. The 500-million-member LinkedIn network listed him as their #1 Top Voice for Money, Finance, and Economics for both 2015 and 2016, and Worth Magazine profiled him as one of the 100 Most Powerful People in Global Finance. Millions of readers have enjoyed his unique multi-lens approach to connecting seemingly irrelevant dots. Dr. Mansharamani also regularly advises sovereign wealth funds, endowments, foundations, and family offices on how to manage their allocations in the face of overwhelming uncertainty.

Dr. Mansharamani is currently a lecturer at Harvard University, where he teaches students to use multiple perspectives in making tough decisions. He has also taught classes at Yale University on business ethics and governance, financial bubbles, and economic inequality. In addition to teaching, he also helps several Fortune 500 CEOs and their top management teams to navigate the radical uncertainty in today's global business and regulatory environment. He has a PhD and two masters degrees from MIT and a bachelors degree from Yale University, where he was elected to Phi Beta Kappa.

Index

- 3 Rs. See relief, recovery, and reform

- 5Cs. See confidence, collateral, credit, conditions, and capital

- A

- active mutual funds, indices, 335

- adjustable rate mortgages (ARMs), 214

- adjustment, 70–71

- Adoration of the Magi (Mantegna), 168

- affordability‐based approach, 224

- affordability‐based valuation, 36

- Age of Diminished Expectations, The (Krugman), 165

- aggregation, paradox, 29

- Allen, Lewis, 144, 147, 151, 156

- AllianceBernstein, analysis, 327

- All Nippon Airways, 173

- Alt‐A loans, 219, 263

- growth, 219f

- ambiguity, 85

- Amsterdam, banking systems (stability), 134

- anchoring, 70–71

- Anhui Salt Industry Corporation, Platinum Bay development, 246

- animals, group decision making, 115

- annual world trade volumes, 102t

- ants

- characteristics, 113–114

- pheromone, emission, 114

- Apple, ETF ownership (ranking), 325f

- appraiser confidence, 171

- art market, bubble economy (relationship), 167–168

- art, mergers/acquisitions/transactions/value, 170

- Ascent of Money, The (Fergusson), 205

- Asia

- confidence, reflexivity, 194–196

- crony capitalism, cultural coping, 200–201

- cultural homogeneity, 267–268

- East Asia, boom times, 185–188

- export‐oriented game, ground zero, 188

- harmony, Western individualism (contrast), 201–202

- miracle/myth, 196–198

- past/future, 198–200

- stock markets, performance (1993), 187f

- Asian financial crisis, 185, 192, 262–263

- boombustology, 193–202

- cheap money, 267–268

- epidemics/emergence, 281–282

- financial innovation, 267–268

- five‐lens approach, 202t

- leverage, 267–268

- overconfidence, 274

- asset markets

- debt/deflation, impact, 29

- mispriced money, impact, 29

- reflexive dynamics, destabilizing, 263–264

- asset prices

- affordability‐based valuation, 36

- debt deflation, relationship, 40–41

- impact, consideration, 15

- assets

- credit‐fueled asset bubble, 293

- economic exposure, 266

- athletes, experience (provision), 110

- Austrian School of Economics

- beliefs, 153

- business cycle theory, 30, 46–52

- availability, 67–70

- bias, 77, 78, 179, 274

- heuristic, 62, 67–68

- B

- Bachelier, Louis, 11

- balance sheet recession, 267

- Bank of Amsterdam

- balance sheet balloons, 135t

- total balances, increase, 135

- Bank of International Settlement (BIS) data, 246

- Bank of Japan, 173

- goal, 177

- interest rate, increase, 174

- banks

- failures, presence, 151

- loan rates, example, 32t

- losses, transmission, 194

- risk‐taking, 278

- Barbara, Robert, 186

- base rates, inattention, 63–64

- BCA Research, 232–233

- bees

- characteristics, 112–113

- logic, application, 117

- streakers, 113

- swarm, 181

- waggle dance, 112

- behavioral decision‐making, 65

- Behavioral Finance (Burton), 13

- Bell, Alexander Graham, 317

- Belt and Road Initiative (China), 235

- benchmark indexes, 331

- Benedict, Peter S., 299

- Berkshire Hathaway, gains, 315

- Berlin, Isaiah, 311

- Bernanke, Ben, 43

- Bernstein, Peter L., 97

- Best Buy, limited supply, 73

- bias

- availability bias, 77, 78, 179, 274

- cognitive bias, 64

- confirmatory bias, 77

- decision‐making biases, 68

- ease‐of‐recall bias, 68–69

- emanation, 63

- psychological biases, 328

- representative bias, 274

- retrievability‐based biases, 69–70

- retrievability bias, occurrence, 69

- Big Bull Market, 159

- Big Short, The (Lewis), 222

- biological frameworks, 105

- Bitcoin, 99, 119–120

- boom, 20

- events, 119–120

- reflexive dynamics, 20

- Black, Fischer, 14

- black market economy, 88

- Black Monday crash (1987), 314

- Black Thursday, 150

- Black Tuesday, 150

- blood‐based relationships, 200

- Bogle, John, 319

- boom/bust cycles

- impact, 70

- market receptivity, 96

- theory, development (Fisher), 40–41

- boombustology

- action, 287

- Great Depression, 152–161

- roadmap, 285t

- Booms and Depressions (Fisher), 40

- booms/busts, 120–121

- capitalist phenomenon, 95

- efficiency boom/bust, 15–17

- emergence phenomenon, implications, 120–121

- financial booms/busts, 78t

- puzzles, perspective, 5

- reflexive lens perspective, 18

- unsustainable boom, maturity, 106–109

- booms, unsustainability

- identification method, 259

- presence, signal, 275

- Borg, Joseph, 42

- Boston (magazine), cover, 215f

- Brazil, Russia, India, China (BRICs), 290–291

- Brin, Sergey, 69

- Bubble Economy, 166

- bubble economy (Japan), 165–175

- bubble economy, art market (relationship), 167–168

- bubble indicator

- skyscrapers, 269–271

- Sotheby's stock, 168–171

- bubbles

- bursting, 120

- identification, 232, 259

- passive investing bubble, 319

- search, 283–284

- speculative bubbles, 108

- Buddhism, 163

- Buffett, Warren, 57, 119, 224, 293

- building cost index, home price index (contrast), 207f

- bull market, 118, 149t

- epidemic thinking/silent leadership, 159–161

- Burj Dubai, 270–271, 295

- Burton, Edwin T., 13

- business cycle theory (Austrian School of Economics), 30, 46–52

- Busted Thirties, transition, 147–152

- buyer confidence, 171

- buying demand, 152

- C

- capital

- capital‐driven growth, source, 248

- markets, considerations, 290–292

- production, 197

- structure, macroeconomics (relationship), 48–49

- capitalism

- crony capitalism, 191, 267–268

- implosion, 22

- capital misallocation, 228, 234, 268–269

- price distortion, impact, 321

- career risk, 118

- Case Shiller home price index, 223f

- Case‐Shiller housing price index, 206

- Case‐Shiller price index, peak, 220

- causality, establishment, 154

- Celtic Tiger (Ireland), 305

- central banks

- involvement, 49–50

- money creation, 49–52

- Chancellor, Edward, 150, 153, 158, 163, 167, 173

- chance, perspectiveness, 65–66

- Chanos, Jim, 231, 232, 286

- Chaos Theory, 330

- Charles Schwab accounts, 120

- cheap money, 264–271

- housing expense, equivalence, 213–214

- cheap money (China), 231–236

- China

- Belt and Road Initiative, 235

- building, unsustainability, 235

- buyers, excess, 239–240

- capital‐derived growth, measure, 237

- cheap money, 231–236

- citizens, variation, 244–245

- consensus, 244–246

- consumption, conspicuousness, 238–241

- credit‐fueled investment boom, 227

- credit‐fueled investment boom/bust, boombustology, 249–250

- credit growth, deceleration, 233

- currency outflow, 191

- debt‐to‐income, increase, 249

- deflation, potential, 231–236

- devaluation, 191

- economy, US economy (synchronicity), 232

- epidemics, 244–246

- equilibrium, 228–231

- growth, sustainability, 237–238

- growth, tension, 242

- household debt, increase, 249

- housing prices, increase, 249

- India, comparisoin, 303

- investment‐driven growth addiction, 237

- investment, percent of GDP, 237

- lending volume, 232–233

- leverage, 231–236

- market‐focused communist economy, 250

- market interest rates, impact, 233

- moral hazard, 241–244

- one‐child policy reversal, 248–249

- outstanding loans, 229f

- overconfidence, 238–241

- overinvestment, 236, 240–241

- political distortion, 241–244

- price‐to‐income ratios, 229f

- property boom/bust, five‐lens approach, 251t

- property boom, US housing bust (connection), 232

- property bubble, credit (impact), 247

- property markets, 228, 243–244

- residential mortgages outstanding, 247

- rights, 241–244

- silent leadership, 244–246

- Sky City, cancellation, 241

- Starbucks, presence, 303

- state owned enterprises (SOEs), 233

- steel production/global market share, 234f

- stock market, 245

- instability/immaturity, 250

- story, unsustainability, 247–249

- US dollar, currency linkage, 231–232

- yuan investment focus, 230

- China Banking Regulatory Commission, constraints, 243

- China investment bubble, 263

- cheap money, 268–269

- epidemics/emergence, 282–283

- financial innovation, 268–269

- leverage, 268–269

- overconfidence, 275

- policy‐driven distortions, 279

- China Ordinance Group, land development, 246

- Chrysler Building, 156, 273

- client‐driven bubbles, 118

- closet indexers, 332

- CNOOC, 85

- Coffey, Brendan, 331

- cognitive bias, 64

- cognitive function, 18

- cognitive issues, 70–77

- cognitive processing, 68

- Cohen, Steven A., 275

- Coinbase, 120

- coin, flipping, 117

- collateral

- collateral‐based lending, 264

- credit, reflexivity relationships, 212

- rates, debt dynamics (relationship), 31–40

- value, 293

- collateralized debt obligations (CDOs), 210

- collateral values

- increase, 261

- reduction, 219

- Communism, impact, 85–88

- Communist Manifesto, 83

- Community Reinvestment Act (CRA), 216, 278

- companies, comparisons, 34t

- comparative advantage, theory, 100–101

- competition, increase, 100

- composition, fallacy, 29–30

- concentration risk, absence, 210

- confidence

- feedback loops, 151

- reflexivity, 194–196

- confidence, collateral, credit, conditions, and capital (5Cs), 194–196, 198, 222

- framework, 260–261

- confirmatory bias, 77

- conformity, psychology, 177–179

- congruence heuristic, 77

- conjunction fallacy, 66–67

- Conoco, forcing, 87

- consensus beliefs, direction, 245

- consumable goods, corporate capital expenditures/consumer purchasing (increase), 153–154

- Consumer Financial Protection Bureau (CFPB), 211–212

- Consumer Price Index (CPI), 42

- consumer purchasing, increase, 153

- consumption

- decisions, interest rates (impact), 50f

- investment, 49

- Cook, Tim, 296–298

- Coolidge administration

- economic prosperity, 145

- hands‐off approach, 157

- CoreLogic Housing Credit Index, 223

- corporate capital expenditures, increase, 153

- corporate investing, interest rates (relationship), 37

- corporate nonproperty borrowing, shift (Japan), 181

- cost/benefit analysis, complexity, 61

- Create Research, 333

- creative destruction, 103

- creative thinking, 317

- credit

- collateral, reflexivity relationships, 212

- conditions, unsustainability (signs), 269

- cycles, impact, 53f

- disbursement, 268

- expansion, 165

- scores, reduction, 219

- Cremers, K.J. Martijn, 331

- Crestone Energy, 84

- Crisis of Global Capitalism, The (Soros), 22

- crony capitalism, 191, 267–268

- cultural coping, 200–201

- Crypto‐Craze (2017), 42

- cryptocurrency, Google searches, 283–284

- cultural homogeneity (Asia), 267–268

- Culver, John, 303

- currency

- appreciation, 267

- depreciations, 274

- price, determination, 201

- cyclicality adjusted price‐to‐earnings (CAPE) ratio, 329f

- D

- Dash, Michael, 128

- de Angelis, Pompeius, 139

- debt

- amplification power, 31t

- debt‐deflation theory (Fisher), 30, 41

- deflation, asset prices (relationship), 40–41

- deflation, combination (problem), 267

- descriptions (Minsky), 39t

- dynamics, collateral rates (relationship), 31–40

- impact, 29

- repayment, usage, 51

- debt‐to‐income (China), increase, 249

- decision‐making biases, 68

- decision‐making processes

- biases, 62

- impact, 78–79

- optimum, 77

- decisions, fairness (impact), 73–74

- deflation

- debt‐deflation theory (Fisher), 30, 41

- impact, 29, 30

- potential (China), 231–236

- Dell, Michael, 292

- democratic socialism, 95

- detection, factors (consolidation), 24–25

- Devil Take the Hindmost, 164

- disequilibrium, 9–10

- reflexive disequilibrium, 322

- signs, 264t

- tendencies, 152–153

- Dodd‐Frank Act (2010), 211

- dollar, examination, 74–76

- domestic jobs, protection, 101

- Dow, Charles, 326

- Dow Jones Industrial Average, losses recovery, 150

- down payment, 36

- Downs, Anthony, 167

- Druckenmiller, Stanley, 336

- durable goods, corporate capital expenditures/consumer purchasing (increase), 153–154

- E

- ease‐of‐recall bias, 68–69

- East Asia

- boom times, 185–188

- interconnections, 187

- money, impact, 262–263

- East Asian Miracle (World Bank), 186, 274, 281

- East Indies

- riches, 137

- trade, domination, 130

- economic bust, initiation, 148, 150

- economic slowdown, 157

- economic thinking, influences, 11

- economy, growth sources, 248

- Edwards, Jonathan, 98

- Edwards, Sebastian, 231

- efficiency

- boom/bust, 15–17

- forms, 11–12

- logic, success, 25

- reflexivity, reconciliation, 25

- efficient market hypothesis, 10–17

- acceptance, 14–15

- evidence, solidity, 14

- origins, 11

- emergence

- definition, 106

- lens, 105

- phenomenon, implications, 120–121

- scholars, industry, 109

- emergent complexity, examples, 110

- emergent order, occurrence, 111

- emergent swarm intelligence, 113

- emerging markets

- managers, benchmark, 282

- Third World, 190

- Empire State Building, 156, 273

- End of Poverty, The (Sachs), 296

- endowment

- effect, 76–77

- equities, passive management, 334

- Engels, Friedrich, 83

- Enron, transactions (truth), 3

- epidemic lens, 105

- epidemics/emergence, 244–246, 280–283

- indicators, 284t

- epidemic thinking/silent leadership, 159–161

- epidemiology

- definition, 106

- emergence, 105

- equilibrium, 9

- illustration, 10f

- overshooting/undershooting, 9

- equilibriumists, impact, 17

- equilibrium‐seeking dynamics, 230–231

- equity

- amount, 36

- holdings, SEC report, 332–333

- index, 326

- value, 31

- error of commission, 313

- error of omission, 313

- events, personal experience/knowledge, 67–68

- exchange‐traded funds (ETFs), 290

- asset ranking, 325

- expenses, 334

- fund asset percentage, 332

- GLD gold ETF, evidence, 24

- ranking, 325t

- Extraordinary Popular Delusions and the Madness of Crowds (Mackay), 177

- Exxon Building, purchase, 167

- ExxonMobil, forcing, 87

- F

- fairness

- impact, 73–74

- ultimatum question, 74

- false substitutes, sale, 265

- Fama, Eugene, 11, 12

- fame, analysis, 69

- families, comparison, 34t, 35t

- Far Eastern Economic Review, 181, 281

- Federal Deposit Insurance Corporation (FDIC), 159, 277

- Federal Home Loan Mortgage Corporation (FHLMC), 216, 278

- Federal National Mortgage Association (FNMA), 216, 278

- Federal Reserve System

- balance sheet, 45

- creation, 147

- leadership, Krugman description, 206

- printing press, 52

- feedback loops, reflexivity (relationship), 322–326

- Fergusson, Niall, 205

- Fey, Bruno, 59

- fiat currency, 98

- Finance One, value/collapse, 192

- financial arena, equilibrium (application), 10

- financial booms/busts, 78t

- financial bubbles, 160

- likelihood, evaluation, 127–128

- financial deregulation (Japan), 180

- financial efficiency, implications, 14–15

- financial extremes, study, 1

- financial innovation, 152, 260–265

- financial instability hypothesis (Minsky), 37–40, 46, 136, 213–214, 264

- financial intermediaries, development, 267–268

- financial products, development, 160

- financial world, equilibrium‐oriented view, 10

- Fisch, Jill E., 329

- Fisher, Carl Graham, 144

- Fisher, Irving, 40

- booms/busts, cycle theory (development), 40–41

- debt‐deflation theory, 30

- Fisher, Len, 110–113

- Flipkart, 298–299

- Flip That House, 220, 282

- Flip This House, 220, 282

- Florida

- land boom, 145–146, 262

- real estate bubble (1920s), 143–147

- focus, problem, 105–106, 316–318

- Ford II, Henry, 288

- foreign capital inflows, impact, 261

- foreign direct investment (India), 291f

- foreign fund flows, reversal, 194

- Four Little Dragons (Vogel), 274

- Fox, Justin, 14

- fractional reserve banking

- money creation, relationship, 46–47

- usage, 47

- framing reversal, 71–73

- Frankel, Mark, 131

- Frazer, William Johnson, 157

- free trade, analysis, 102

- French, Doug, 134

- Friedman, George, 242

- Friedman, Milton, 43, 81

- From Manet to Manhattan (Watson), 167

- fundamentals, 134

- futures contracts, conversion, 137

- future, vision, 156

- G

- Gadkari, Nitkin, 295

- Galbraith, John Kenneth, 127, 143, 148, 156, 287

- gambler's fallacy, 65

- Garber, Peter, 130, 134

- GDP targeting, 241–242

- General Motors, share price (increase), 155

- General Theory of Employment, Interest, and Money (Keynes), 118

- Gennaioli, Nicola, 264

- Germany, Walmart (exit), 298

- Gibson, George Rutledge, 11

- Gilbert, Bradford Lee, 269–270

- Gladwell, Malcolm, 3, 6

- glamour stocks, fall, 150t

- global capitalist system, disintegration, 22

- global financial crisis, 169

- Global Financial Stability Report (IMF), 231

- gold, 97–99

- dynamics, 23–24

- prices, efficiency, 24

- reflexivity, 24

- standard, 151, 277

- Gonczy, Anne Marie, 47

- Goodhart, Charles, 241

- Goodhart's law, 241

- goods

- cheapness, 101

- surge pricing, 73

- Gould, David, 102

- government guarantees, 269

- Grantham, Jeremy, 118, 316

- Gray, Elisha, 317

- Great Crash, 143, 154, 161

- conversion, 151

- economic bust, 153

- Great Depression, 143, 147, 262

- boombustology, 152–161

- cheap money, 266–267

- criticism, 153

- disequilibrium tendencies, 152–153

- economic contraction, 205

- epidemics/emergence, 280–281

- epidemic thinking/silent leadership, 159–161

- financial innovation, 266–267

- five‐lens approach, 160t

- interest rates, problems, 153–154

- leverage, 266–267

- market participants, psychological state, 154–156

- overconfidence, 272–273

- policy‐driven distortions, 277

- return, 37

- world, emergence, 151–152

- great depressions, debt‐deflation theory, 40–41

- Great Recession, 43

- aftermath, 316

- Greenspan, Alan, 17, 95

- Griffin, Kenneth, 292

- Gross Domestic Product (GDP) incentivized government agencies, 279

- group dynamics, 115–117

- group order, emergence, 280

- Groves, Dick, 236

- H

- Haberler, Gottfried, 100

- Hayek, Friedrich (triangle/production stages), 48f

- Hayekian triangle, 48

- hedge income‐debt relationship, 38

- Heisenberg uncertainty principle322

- helicopter drop, 43–44

- herd behavior, 117–119

- herd mentality, silent leadership (relationship), 332–334

- heuristics

- congruence heuristic, 77–78

- impact, 63

- usage, 61–70

- Holland, liquidity/money, 134–136

- Holy Grail of Macroeconomics, The (Koo), 174

- home equity loans, interest, 218

- home loans, refinancing, 216

- home ownership, social psychology, 214

- home price index, 209f

- building cost index, contrast, 207f

- Homo economicus, 57

- Hong Kong Stock Exchange, 173

- Hortus Academicus, establishment, 128

- House Banking Committee, Soros testimony, 21

- household debt (China), increase, 249

- houses, comparison, 34t, 35t

- housing. See United States

- after‐tax cost, 217

- expense, cheap money (equivalence), 213–214

- housing finance companies (HFCs), 293

- housing prices

- increase (China), 249

- interest rates, dynamics, 213

- Huang, Yasheng, 237

- humans

- biased human decision‐making process, 60

- decision‐making process, 114

- group decision making, 115

- irrationality, 78t

- objectives, differences, 116

- simple rules perspective, 114

- swarms, emergent behavior, 114–119

- Hurricane Harvey, 73

- I

- ideas, word‐of‐mouth dissemination, 108

- ideological reliance, 313

- index‐based investing, danger, 320

- index funds, cost, 333

- India

- automation, 299–301

- boombustology, 287

- capital markets, considerations, 290–292

- China, comparison, 304

- culture, 296–299

- debt, 292–295

- development, problem, 301–302

- economic performance, 291

- ETFs/bonds, 306

- foreign direct investment, 291f

- GDP growth, increase, 298

- global production hub, 301

- investment, 292–295

- “Make in India,” 301–302

- middle class, 302–306

- per capita income, decline, 297

- population, 292–295

- potential/reality, distinction, 288–289

- residential real estate market, total loans outstanding, 294

- shadow banking, credit crunch, 294

- Starbucks, presence, 303

- underdevelopment, 296–299

- Versailles, 295–296

- vocation, 296–299

- workers, potential, 302

- Indonesia, stock market (1995‐1999), 194

- induction, problelm, 18

- industrial patterns, destablizing, 276

- inefficiency, 18–24

- infection rate, 105, 106

- removal rate, contrast, 107f, 108f

- information cascades, 117, 119

- instability, 18–24

- credit cycles, impact, 53f

- production, 38

- installment purchases, 152

- intentional misleading, impact, 116–117

- interest rates

- affordability‐based valuation, 36

- corporate investing, relationship, 37

- housing prices, dynamics, 213

- interest‐rate mortgagees, 214

- investment, relationship, 330–332

- leverage, relationship, 42

- manifestation, 234

- problems, 153–154

- International Monetary Fund (IMF), 189

- Global Financial Stability Report, 231

- investment

- bubble, 241

- decisions, interest rates (impact), 50f

- interest rates, relationship, 330–332

- stages, 48–49

- invisible hand, efficiency/reflexivity duality (investment implications), 25

- Ireland (Celtic Tiger), 305

- Irrational Exuberance (Shiller), 107, 206

- irrationality

- impact, 12

- study, 59, 61

- irrelevance, power, 70–71

- Ito, Takatoshi, 180

- J

- Jacques, Martin, 240, 245

- Jae‐in, Moon, 299

- Jakarta Stock Exchange Composite Index, performance, 193

- Japan

- art market, bubble economy (relationship), 167–168

- asset boom, reflexiv foundation, 175–176

- asset prices, rise, 180–181

- bank loans, growth, 175

- bequest strategy, 180

- bond market, deregulation, 181

- bubble economy, 165–175, 181

- bursting, 173–175, 174f

- characteristics, differences, 163–165

- conformity, psychology, 177–179

- consensus society, 182

- corporate nonproperty borrowing, shift, 181

- credit‐fueled bubble economy, 163

- credit, growth (rapidity), 262

- currency control, 177

- deposits/loans, interest rate ceilings, 180

- economy, growth, 165–166

- financial deregulation, 180, 181

- financial sector, deregulation/liberalization, 176

- hidden real estate values, 173

- housewives/preschool/pollock, 181–183

- land boom, 165–167

- land, collateral, 175–176

- Lost Decade, 41

- macroeconomic policy, effects, 176–177

- Nikkei 225 Index (1980s), 172f

- physiology, 164

- property market, boom/bust sequence, 175–176

- real estate prices, taxes (impact), 180

- sector valuations, 173t

- speculation, herd behavior, 182

- stock market, verticality, 171–173

- trophy properties, 165–167

- wealth‐creation event, 173

- Japan as Number One, 179, 281

- Japan as (Anything But) Number One (Woronoff), 273

- Japan boom/bust, 163, 201, 262

- cheap money, 267

- epidemics/emergence, 281

- financial innovation, 267

- five‐lens approach, 182t

- leverage, 267

- overconfidence, 273

- policy‐driven distortions, 277–278

- political/regulatory conditions, 179–181

- Jeddah Tower, 271

- Jensen, Michael, 14

- Jones, Paul Tudor, 314

- K

- Kahneman, Daniel, 58–59, 61–64

- fame analysis, 69

- psychology work, 66

- research, 71–72

- Keene, Tom, 23

- Kelly, Brian, 119

- Keynes, John Maynard, 118, 330

- knowledge

- facts, presupposition, 19–20

- test, usage/answers, 60t

- Koo, Richard, 41, 52, 174, 267

- Krugman, Paul, 165, 185, 188, 190

- comments, 274

- leadership description, 206

- resource mobilization, 237

- warning, 196

- L

- labor market distortion, 159

- land, collateral (Japan), 175–176

- Larson, Christina, 250

- Lauder, Ronald, 275

- l'Ecluse, Charles de, 128

- Lehman Moment, 295

- lending against property (LAP), 293

- leverage, 264–271

- creation, 264–265

- excesses, signs, 269t

- foundation, 136

- interest rates, relationship, 42

- power, magnification, 30–31

- procyclical nature, 152

- usage, 144–145, 266

- leverage (China), 231–236

- Levin, Richard, 317

- Lewis, Michael, 16, 202, 223

- likelihood, misconceptions, 65

- Lindbergh, Charles, 156

- lines of credit, interest, 218

- liquidity, depression, 277–278

- locusts, plague, 111–112

- London School of Economics study, 102

- Long, Morgan, 239

- long‐run investments, examples, 50

- Long‐Term Capital Management (LTCM), 321

- equilibrium‐oriented financial models, 16

- hedge fund success, 16

- Lowenstein, Rogert, 42

- Lypsius of Leydan, 139

- M

- Mackay, Charles, 129–130, 137, 139, 177

- MacKay, Harvey, 259

- macro complexity, micro simplicity (impact), 109–114

- macroeconomic policy, effects, 176–177

- macroeconomics

- capital structure, relationship, 48–49

- perspectives, 29

- macro lenses, integration, 52–53

- mad December dash, 95

- Magnus, George, 38

- Mahotra, Kapil, 294

- “Make in India,” 287–288, 299, 301–302

- campaign, rethinking, 302

- malinvestment, 49–52

- Malkiel, Burton, 14, 41

- Mantegna, Andrea, 168

- Marble, Darren, 119

- market

- accommodation, 45

- capitalization, 322, 334–335

- crash (2008), 43

- distortion, 326–327

- freedom/competitiveness, 17

- inertia, shift, 335

- interest rates, impact, 233

- knowledge, 15

- market‐determined prices, informational value (absence), 88

- market efficiency

- credit crunch, impact, 17

- forms, 13t

- Market Vectors India small cap index, 290

- Marks, Howard, 320, 327

- Marshall Plan, 235

- Martin, John, 160

- Marxism, 327

- Marx, Karl, 83

- Massachusetts Institute of Technology (MIT), NIH/CDC cooperation, 92

- mass production, prominence, 147–148

- McAfee, John, 119

- McCulley, Paul A., 9, 29, 39

- McMillon, Doug, 299

- mental accounting, 74–76

- demonstrations, 75–76

- Merton, Robert C., 16

- Miami (Florida) bank clearings, 146t

- microeconomics

- perspectives, 9

- tulipomania, 133–134

- micro simplicity, impact, 109–114

- Milgram, Stanley, 116

- mind‐set, creation, 71

- Minsky, Hyman, 46

- debt descriptions, 39t

- financial instability hypothesis, 30, 37–40, 136, 213–214, 264

- Ponzi financing, 284

- Minsky Migration, 39

- Minsky Moment, 39

- mispriced money, impact, 29

- Mitsui Real Estate Company, 167

- Modern Money Mechanics (Nichols), 47

- Modi, Narendra, 287–290, 299

- money

- liquidity/money (Holland), 134–136

- manufacture, 50

- multiplication, fractional reserve banking (usage), 47

- possession, 158

- total money supply (Roaring Twenties), 155f

- money creation

- central banks, relationship, 49–52

- fractional reserve banking, relationship, 46–47

- money supply, 42–43

- increase, 44

- moral hazard, 243

- moral hazard‐inspired lending, 269

- policy‐induced moral hazard, 277

- mortgage

- balance, usage, 34

- borrowers, creditworthiness (tracking), 223

- interest deduction, 217

- quality, 218–219

- MSCI Emerging Markets Index, 282

- Mulvaney, Mick (appointment), 99

- Mundell‐Fleming model, development, 231

- mutation rate, 108

- mutual funds

- expenses, 334

- performance, 227–228

- mysteries

- secrets, contrast, 2–3

- uncovering, 5–6

- Myth of the Rational Market, The (Fox), 14–15

- N

- Nakamoto, Satoshi, 283

- National Association of Realtors (NAR)

- member display, 222

- membership, 221f

- nationalizations

- example, 84t

- Soviet Union, nationalization, 85–88

- Venezuela, nationalization, 85–88

- natural science, social science (observational distinctions), 19f

- Neely, Christopher J., 45

- Netherlands

- plague, 131

- total mint output, 135t

- New Deal (Roosevelt), 151

- New era thinking, 155

- Newton, Isaac, 314

- New York City, skyscraper (development), 273

- Next 100 Years, The (Friedman), 242

- Nichols, Dorothy M., 47

- Nikkei 225 Index (1980s), 172f

- Nippon Autopolis, 168

- Nippon Telegraph and Telephone (NTT), privatization, 172

- No‐Income, No‐Job or Assets (NINJA) loans, 263

- noise traders, 14

- Nomura Securities, customer (numbers), 181

- non‐economic behavior, motivation, 71

- Nye, Joseph, 2–4

- O

- observations, class (representative), 63

- occurrence

- likelihood, elevation, 67

- probability, 64

- one‐armed analysts, processing capabilities, 2–3

- one‐child policy, China reversal, 248–249

- Only Yesterday (Allen), 144, 153–155

- Open Society and Its Enemies (Popper), 18

- optimism, 31–35, 39, 156

- option contract, purchase, 133

- Ordos City, building, 235–236

- outstanding loans (China), 229f

- overconfidence, 272–276

- indicators, 276t

- manifestation, 274

- oversimplification, relationship, 328–330

- overconsumption, 49–52

- interest rates, impact, 51f

- result, 52

- overindebtedness, impact, 41

- overinvestment, 240–241

- boom, example, 236

- interest rates, impact, 51f

- oversimplification, overconfidence (relationship), 328–330

- ownership, evaluation, 82–85

- P

- Page, Larry, 69

- participating function, 18

- Partridge, Brian, 109–110

- passive investing

- activity, 335

- bubble, 319, 334–336

- effects, 327

- revolution, 329–330

- shift, 333–334

- passive investors, impact, 322

- Paul, Ron, 52, 98

- Paulson, John, 223

- PayPal, goals, 119

- PDVSA, subsidization ability, 87

- Pemex, underinvestment, 83–84

- pension funds, mortgage exposure, 210

- Perfect Swarm, The (Fisher), 110

- performance, hypothetical example, 315t

- Perkins, Dwight, 200

- Petajisto, Antti, 331

- Petronas Towers, construction, 199, 274

- pheromone, 114

- physics envy, 19

- Ping, Zhang, 242

- Platinum Bay development, 246

- policy

- developments, unintended consequences, 279

- distortion indicators, 279t

- policy‐driven distortions, 276–279

- political distortions, evaluation, 81

- political foundations, 81

- Ponzi finance, 136, 213

- weight, 39

- Ponzi income‐debt relationship, 38

- Popper, Karl, 18

- population, home price index (contrast), 208f

- portfolio inflows, 281

- positive feedback loop, example, 195

- positive hypothesis testing heuristic, 77

- Power of Gold, The (Bernstein), 97

- Predictions, dangers, 311

- preference reversal, 71–73

- price determination, 93–94

- market‐oriented approach, 89–90

- methodologies, 81–82

- price mechanisms

- evaluation, 81

- government meddling, 93–96

- prices

- ceiling/floor, 93, 276

- considerations, 201

- discovery processes, 82

- floors/ceilings, 93–94

- generation, supply‐demand dynamics, 85

- guidance, 88–96

- market‐derived prices, argument, 85

- market‐oriented approach, 90–92

- mechanism, distortion, 276

- political distortions, 96–98

- price‐dictation method, 90

- price‐discovery efforts, passive investor piggyback, 321

- price‐taking actions, 322

- price‐to‐income ratios (China), 229f

- volatility, 82

- pricing methodology, determination, 82

- Prince, Chuck, 208

- probability, 66

- order, descriptions, 67

- probability‐weighted value, 72

- problems, approaches, 3–5

- production stages (Hayek), 48f

- productivity, definition, 288

- Prohibition, consequence, 277

- property

- developer, opinions, 282–283

- ownership, meaning, 205–206

- political distortions, 96–97

- price, guidance, 88–96

- property rights

- definition, 82

- evaluation, 81

- existence, 200

- modification/constraint, 96

- modifiers, 85

- political decisions, 96

- range/spectrum, 83

- proprietary trading, 211

- pseudo‐government entities, mortgages (unloading), 278

- psychological biases, 328

- psychology

- impact, 57

- work (Kahneman/Tversky), 66

- puzzle work, 4

- Q

- quantitative easing (QE), 41–46, 49

- quantitative tightening (QT), 44

- R

- Radio Corporation of America (RCA), domination, 155–156

- Rajan, Raghuram, 294

- Rajin, Amin, 333

- Rand, Ayn, 17

- Random Walk Down Wall Street, A (Malkiel), 14

- random walks

- description, 12

- prices, 10–17

- Rapp, Don, 129

- rational choice logic, 57–58

- real S&P Composite Index, moving average earnings (1881‐1929), 149f

- recession, 154

- balance sheet recession, 267

- reflexive disequilibrium, 322

- reflexive dynamics

- destabilizing, onset, 263–264

- five Cs framework, 260–261

- signs, 264t

- reflexive foundation, asset boom (Japan), 175–176

- reflexivity (Soros)

- efficiency, reconciliation, 25f

- feedback loops, relationship, 322–326

- implications, 20–21

- importance, 20

- problems, 263–264

- recognition, 22–23

- self‐fulfilling dynamics, relationship, 260–264

- theory, 10, 18, 322

- two‐way feedback mechanism, 18

- regression, powers (dismissal), 65–66

- relief, recovery, and reform (3 Rs), 158

- removal rate, 106

- infection rate, contrast, 107f, 108f

- rent control, problem, 94

- representative bias, 274

- representative governments, adoption, 95

- representativeness, 62–67

- heuristic, 62

- resources, mobilization, 237

- Retail Credit Company of Atlanta, 145

- retrievability‐based biases, 69–70

- retrievability bias, occurrence, 69

- Return of Depression Economics, The (Krugman), 188–189

- Reuther, Walter, 288

- Ricardo, David, 101

- risk, increase, 37

- risk management, 22

- risk‐reward trade‐offs, 277

- risky loans, exponential growth, 220f

- risky mortgages, rise, 282

- Roaring Twenties, 143

- bubble, 328–329

- market participants, psychological state, 154–156

- political/regulatory conditions, 156–159

- total money supply, 155f

- transition, 147–152

- robo‐advisors, 329

- Rockefeller Center, purchase, 167, 179

- Roosevelt, Franklin D., 23, 151

- administration, economic policies, 158–159

- Rubin, Robert, 2

- rules of thumb, impact, 61–70

- Russia, debt defaults, 188

- Russo, J. Edward, 59, 60

- Rybolovlev, Dmitry, 212

- S

- Sachs, Jeffrey D., 296

- “Safe as Houses,” 205–208

- Saito, Ryoei, 168

- Salmon, Felix, 329–330

- sample size, insensitivity, 64

- Samuelson, Paul, 11, 151

- savings, increase, 51–52

- Scholes, Myron, 16

- Schumpeter, Joseph, 103

- Scott, John, 57

- secrets, mysteries (contrast), 2–3

- Securities and Exchange Commission (SEC)

- creation, 159

- rules, 330

- securities, index effect, 323

- securitizations, 210

- ramifications, 210–211

- success, 211

- Seeking Alpha, 305

- self‐fulfilling dynamics, reflexivity (relationship), 260–264

- self‐seekers, generation, 158

- self‐seeking greed, 157

- semi‐strong form efficiency, 11–12

- serfdom, type, 327

- services, cheapness, 101

- shadow banking

- sector, credit crunch, 294

- system, 210–211

- shadow indexing, effect, 331

- Shah, Bijal, 238

- Shanghai Composite Index, tripling, 249

- Shih, Victor, 243

- Shiller PE Ratio, 328

- Shiller, Robert, 105, 107, 154, 206

- Shiller, Robert (home price index), 209f

- building cost index, contrast, 207f

- population, contrast, 208f

- Shintoism, 163

- Shoemaker, Paul, 59, 60

- short‐term trading, minimization, 277–278

- signal strength, role, 195

- silent leadership, 121, 286

- epidemic thinking, relationship, 159–161

- herd mentality, relationship, 332–334

- simple rules perspective, 114

- Singapore, growth, 198

- Singh, Dinakar, 292

- single‐lens analysis, 4–5

- Sky City, cancellation, 241

- skyscrapers

- bubble indicator, 269–271

- ranking/busts, 270

- Slaughterhouse Federation, 92

- smart money, 332

- Smith, Adam, 10–11, 25, 89

- Smithers, Andrew, 118

- Smoot‐Hawley Act, 101–102

- Smoot‐Hawley tariffs, 102

- Snail House, 246

- social science, natural science (observational distinctions), 19f

- Social Security program, 159

- Sokolov, Kiril, 336

- Solow, Robert, 196

- Soros, George

- House Banking Committee testimony, 21

- reflexive situation, 134

- reflexivity, implications, 20–21

- reflexivity theory, 10, 18, 322

- risk management, 22

- Sotheby's, stock

- bubble indicator, 168–171

- price, 169

- South China Mall, 236

- South Korea, war devastation, 186

- South Sea Comopany, monopoly, 314

- sovereign wealth funds, control, 334

- Soviet Union

- bubbles, 95

- implosion, 196

- nationalization, 85–88

- puzzle, 4

- specialization, success, 317

- speculative bubbles, 108

- speculative income‐debt relationship, 38

- stability, endurance, 23

- Standard & Poor's (S&P)

- Composite Index, real earnings division, 148

- real S&P Composite Index, moving average earnings (1881‐1929), 149f

- Standard & Poor's 500 (S&P 500)

- CAPE ratio, 329f

- Shiller PE ratio, 328

- Starbucks, economic indicator, 303

- stated‐income loans, 214

- state‐owned banks, funding, 279

- state owned enterprises (SOEs), 233, 279

- statistics, fundamental notion, 64

- steel production/global market share (China), 234f

- sticky wages, 151

- Stiglitz, Joseph, 89

- Stock Exchange of Amsterdam, 132

- stock market crash (1929), 151, 281

- streakers, 113

- strong form efficiency, 11–13

- suboptimal decisions, 58

- subprime loans, 214, 263

- growth, 219f

- supply and demand, 81

- drivers (distortion), price ceilings/floors (impact), 97

- dynamics, 85, 89, 96

- price discovery processes, 82

- Sutcliffe, Serena, 239

- swarms. See humans

- intelligence, 113

- leadership, absence, 112–113

- processes, 115–117

- T

- taper tantrum, 45

- tariffs, 99–103

- justification, 100

- tax policies, 94–96

- Tenaris, facility/capital, 86

- Tetlock, Philip, 312

- Thailand

- baht, collapse, 202

- borrowers, currency movement vulnerability, 274

- capital inflow, 189

- currency mismatches, 199

- economic meltdown, 188–193

- foreign capital, 189

- losses, 267

- Thiel, Peter, 119

- Third World, emerging markets, 190

- Thompson, Earl, 132

- Thomson, Derek, 169

- Thornton, Mark, 271

- Tinsulanonda, Prem, 189

- total money supply (Roaring Twenties), 155f

- trades

- annual world trade volumes, 102t

- trade wars, 99–103

- tranche, 210

- Treverton, Gregory, 2, 4

- triangle (Hayek), 48f

- trophy properties, 167

- Truman, Harry, 1

- Trump, Donald, 99, 212, 282

- Tsurumaki, Tomonori, 168

- tulipjobbers, psychological state, 136–137

- Tulipomania, 261

- boombustology, 133–139

- boom/bust, social mood, 131–132

- botanical biology, 139

- bulbs, price behavior, 134

- cheap money, 265–266

- classification/categories, 129

- craze/bubble, 127

- formation, 130–133

- epidemic lens, 139

- epidemics/emergence, 280

- financial innovation, 265–266

- five‐lens approach, 140t

- leverage, 265–266

- microeconomics, 133–134

- option contract, 133

- overconfidence, 272

- policy‐driven distortions, 276–277

- political/regulatory considerations, 137–138

- price correction, 138

- prices, support, 138

- supply and demand‐driven price‐discovery process, 138

- uniqueness, 128–130

- Tulipomania (Dash), 128, 131

- Tversky, Amos, 58, 61–64

- fame analysis, 69

- psychology work, 66

- research, 71–72

- two‐way feedback mechanism, 18–19

- U

- uncertainty

- assessment, 6

- certainty, 78–79

- United States

- Alt‐A loans, growth, 219f

- Boston (magazine), cover, 215f

- building materials, inflation, 207–208

- Case Shiller home price index, 223f

- economic contraction, 205

- economy, Chinese economy (synchronicity), 232

- homeowner society, panic, 205

- house values, boom/bust, 214–216

- money, cheapness, 213–214

- money supply (1920s), 262

- National Association of Realtors, membership, 221f

- risky loans, exponential growth, 220f

- “Safe as Houses,” 205–208

- subprime loans, growth, 219f

- trophy properties, 167

- upper middle class, meaning, 296

- United States housing

- complications, 208–212

- expense, 213–214

- repeat, 223–224

- United States housing boom/bust, 205, 263

- boombustology, 212–222

- cheap money, 268

- epidemics/emergence, 219–222, 282

- factor, 216–218

- financial innovation, 268

- five‐lens approach, 222t

- leverage, 268

- overconfidence, 274–275

- policy‐driven distortions, 278–279

- United States Tariff Act of 1930, 101–102

- university endowments, passive investing shift, 333–334

- unsustainable boom, maturity, 106–109

- urban center, Chinese definition, 238

- U.S. Tax Code, depreciable business assets, 94

- V

- valuation, affordability‐based approach, 224

- value in exchange, 89

- value in use, 89

- Valuing Wall Street (Smithers), 118

- Van Agtmael, Antoine, 190

- Venezuela

- market forces, 87–88

- nationalization, 85–88

- venture capital (VC) funds, 292

- Villard, Henry, 146

- Violetten Admirael van Enkhuizen bulb, sale, 130

- Vogel, Ezra, 273, 274

- von Mises, Ludwig, 29

- W

- waggle dance, 112

- Wallison, Peter, 216, 218

- Warfield, S. Davies, 161

- water‐diamond paradox, 89

- Watson, Peter, 167

- weak form efficiency, 11–12

- Wealth of Nations, The (Stiglitz), 89

- Weisenthal, Joe, 99

- Western individualism, Asian harmony (contrast), 201–202

- what if scenarios, 136–137

- When China Rules the World (Jacques), 240, 275

- willingness to accept (WTA), 76–77

- willingness to pay (WTP), 76–77

- Wilson, E.O., 1

- Winning Decisions (Russo/Shoemaker), 59

- Wood, Christopher, 166

- World Bank, 189

- data, 297

- Woronoff, Jon, 273

- X

- Xiang, Andy, 230

- Xiaoping, Deng, 186

- Xie, Andy, 245

- Xin, Zhang, 245

- Y

- Yawning Thirties, 143

- transition, 147–152

- Yeh, Ken, 239

- YuMi, 287–288

- Z

- Zhongqing, Yin, 227

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