More Praise for The 3 Simple Rules of Investing

“This is the book that investors have needed for a long time. It empowers readers to take control of their financial lives and to tune out the propaganda from the investment industry. It becomes easy for investors to do the right things for themselves once they get rid of all the noise and follow the simple approach that the book recommends.”

—Joseph Tomlinson, actuary and financial planner

“In the world of high finance and investment, in which the top 0.01 percent and the wannabes who work for them are out to make lots more money with your money, without giving a hoot about your interests, this is one of the few books that we nonexperts on such matters can trust to get us started.”

—Bill Domhoff, Professor Emeritus of Sociology, University of California, Santa Cruz

“This very practical book by four expert insiders cuts through all the nonsense and reduces successful investing to a few commonsense principles. It can help you and everybody else—except, perhaps, the huge financial industry that generates lavish profits by misleading millions of investors.”

—Richard C. J. Somerville, PhD, Professor Emeritus and Research Professor, Scripps Institution of Oceanography, University of California, San Diego

THE 3 SIMPLE RULES OF INVESTING

THE 3 SIMPLE RULES OF INVESTING

WHY EVERYTHING YOU’VE HEARD

ABOUT INVESTING IS WRONG

—AND WHAT TO DO INSTEAD

Michael Edesess • Kwok L. Tsui

Carol Fabbri • George Peacock

The 3 Simple Rules of Investing

Copyright © 2014 by Three Rules LLC

All rights reserved. No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the publisher, except in the case of brief quotations embodied in critical reviews and certain other noncommercial uses permitted by copyright law. For permission requests, write to the publisher, addressed “Attention: Permissions Coordinator,” at the address below.

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First Edition

Paperback print edition ISBN 978-1-62656-162-5

PDF e-book ISBN 978-1-62656-163-2

IDPF e-book ISBN 978-1-62656-164-9

2014-1

Production Management: Michael Bass Associates

Cover Design: Dan Tesser/Pemastudio

To our daughters, sons, and spouses:

Hilary, Ariel, Joshua, Matthew,

Suklai, Alex, Luca, Duncan, June,

Hayley, and Mackenzie

CONTENTS

PREFACE

INTRODUCTION

The Simplify Wall Street Portfolio

PART I The 3 Simple Rules of Investing

RULE #1

Simplify Your Options

RULE #2

Look Only Forward

RULE #3

Tune Out Noise

PART II Investing’s 7 Deadly Temptations

DEADLY TEMPTATION #1

Beat the Market

DEADLY TEMPTATION #2

Trust It All to Our Expertise

DEADLY TEMPTATION #3

Precisely Calibrate Your Portfolio

DEADLY TEMPTATION #4

Sleep Well at Night with Your Risk Under Control

DEADLY TEMPTATION #5

Use the Strategies That Always Work

DEADLY TEMPTATION #6

Do What Wealthy and Sophisticated Investors Do

DEADLY TEMPTATION #7

Use Modern Scientific Financial Theory

EPILOGUE

The Invisible Squeeze

Notes

Glossary

Acknowledgments

Index

About the Authors

PREFACE

We wrote this book for all sorts of investors, from novice to experienced professional, because all of us knew—independently and at different times—that something was very wrong with the financial services industry (or simply financial industry, in this book), especially its fastest-growing sector: the securities sector and its huge investment management and advisory services arm. We wanted to explain this problem and then offer an easy-to-use solution.

Michael made an important discovery almost immediately after receiving a degree from MIT and a PhD in mathematics from Northwestern, when he joined a brokerage firm known for its use of sophisticated mathematics. He found that although the firm’s representatives spoke to clients in a language laden with mathematical terms (e.g., “This fund has a low beta but its alpha is high”; “The portfolio has the highest expected return for its standard deviation”), they actually knew nothing about the mathematics and the mysterious formulas and algorithms behind their statements.

Kwok, a professor and renowned statistician whose career spanned roles at Bell Labs and several universities, knew that almost all claims of “beating the market” arose from practices that statisticians recognize as unprofessional and downright wrong. For example, investment firms often boast about strong returns on their investment products—mutual funds and such—in their marketing materials. If you were to read the fine print in these documents, you might uncover the data “supporting” these claims. What Kwok recognized, however, is that many firms do what statisticians call cherry-picking: selecting from a vast body of data only those data points that back up the desired claims, and ignoring the mountain of research that disproves those claims. In other words, investment firms typically reveal only what they want investors to see.

Carol became a financial advisor following an MBA from MIT’s Sloan School of Management and a successful career in management consulting. While working for a large brokerage firm, Carol grew dissatisfied with the direct and indirect fees that the firm charged her clients, and with the lack of a formal responsibility to act in the client’s best interest. Today she continues to see complicated descriptions, fancy terminology, and opaque fee structures mislead hardworking investors, who are tirelessly saving to achieve their dreams.

George was a liberal arts major at Georgetown University who became a financial advisor. He naturally assumed that the methodologies that the industry and his employers promoted were correct. But gradually, over a 20-year period working for several of the best-respected financial firms, he began to realize that most of the foundational assumptions and “best practices” didn’t make sense. For example, increasingly over time, every firm he worked for made a key sales point out of the fact that they would “optimize” a client’s portfolio by running a scientific asset allocation; but actually the process was nothing but GIGO: garbage in and garbage out.

Collectively, we authors have concluded that by honing marketing pitches, the financial industry has created a false framework of concepts that it conveys to clients and sells to prospects. With only a few notable exceptions, the confusing proliferation of investment services and products is of no value to any investor. A minuscule subset of the investment products and advice available—the simplest and least costly ones—are all that an investor really needs. To opt for the more complex or confusing investment vehicles and advice is merely to pay a lot for nothing.

WHO SHOULD READ THIS BOOK—AND WHY?

This book is for every individual or family who invests their savings, has a 401(k) or other tax-deferred vehicle or pension fund that is managed on their behalf, or has merely heard about investing and might do it sometime. It is also for everyone who advises or consults on investing, who manages, oversees, or administers investments, including big wheeler-dealers. To all of these readers, we’ll be telling them that everything they thought they knew about investing is wrong. This declaration should not be that surprising. As many of us remember, a very big investment firm, Long-Term Capital Management, went straight down the tubes in 1998, even though it was run by Nobel laureates in economics and finance. Giant banks, with the money to hire top investment talent, also discovered in 2008 that everything they thought they knew about investing was wrong. As news stories about banking disasters around the world continue to reveal, “experts” keep reaching this sad conclusion with depressing regularity.

The everything that you thought you knew about investing, as noted in our book title, is different depending on which sort of investor you are. Let’s see how the “why” varies with each “who.”

Novice or uninformed investors might think that frequent trading of securities will make a lot of money. Millions of investors trading furiously at their computers this very moment suffer from this delusion, believing that they’re making money—or wondering why they’re not.

Ordinary individual investors also fall victim to misconceptions fed to them and reinforced by a vast array of “helpers.” These helpers comprise a pantheon of advice givers, some of whom may not realize that they’re lending support to misconceptions: financial advisors, financial journalists, radio and television personalities, and financial academics. Even the most conscientious helpers often don’t know that many of the things they think are true about investing are, in fact, wrong. They are themselves victims of misinformation—such as the cherry-picked data and “sophisticated” math lingo we described earlier that promise low risk and high returns—and they unwittingly victimize their clients and audiences in turn.

Other types of ordinary individual investors, informed in the investment field through helpers’ counsel, their own research, or both, may believe they’re doing all the “right” things prudent and smart investors do: making sure to contribute to a 401(k) plan, rebalancing or diversifying their portfolios regularly, putting money in target-date funds, and dollar-cost averaging. Many of these practices, however, are of no use, with some even losing investor dollars.

Less ordinary, so-called sophisticated investors—such as very wealthy people, retirement plan administrators, endowment fund managers, and investment consultants—suffer from different misconceptions. Consider the individual-investor victims of Bernie Madoff—members of the socioeconomic elite. Many of them clamored for Madoff’s investment advice based on the recommendation of friends and family, fellow members of the much-touted “1%.”

Investment professionals—retirement plan and endowment fund managers, investment consultants, and so forth—often have joined a financial firm believing in its stated mission. Accepting organizational values like offering focused attention on unique client needs or helping create security for future generations, these service professionals believe—as George did at first—that they’re helping their clients. But the strategies their firms offer to make good on those values are usually riddled with misconceptions, many of which serve only to enhance the revenues of the firms. And if employed at one of those five or so investment banks that are still often described as “too big to fail,” they’re victims of another misconception: that the mathematical risk models their firm uses protect against disaster. Actually, these models are nearly worthless.

Administrators of large public pension or university endowment funds—and wealthy individuals, too—typically engage a consulting firm that may recommend investing in alternatives like hedge funds or funds-of-hedge-funds. These types of investors probably don’t realize that bloated payments for these all too often inferior alternatives heedlessly squander their money—money that was to protect heirs, finance charities, support needy university students, or secure the pensions of city employees like firefighters and teachers.

And for readers who are financial academics or investors with a technological bent and a fondness for cutting-edge ideas, the misconception dogging them is that employing a quantitative strategy, using an 18-factor mathematical model, or replicating hedge funds will make them millionaires. What they don’t realize is that, again, such models aren’t as effective as they sound and that the fees hidden inside these investment strategies will obliterate the return—if any—they might get.

If you recognize yourself among these types of investors, then we encourage you to read on.

PLAN OF THE BOOK

We begin our main discussion in the introduction, where we describe our recommended “Simplify Wall Street” portfolio, as well as explain why Wall Street and the mainstream financial industry don’t want you to hear our advice. We’ll explain how these entities make their money, and we’ll clarify the terminology and numerical assumptions that we use in Part I.

Part I describes the 3 Simple Rules of Investing, with a chapter dedicated to each one. The first rule is to ignore 99.9% of all the investment alternatives offered to you by the financial industry. That’s not a misprint: a huge number of investment products are available, but 99.9% of them are of no use to any investor. We’ll tell you which two or three should be your mainstays, and a few more that will do no harm and could be helpful.

The second rule is to invest by looking forward. That recommendation may seem rather obvious, but it’s not what most people do. Most people—and their advisors—comb through historical investment performance figures to decide how to invest. Because that information is about as relevant to your investing future as the price of wool in Uzbekistan, you should learn to ignore the past and look only forward.

The third rule is to screen out almost everything that Wall Street and the mainstream financial and investment advice industry tell you, because almost everything they tell you is simply wrong. Tuning out the noise will free you up to pursue simpler yet more rewarding investing strategies.

Following these three rules means taking control of your own financial future. Therefore, it also involves your coming to grips with risk and uncertainty. Let this modified version of Reinhold Niebuhr’s famous “Serenity Prayer” guide you: “Grant me the grace to accept with serenity what I can’t foresee, the courage to plan for what I can, and the wisdom to know the difference.”

Because our 3 Simple Rules don’t include most of the standard recommendations from the investing community, financial media, and academia, we’re going to have to explain why they don’t, or you won’t believe us. This is the objective of Part II, with a chapter dedicated to each of the investing world’s 7 Deadly Temptations. Seductively appealing, these seven claims and recommendations have been molded over many years to sound “right,” through a trial-and-error process of testing client reactions. We’ll tell you why they are all wrong, meaningless, or inapplicable in the real world.

We’ll begin with the temptation to beat the market. Aside from whether it makes sense as a primary goal, both theory and evidence compiled over decades of research show that beating the market is about 99.9% luck and no more than 0.1% skill.

A second and very seductive temptation is to seek wise counsel. Of course this is fine advice—wise counsel can be very valuable. Our real concern is, is the counsel truly wise? So-called wise counsel—no matter how well intentioned it seems or how smart it sounds—may simply lead you into further temptations, temptations that are, at best, of no value and, at worst, will destroy your finances—due partly to the cost of the counsel itself.

A third temptation is to decrease risk and increase returns by submitting to a mathematically calculated asset allocation. Providers of “wise counsel” often use this temptation to entice prospects to become their clients. As we’ll show, however, asset allocation is usually a meaningless exercise (whether the professional advisor realizes it or not) that has to be laboriously rigged to produce acceptable outputs. It does nothing to improve your investment results.

A fourth temptation is to control risk by applying discipline to your investing approach. Of course you want discipline and risk control—who wouldn’t? But many falsehoods and enticements to pay extra fees can lurk in the folds of this temptation. Furthermore, the mainstream financial industry construct of “risk” is different from how average investors define the term. We’ll tell you how understanding this difference can save—and make—you money.

A fifth temptation is to do the things that always work—except that they don’t. These include activities almost always regarded as best practice, so no one doubts that they are correct, such as “Always fund your 401(k) to the max,” “Regularly rebalance your portfolio,” and “Dollar-cost averaging increases return and reduces risk.” The problem is that all of these conventional wisdoms are wrong.

A sixth temptation is to do what the most wealthy and supposedly sophisticated investors do, in hopes that financial reward follows. This temptation often motivates people to hire (at high cost) a major brand-name investment manager or advisor. If you had the chance to have Goldman Sachs as your investment advisor, wouldn’t you do it? You may not believe this, but as we’ll see in Deadly Temptation #6, if you had invested with Goldman Sachs in recent years, your investments would have performed far worse than most of the alternatives.[1]

And the final temptation: use modern scientific financial theory. Nothing is both more seductive and more mind-muddling than the idea that finance uses “science” and “technology.” We’ll show you that, if the finance industry even uses science and technology at all (which is rare), not only does it not get better results, but the science and technology themselves aren’t even that sophisticated and actually are often inaccurate or wrong.

George and Michael follow the 7 Deadly Temptations with an epilogue that will show how our Simplify Wall Street investment strategy might achieve far more than greatly enhancing your own investment prospects. It could also inspire a collective movement to help reduce systemic financial risk and economic and political inequality, and help eliminate the overconcentration of power in the hands of a rich few. The global financial crisis (GFC) of 2007-2009 triggered a lot of discussion about how to regulate banking and finance to prevent more crises. But another way to change the industry is through mass action, by boycotting those investment services that are of no value to clients—namely, the vast majority of them. The more investors adopt some or all of our book’s recommendations, the more they can help reduce the financial complexity that creates systemic risk and global crises.

Following our simple strategy not only benefits your own financial health, but represents a step toward healing the entire financial system—our iteration of “think globally, act locally.”

THE 3 RULES OF INVESTING WEBSITE

References to this book’s website, 3rulesofinvesting.com, appear throughout our discussion. The website goes into more detail about some topics and also shares useful tools, timely news, articles, book updates, and corrections to the book (if there are any). Also included is a page of clickable links to all the reference materials and websites that are mentioned in the book, a forum for discussion, and ways to contact us directly.

INTRODUCTION

The Simplify Wall Street Portfolio

Many people refer to “Wall Street” and the “financial industry” as if these were synonymous. We’ll sometimes take that liberty, too, but in reality, many financial services firms operate far from the actual Wall Street in Lower Manhattan. In discussing the financial industry, wherever it may operate, we follow a definition described in a 2012 paper, “The Growth of Modern Finance,” by Harvard Business School professors Robin Greenwood and David Scharfstein.[1] These authors give a detailed breakdown of the U.S. financial industry:

the securities industry (investment management and advisory services, brokerage, etc.),

credit intermediation (loans, bank deposits, credit cards, etc.), and

the insurance industry.

The securities industry, the fastest-growing sector, is what we mean when we use the term “financial industry” in this book.

Almost everybody gets one thing wrong about the financial industry: they think people in the financial industry know how to make money and therefore can help them make money, too. They think these folks have “magical” ways to make money with money, since most people in the financial industry make good money themselves.

Certainly some people in the financial services industry are smart and do know how to make money, but not in the way you think—not because of any particularly special or high-level knowledge requiring turbo-charged brainpower. This is the sort of fundamental fallacy we’ll explore more fully in Part II, where we discuss the “deadly temptations” common to investors of all types. The fact is, Wall Streeters and others in the financial services industry know how to make money in the most mundane way you can imagine: by charging more for their products and services than it costs to make or deliver them. In other words, they make money by generating a profit. It’s how they generate that profit and why that profit is so big that is the basis of our argument to resist the deadly temptations and follow our 3 Simple Rules instead.

HOW THE FINANCIAL INDUSTRY MAKES MONEY

Every business, even a “nonprofit” business, needs to earn a reasonable profit. A profit is necessary because the alternative is a loss; and if you lose money regularly, you will eventually go out of business. This fact applies whether the business is a lemonade stand selling cold drinks, a retailer peddling women’s clothes, a homeless shelter—a nonprofit—serving free meals every Friday night, or a big investment firm offering mutual funds and IRAs.

Every business also strives to make its products and/or services seem special, even unique, so that they stand out among the crowd and attract more customers than their competitors do. The financial industry has a challenge because their products (mutual funds, retirement packages, hedge funds, etc.) and services (investment advice, portfolio management, stock trading, etc.) don’t easily lend themselves to appearing distinctive, let alone unique. If a product/service is hard to distinguish from that of competitors, the business must wield other strategies to still sell more and earn a profit.

To do this, the financial industry uses a common strategy: price confusion. Price confusion means pretty much what you’d think: difficulty in determining what exactly the price is. Let’s say a case of paper is advertised in an e-mail promotion at the “low” price of $29.99. It sounds like a great deal, until you read the fine print and see that the real cost is $39.99, which you must pay now if you want your case of paper. The $10 difference comes only if you fill out the paperwork to get a special rebate. The business hopes to confuse customers around the price so that they’ll wind up spending $39.99 and not bother submitting the rebate form.

Price confusion also occurs when the price of a product—say, a printer—is truly low, but the costs of necessities to operate that product—ink cartridges—are high. Another example is when airlines get into price wars with one another. A low-priced airfare may initially make you happy—until you pay for expensive add-ons like a checked bag or meals during the flight. Wall Street and other financial industry firms engage in this form of price confusion, too, by charging fees to conduct their business. There are so many layers of fees and so many names for them—managerial, advisory, administration, custodial, commissions—that most people prefer to ignore their statements rather than decipher them. Another strategy is to make the price sound like much less than it is. That can be done by stating it as a small percentage of a much bigger number. If an investment advisor or manager charges you 1% of your investments, it sounds small, but your cost can add up to tens or even hundreds of thousands of dollars in a few years. The real cost, then, of financial products/services becomes confusing because these fees are usually hidden.

If you were the sellers of these investment products, and if you didn’t have to clearly explain each fee, would you make it perfectly clear to your customers how much they’re paying in total—especially when the amount is jaw-dropping when stated in ordinary dollars and cents?

The financial industry doesn’t stop at price confusion, however. Many businesses also engage in product confusion. If they can sell you a bicycle but let you think it’s a luxury limo, they can really jack up the price. Investors often can’t distinguish the price from the product, or even know what the exact “product,” let alone price, is. It’s as though a customer is saying, “I’m paying you something in fees to buy $X in returns.” If the return is negative (a loss), that figure represents another cost, on top of the fees. It’s confusing!

Greg Smith shares an example of a purposely confusing product in his book Why I Left Goldman Sachs: A Wall Street Story:

A Goldman quant [an investment analyst who uses a lot of math] came up with a sexy new black box with a very unsexy name. Call it Clorox.

Clorox was what is known as a “multi-asset-class momentum product,” a fancy term for “Give us your money and we’ll reallocate your funds based on historical models (taking a big markup on every reallocation).” This product was a bit like a jazzed-up version of basic portfolio management. It was like taking a baloney sandwich and offering it to a client as a Panino di Bologna. The first is worth fifty cents; the second, you can sell for eight dollars.[2]

Goldman could sell its “Clorox” product as a unique offering, something to distinguish the firm from its competitors and thereby attract more customers—and make greater profits.

This is why the financial industry makes so much money: not because its employees are brilliant at investing, but because the product—some form of investment (stocks, mutual funds, etc.) or advice—lends itself to spawning price and product confusion. What the financial industry is good at is increasing both product and price confusion by adding more, and more complicated, products, and keeping the total price hidden. They have succeeded in charging you the price of an artisan-crafted lunch for a baloney sandwich. That’s all.

Wall Street and the financial industry have mastered profit generation through price and product confusion to an extraordinary degree. The securities industry—that sector we’re focusing on in this book—had revenues in 2007 in the United States alone of almost $700 billion. Of that, asset management (primarily investment advice and portfolio management) was the largest component, $342 billion in 2007—well over four times the level in 1997.[3] (It has grown further since 2007, but this is the most recent year for which we have reliable data.)

As a percentage of gross domestic product (GDP—the value of all types of products created in the United States), securities industry revenues grew more than tenfold from 1980 to 2007, from 0.24% in 1980 to 2.44% in 2007. According to Harvard researchers Greenwood and Scharfstein, this tenfold growth was due mostly to an increase in fees, specifically.

Clearly, galloping growth in the securities industry, particularly its investment advisory and investment management components (i.e., the topic of this book), has been a main reason that the financial industry as a whole has grown so large over the last 30 years. It is important to remember this fact as we proceed, and to consider the reasons for the growth.

JUSTIFYING THE MEANS—IF NOT THE ENDS

Remember the misconception that opened this chapter: that people in the financial industry know how to make money with money. Even if they did, does it follow that they can help you to make money, too, or that they will? No, it doesn’t. Nevertheless, people working on Wall Street and elsewhere in the financial industry often believe that they’re helping you make money, even when selling you something that is not so special in terms of either a good price or a distinctive product.

There are two reasons for this phenomenon. The first involves the “silo effect.” No single person or group in a large organization or network is wholly responsible for all the stages of a product—the way it is designed, priced, marketed, and actually comes out in practice. Different groups at a company may make these decisions separately, in their own department, or “silo.” To complicate things further, the silos seldom interact or communicate effectively with each other, so one doesn’t know what another is doing. This is the silo effect: One group makes a product they believe will help consumers, another group prices it, but no one typically looks at the larger picture and says, “If we price it this way, we’ve removed the value of the product—it’s no longer doing what it’s supposed to do.” Each decision might be thought to be in the customer’s interest, but nobody assesses the overall result to know for sure whether that winds up being true. It’s as if one team creates what they believe is a low-calorie granola bar; another team adds a lot of nuts, coconut, and chocolate; and no one notices that the bar is no longer doing what it was supposed to do. In the financial industry, there’s a long stretch between that quantitative analyst who concocted “Clorox,” for example, and the financial advisor selling that product—and a longer stretch, still, to positive returns on the investment for the customer. Each of these silos—creation, sales, and end value (returns)—operates independently of one another. It’s easy, therefore, for one group to carry on the self-delusion that they’re helping customers (and easy to absolve oneself of responsibility if things go wrong).

The second reason the financial industry believes they’re helping you is simply that when you do buy their product, they assume—not unreasonably—that you must want it. They’re engaging in a business transaction with you. When you agree to buy, they assume it’s what you want. Like any smart businesspeople interested in making money, they won’t exert themselves to point out these products’ weaknesses or to make it any clearer than they have to how much you’re really paying for them. It’s their job to market the products that will make handsome profits—to design something they can convince you to buy, to figure out how to charge you as much as they can without losing you as a client, and then to convince you to buy it. In practice, they might use innovative means to obscure how much you’re paying (i.e., price confusion).

People working in the financial industry can justify their means and comfort themselves if they need to, by assuming that you are also maximizing your profit in your own way. It’s not up to them to second-guess how you do it. Even if they believe it’s their job to serve their clients’ best interests, they may feel they can only gauge what those interests are by what the clients are willing to buy.

We’re compelled, and happy, to note that many in the financial industry don’t fit the mold we’ve just laid out. Many people are not overly greedy, will tell you the whole truth, and will recommend products and services that they believe are in the client’s best interests, even when it doesn’t maximize their own profits. These providers are to be admired and applauded. This praise applies to mutual fund firms that provide simple, ultra-low-cost investment products; to financial advisors who charge transparently by the hour, day, or task, such as creating a long-term financial plan, or as a very low percentage of investment assets; to financial journalists who have a healthy skepticism of what industry sources tell them and delve to get at the truth; and to academicians who bend over backward to be clear and transparent in their writing, and who either are genuinely mathematically rigorous or eschew mathematics when it’s simply unnecessary.

Because misinformation and deceptions are so endemic in the industry, however, it is difficult for investors to distinguish truths from falsehoods. This can often make it difficult for honest, ethical, and genuinely knowledgeable professionals to get through. We hope that the publication of this book will make it easier for those true professionals—the better elements in the financial industry—to get their message across.

In the final analysis, it’s not the job of people, good or bad, working in the financial industry to make you money. It’s your job. We hope that the Simplify Wall Street portfolio that results from following our 3 Simple Rules of Investing will help you do that job successfully.

THE SIMPLIFY WALL STREET PORTFOLIO

Simplicity is the ultimate sophistication.

—LEONARDO DA VINCI

Many investors, as we’ve noted, are perplexed by the array of possible investment vehicles and strategies, not to mention their costs. They would be justified to lower the number of alternatives merely to reduce the price and product confusion and make it easier to make a decision, even if in the process they missed some slightly better results. But in our case, the best alternative is as plain as the nose on your face. It’s the least costly, the easiest to implement, and the least risky for the benefit an investor can expect to get from it.

With this in mind, we’ve designed the Simplify Wall Street portfolio with just these five common investment products at its core:

Government inflation-protected securities (in the United States, these are Treasury Inflation-Protected Securities, or TIPS)

A low-cost total U.S. domestic equity (stock) index fund, either a mutual fund or an exchange-traded fund (ETF—i.e., a sort of mutual fund that can be traded like stocks on an exchange)

A low-cost total international equity index fund, either a mutual fund or an ETF

Single-premium income annuities

Low-cost term life insurance

In Rule #1, “Simplify Your Options,” we’ll delve into these items in greater detail and add a few more alternatives, too, but still the whole portfolio contains only 10 possible investment products.

A Portfolio for Everyone

Besides its modest number of investment vehicles and the easy-to-understand advantages of each, the Simplify Wall Street portfolio is also appealing because it works for all types of investors, no matter what their experience, knowledge base, or financial circumstances. We divide investors into four general categories:

1. Superrich individuals with multigenerational wealth and institutional investors (investors who are managing huge assets that represent, e.g., a corporation’s or state government’s retirement fund for its employees or an endowment at a university).

2. Reasonably well-off people

3. People who are getting by

4. Struggling individuals (the working poor)

The main difference between category 1—the multigenerational or institutional investor—and categories 2-4 is that the latter categories have a life cycle. That is, their investments will accumulate for a period and then wind down as the individuals doing the investing grow older and start making withdrawals. By contrast, institutional funds—such as pension, endowment, and foundation funds—usually don’t have an end point—there is no life cycle. Similarly, ultrawealthy people and families usually don’t need a spend-down plan, either. They need cash flow, as institutional investors do, but they also need estate planning—specific counsel on how to preserve and pass on their wealth to future generations. Most people assume that category 1 investors, the superrich and institutional investors, are different from ordinary investors, but they can be seduced by the 7 Deadly Temptations and fall prey to investment frauds as much as everybody else. Many investors in this category were fooled by Bernie Madoff’s fraudulent investing schemes, after all, and by the subprime mortgage securities that collapsed in the financial crisis in 2008. The frauds may have a more polished appearance or sales pitch and better connections, but studies show that these investors waste many billions of dollars paying for worthless investment advice and management.[4]

Reasonably well-off investors, our second category, aren’t rich. They must set aside money to invest in order to make withdrawals when they need the money later, to pay for their children’s college expenses and to finance their retirement. They therefore want to get the most out of their investments but also create a comfortable and secure safety net, in case investments go very badly.

Many individuals are in the third “getting by” category. These investors can barely invest enough to fund their retirement. We’ll have good news and bad news for them. The good news is that they do have a good chance of building a retirement account. The bad news is that they won’t also be able to afford a secure safety net. So, in the unlikely but possible event that their investments don’t succeed, getting-by investors will be either living well below their expectations or working longer than they had hoped.

Struggling investors are faring even worse than those in category 3: they’re hardly getting by at all and can’t afford to invest. Unfortunately, this category includes the vast majority of people on Earth, including most people in the United States. Struggling investors are living paycheck to paycheck. This vulnerable group is frequently preyed upon by predators like payday lenders who take advantage of their instability. They will have to work and save as much as they can, though many will have no opportunity at all to save or invest. For those few who can save some money, there are (in the United States) federal, state, and local programs that offer very generous incentives to jump-start their savings (see the box).

THE TERMINOLOGY AND CONTEXT FOR OUR PORTFOLIO

Before we move on to the 3 Simple Rules, let’s define a few words and numbers, and offer some historical context, so that you understand the Simplify Wall Street portfolio even better.

Rates of Return on Investment

How much your investments gain or lose is measured as a “rate of return on investment,” or simply “return.” This figure is the percentage by which your investments grow every year, on average. For example, suppose you invest $100. A year later you have $105. Your rate of return was 5%.

INCENTIVES FOR LOW-INCOME SAVING IN THE UNITED STATES

Saver’s Credit provides a federal income tax credit of up to 50% for investing.[5] Some Individual Development Accounts (IDAs) provide as much as a 4-to-1 match.[6] Up to $1,000 in savings will be matched with $4,000 if low-income individuals are able to save.

“Nominal” dollars means to economists the dollars you see in your billfold. These dollars don’t change over time. “Real” dollars means something different to them—it means what you can buy with those dollars. That changes because of inflation. Similarly, the rate of return can be a “real” rate or a “nominal” rate. A nominal rate is the percentage rate of growth in nominal dollars. If you invest $100 and have $105 a year later, your nominal return was 5%. The real rate is the rate of growth in purchasing power—in other words, the nominal rate adjusted for inflation. For example, let’s say inflation will be 2% for the next 23 years. If your nominal rate of return is 5%, then your $1 will grow to $3 in 23 years. But if you think you can buy three burgers with that $3 in 23 years that are now priced at $1 each, think again. For the real, inflation-adjusted rate of return, we’ll need to deduct that 2% for inflation from your 5% nominal return, so the real rate of return is 3%. So, your dollar will grow to a purchasing power of only $2, and you’ll be able to eat only two burgers.

Let’s now look at what the returns are likely to be in the future. Table 1a summarizes the expectations for long-term bonds and inflation, and Table 1b summarizes the expectations for equities (equities means stocks).

TABLE 1A U.S. Treasury and Corporate Bond Expected Returns and Expected Inflation September 2013

TABLE 1B Global Equities Expected long-Term Annual Returns and high and LOW Scenarios

We’ll explain how we got these numbers, but first we have to ask you to do something no one likes to do: reel in your expectations.

What History Tells Us

An endless number of investment gurus peddle unrealistic claims and aspirations. Unfortunately, these statements are not true—except in the same sense that someone actually does win the lottery. But people would like them to be true. It’s one of the reasons the financial industry serves up so many shaded truths and near-lies: because they can raise clients’ hopes and spirits.

A challenge today is that for almost two decades—in the 1980s and 1990s—the real return on investment in stocks was unusually high. It averaged more than 16% a year from August 1982 until the end of 1999.[7] In the last five years of the 1990s, it averaged 25.5%. This figure is still vivid in the memories of people who will soon retire or are already retired. At that time, they came to expect such high returns.

These very high rates of return were in part a correction for the poor returns over the prior 10-year period. In 1982, at the end of that dismal time frame, the consensus outlook was bleak. Stocks’ prices were so low that they implied that future corporate earnings growth rates would be negative. But this expectation turned out to be completely wrong. Partly due to correcting that very negative expectation, the price of stocks subsequently soared in the 1980s and 1990s. These high expectations nourished an overcorrection of the negative view of 1982—a bubble. (This overcorrection was partly compensated for by the poor returns of the first decade of the 2000s.) But this booming stock market led the generation who lived through it around 20 years ago to think that 15% to 25% real annual rates of return on stocks were to be expected. Many of them still think that, even though it’s wrong.

The best estimates of future expected rates of return come from information that is specified exactly by contract. Such precisely specified contracts, unfortunately, apply only to fixed-income investments, such as bonds. For example, the contract could say that the price of the bond is $1,000, and it will pay $40 a year in interest until maturity, when the $1,000 principal will be returned. Hence, the expected rate of return is 4%. (This number is also called the bond’s “yield to maturity,” or simply its “yield.”)

Expected Rates of Return on Bonds

The issuer of a bond—usually a corporation or government—guarantees you a specified stream of payments every six months (the bond’s interest or “coupon”) until the bond matures. When the bond matures, you’ll get approximately what you originally paid for it back—the principal (unless, of course, the bond issuer defaults—that is, can’t make the payments). The bond’s yield to maturity is the return you get from now until it matures, if all the payments are made. We know what that rate is, because it can be calculated with certainty.

Let’s start with the safest, least risky investment you can possibly make, U.S. Treasury Inflation-Protected Securities—TIPS. (We’ll be saying much more about these later in the book.) At the time of this writing, 30-year TIPS (TIPS maturing in 30 years) yielded about 1.5%.[8] This is a real—that is, inflation-adjusted—rate of return because the TIPS’ principal value increases annually for inflation.

The nominal Treasury rate of return is the yield on a non-inflation-protected U.S. Treasury bond. At the time of this writing, the yield on a 30-year Treasury bond is about 3.7%. This is a nominal return because the bond’s principal does not increase with inflation.

We can infer from the 3.7% nominal return on Treasury bonds and the 1.5% real return on TIPS that their difference, 2.2%, is what the issuers of the bonds and the investors who buy them expect inflation to be over the next 30 years. Of course, this doesn’t mean that’s what inflation will be; it’s just their best guess right now.

Both TIPS and U.S. Treasury bonds are guaranteed by the U.S. government. That makes them extremely low-risk. Indeed, financial academics refer to them as “risk-free.” With a little more risk, you could invest in a diversified basket of U.S. domestic corporate bonds. At the time of this writing, the yields on long-term corporate bonds average about 4.9%.[9] Hence, the “risk premium” for investing in riskier corporate bonds instead of U.S. government bonds is about 1.2% (the corporate bond yield of 4.9% less the Treasury bond rate of 3.7%).

And remember, those yields of 3.7% for 30-year Treasury bonds and 4.9% for long-term corporate bonds are nominal returns. If we assume 2.2% inflation, then the real rate of return on Treasury bonds is expected to be 1.5%, just like TIPS. The real return on corporate bonds is expected to be 2.7%. These numbers round off the entries in Table 1a.

Expected Rates of Return on Stocks

Stocks (also called equities) are more risky than bonds for two reasons. First, there’s no contract with stocks, as there is with bonds, saying that the investor will receive any specific series of payments. Second, if the institution that issued the bond goes bankrupt, it must pay bondholders before stock owners get anything.

Because stocks are riskier than bonds, an investor will require a higher expected return to invest in stocks. The difference between the expected return on stocks and the expected return on bonds is often called the “equity risk premium.” The amount of the equity risk premium can be estimated (a better term might be “guessed at”) in various ways.[10] The historical average has been about 4% to 4.5%.

The return on stocks varies quite a lot from year to year, as anyone who has invested in them knows. However, their long-term average annual returns do not vary as much when measured over long time periods such as 30 or 35 years. As we’ll see, this is the time period for which an individual’s average retirement dollar is typically invested.

Three professors from the London Business School studied the past performance of stocks in 19 developed countries.[11] The countries included most of the major nations of Western Europe and Northern Europe (i.e., Scandinavia) plus the United States, Canada, Australia, Japan, New Zealand, and South Africa. The results of the study showed that from 1900 to 2011, stocks of these 19 countries offered average real returns from 2% to 7% a year—Italy lowest with about 2%; Australia highest with about 7%; Germany and France about 3%; Switzerland and Norway about 4%; the United Kingdom and Finland about 5%; and the United States, Sweden, Canada, and New Zealand about 6%.[12]

These are the kinds of real returns that are realistic—moderate single-digit returns, not whopping double-digit ones. Occasionally higher or lower returns occur for a while, sometimes for a long time, but they’re not what you should expect. If you’re lucky and you’re invested in stocks, you’ll get them. But you can’t expect them, and you can’t plan on them.

The future is never predictable, but one must sometimes use numbers for planning. For those purposes, the real rates of return from 1900 to 2011 that we just cited are the best guide we have. Those real rates ranged from 2% to 7%, with a country-weighted average—a global stock return—of 5.0%.[13] (The nominal country-weighted average return over the same years was 8.3%.) For our discussion in later chapters of this book, we’ll use this long-term historical global average of 5.0% as our expected real rate of return on stocks. We won’t, however, use 8.3%—the historical rate—as the expected nominal return. In addition, we inferred earlier that investors anticipate 2.2% inflation in the next 30 years. Hence, our assumed nominal return on equities will be 7.2%—the real return of 5.0% plus 2.2% inflation.

But those are all only the expected returns, the ones we deem most likely. A whole range of other returns are possible. We’ll want to estimate how much worse or better it could get. If we consider single years, it can get much, much better—or much, much worse. The highest single-year real returns on stocks in the United States were more than +53%, which occurred in 1933 and 1954. The worst were losses of nearly 37.5%, which occurred in 1931, 1937, and 2008.

But if we look at a long time period, the variation is far less. The worst three-decade real return was +3.4%, and the worst nominal return was +4.8%. Those were in the early part of the twentieth century, from 1910 to 1940. After 1925, the worst real return was 4.5%, and the worst nominal return was 7.7%. The best 30-year returns were quite good: the best real return was 9.9%, and the best nominal return was 13.2%.

We’ll use these best and worst 30-year returns (see Table 1b) as our guesstimates of the best and the worst that could happen over 30 years. It is true that there is no guarantee whatsoever that worse (or better) could not happen, even a negative global return. As investment writer William Bernstein has said, “A long-term negative global stock return requires not merely universal inflation, but universal Armageddon.” In that event, no investment would be safe.

ONE SMALL STEP FOR THE INVESTOR, ONE GIANT LEAP FOR SOCIETY?

Given that so many on Wall Street and in the mainstream securities industry earn their gargantuan incomes thanks to the fact that you pay much more than you realize for a product that you don’t understand, how will they react when we show you how to pay 1/20 or 1/40 as much for something just as good or better—and perhaps not even to them? Well, we’d expect them to fight this advice tooth and nail. They’ll create every argument they can think up why we’re wrong. Their standard rebuttals to sales objections already try to dispute a few of our claims, and they’ll intensify their counterarguments. They have become comfortable because their customers (or their “muppets,” as Greg Smith says some of his Goldman Sachs colleagues called their clients) so far have shown few signs of truly realizing that they’ve been paying a lot for really very little.

The damage is greater than that, however. Investment strategies and vehicles created in the belief that they will spread out risk and increase returns actually create more risk not just for the individual investor but for the entire financial system. If hundreds of thousands of people around the world purchase shaky investment products that eventually blow up, the financial systems in those investors’ countries can collapse in the explosion. The dramatic economic downturn of 2007-2009 in the United States, for example, was in part due to investment products that were nothing more than bundles of poorly qualified (so-called subprime) mortgages, repackaged and sold as promising investments.

The sorts of investment products we’ve described from Wall Street and the financial industry can also concentrate power and increase economic inequality. A minuscule percentage of the population is getting both phenomenally rich and unduly powerful, in large part because the rest of the population has been seduced into paying extraordinary fees for something that they believe they need—investment products, advice, and management—but that is, in its higher-priced forms, nearly worthless. Funds thus continue to flow to ultrawealthy captains of finance from far less wealthy investors by means of opaque, complex-sounding, well-marketed, and confusingly priced investment products and services. This cycle does nothing but exacerbate economic imbalances, intensifying economic hardship for those who pay, while entrenching a financial elite that is not only too powerful but increasingly removed from and uncomprehending of the lives of the rest of society.

PART I

The 3 Simple Rules of Investing

Everything should be made as simple as possible, but not simpler.

—ALBERT EINSTEIN

TAKING PROFESSOR EINSTEIN’S recommendation, our goal is to make things as simple as possible, but not simpler. You’ll be surprised how simple they can and should be made. In the investment world, however, things are often made as complex as possible. Most investment advice, products, and services offered by investment companies and financial advisors, not to mention academic researchers, are much more complicated than they need to be.

Complexity comes with a cost. If an investment strategy will enable you to save $100 on your taxes or earn $50 more in interest, but it will cost you $3,000 to pay someone to help you do it, then there’s not much point in doing it. Complexity is costing you much more than that, though, and usually you gain nothing.

We’ll cut that kind of complexity to the bone. When there is a strategy that will save or earn you money, we’ll tell you about it. But if it will cost you more to do it than you will save or earn, we’ll tell you that, too. We’re mostly going to describe simplifications that earn or save you more, and cost you less. That may sound like what economists call a free lunch—too good to be true. But free lunches are possible even in the investment field, and we’ve done the work to show you where to find them.

To enjoy your free lunch, just follow the three simple rules discussed here in Part I. The first two rules are about what to invest in and how to invest; the third rule is about what to screen out (and Part II will tell you why). Rule #1 describes what to invest in by encouraging you to radically reduce the number of investment products you use. Rule #2, the central nugget of the book, reveals the investment strategy that is not only the simplest but the best to help you meet your financial goals. You’ll learn how to do this even with only two investment classes. Short and to the point, Rule #3 will tell you to ignore the noise—all the counterclaims and arguments against our recommendations that you typically hear from Wall Street and the financial industry. Don’t let that noise dissuade you from following Rules #1 and #2.

RULE #1

Simplify Your Options

You may not believe this: Whether you’re a small investor with a few thousand dollars to invest, or a wealthy investor with a few million dollars, or a gigantic pension fund with a hundred billion dollars, you need only consider at most about 10 investment products. The rest are of no use and aren’t worth thinking about.

If you were going to buy a computer, how many brands do you need to choose from? About 10? And the same thing for a smartphone—maybe 10 models, at most? This point applies whether you’re a teenager doing homework or a top executive at a Fortune 500 firm.

Shopping for investments is a little different. You’re faced with tens of thousands of investment vehicles to choose from, offered by thousands of investment firms. There are more than 80,000 mutual funds and ETFs (exchange-traded funds) worldwide. There are almost 10,000 hedge funds. The array is mind-boggling. And it keeps expensive financial advisors busy trying to guide confused investors through the mess. They let you know it’s difficult to choose because there are so many choices.

But, actually, there aren’t. If there were 100,000 computers or smartphones to choose from and 99.9% of them cost 5 to 80 times as much as the other 0.1% but weren’t any better, how many would there really be to choose from?

Why aren’t there 100,000 computers or smartphones for the consumer to choose from but there are 100,000 investment vehicles? Because of product and price confusion—a topic we covered in the introduction. Consumers don’t realize that 99.9% of investments cost so much more than the others. Neither do they realize that those few that cost so much less are just as good as or even better than the rest. They have been so confused by the product that they think they’re buying a luxury convertible when they’re only buying a bicycle—and paying 50 times too much for it. They’re so confused that they don’t even know what, exactly, they’re paying.

Hiring a financial advisor to guide you through the mess of investment products is usually no help. Most financial advisors will only reinforce and even add to the confusion because it is what they were taught, it is the industry status quo, and most of them—perhaps too uncritically—believe it. They believe they’re trying to help their clients while also making a living, and they have no incentive to depart from the industry line.

THE ONLY INVESTMENT VEHICLES YOU’LL EVER NEED

In this book, we’re talking about “passive” or indirect investing—in other words, investing in businesses in which you’re not an owner or employee. We’re not trying to tell you how to actively and directly invest in or run your own business selling, say, hamburgers. (Note, too, that a completely different meaning of “passive” applies to an investment vehicle that invests in all the securities in a market index instead of trying to pick certain ones; an index fund, then, is passive in this way.) If you’ve got savings in a 401(k), an IRA, or taxable individual or joint account, or investments you oversee by being the administrator of a university endowment fund, a pension fund, a foundation fund, or a wealthy family’s office—we’re talking to you. You’ll want to invest these funds very wisely. We’ll name the investment vehicles that you should consider—forget all the others. Of course, you’ll need information about how to use them, but reducing the number of choices will go a long way to reduce the complexity, too, so that the rest will also be easier.

You need only consider a very few types of investments, or what’s known as asset classes: stocks (also called equities), fixed income (bonds and short-term investments like bank accounts, certificates of deposit [CDs], etc.), perhaps real estate, and simple forms of insurance that ensure meeting cash flow needs. Specifically, here’s all you need:

Government inflation-protected securities (in the United States, these are Treasury Inflation-Protected Securities, or TIPS)

A low-cost total U.S. domestic equity index fund, either a mutual fund or an ETF (this would be part of a world stock fund)

A low-cost total international equity index fund, either a mutual fund or an ETF (the other part of the same world stock fund—with one fund, then, you’re investing in both U.S. and international stocks)

Single-premium income annuities

Low-cost term life insurance

That’s it. If you want to, you could also add these:

A low-cost total U.S. domestic bond market index mutual fund or ETF

A low-cost total international bond market index mutual fund or ETF

A low-cost REIT (real estate investment trust) index mutual fund or ETF (U.S. and global)

Individual stocks or bonds you buy yourself through a low-cost broker and keep for many years

Small amounts from a very short list of other investments that we’ll discuss later

That’s 10 investment vehicles, max—all you’ll ever need.

In the case of the stock, bond, and REIT index funds, competing versions are offered by a small number of providers for fees of less than a quarter of a percent annually, and often much less. You should confine yourself to only those. Examples appear on the 3 Rules of Investing website, with regularly updated information.

Now we will describe each investment vehicle in more detail.

Government Inflation-Protected Securities (GIPS)

GIPS are the least risky investment on the planet. No other investment you can make or action you can take is closer to 100% certain to preserve the purchasing power of your money for decades to come. That includes stuffing your money under the mattress, buying gold—anything.

The best-known GIPS is the U.S. Treasury inflation-protected securities (TIPS). These can be purchased easily online at the TreasuryDirect website[1] of the U.S. Department of the Treasury. Several other national governments also issue inflation-protected securities. The United States is the biggest issuer, however, so to keep things simple we’ll talk mainly about TIPS.

TIPS protect against inflation by increasing the face value by the inflation rate each year. Let’s start with an example. Suppose you buy a TIPS contract for its face value of $1,000, and the interest rate is 1%. Hence, the first year the interest is 1% of $1,000, or $10.

But each year the Treasury increases the TIPS’ face value for inflation. Let’s suppose that in the first year after you bought it, inflation is 2%. That means the face value becomes $1,020, so next year you will get interest of 1% on $1,020, or $10.20. And so on, for the length of the TIPS contract. As this example shows, as the TIPS contract matures, the government continues to pay you interest on the inflated face value. The final payment, then, is likely to be much more than what you paid for the TIPS in the first place, to keep up with inflation.

Another nice thing about TIPS is that they also protect you against deflation. If inflation was actually negative from the time you bought the TIPS until maturity, then you’ll still get your $1,000 back at maturity, not its deflated value.

Is There Really No Risk with TIPS?

Well, no. There’s never no risk. But you can’t get a smaller risk with a long-term investment than with TIPS. There just isn’t one.

There are two risks with TIPS. One is that the government will default on its obligation. That’s a negligible risk, however, if it’s the government of a major developed country. The second risk is a little greater: that the government’s inflation rate may not be your inflation rate.[2] The government calculates inflation based on the “average” basket of goods consumed by the average person in the country. But your consumption might be of a different basket of goods. Your goods might increase in price faster than the average—then you could be in trouble. (Of course, their prices could also increase slower than the average, and then you’d be fine.) For example, the price or cost of health care and nursing homes has increased much faster than average prices. And guess what? As you grow older, that’s what you’ll be spending a lot of money on, like it or not. So if the price of health care increases faster than the rate of general inflation, TIPS payments will lag behind.

Be that as it may, the main use of TIPS is to protect against huge fluctuations in general inflation, which really could occur; they occurred in the United States as recently as the 1970s and early 1980s and more recently in some other countries. So if you’re depending on payments from investments over the next 30 to 60 years, it’s a good idea to protect against those fluctuations if you can.

Here’s one more point to keep in mind: TIPS’ longest maturity is 30 years. You can guarantee yourself an income until 30 years out, but what about after that? If you’re retired at age 60, there’s a decent chance you’ll still need to collect an income after age 90. For that you need an annuity—the simplest kind available. It’s on our list of core investment vehicles, and we’ll discuss it soon.

Global Diversification in Global Inflation-Protected Securities (GIPS)

In addition to the United States, a number of other countries issue inflation-protected securities, notably the United Kingdom’s Index-linked Gilts and France’s OATi. To hedge against sovereign risk—that is, the very small risk that a major developed country cannot or will not honor its guarantee—an investor could invest in a diversified portfolio of GIPS maturing on the desired dates. Doing so would take more effort than investing only in the inflation-protected securities of one’s own government; but it would add a small measure of diversification, which will reduce risk.

World Stock Index Funds

The second investment is different. Global stocks—that is, stocks that are listed on stock exchanges in the United States, Europe, Hong Kong, Tokyo, Australia, Brazil, and many other places in the world—are the riskiest major category of investments. When you invest in stocks, there’s no contract to say you’ll ever receive anything back.

Because global stocks are not as certain as GIPS to pay your money back, they have to offer you more. As we noted earlier, at this writing, U.S. TIPS offered a return of only about 1.5% above inflation over the next 30 years.[3] Global stocks, however, are likely to provide a higher return, if you wait long enough. As we noted in the introduction, studies suggest that if you hold a diversified stock portfolio for 30 years or more, history suggests you’re likely to get a good return (i.e., about a 5% return, above inflation, on average, give or take perhaps 3%). That likelihood is as high as possible if the portfolio is globally diversified. But it’s not guaranteed, not at all. GIPS, in contrast, are guaranteed by the government that issued them.

Fortunately, it’s easy and very inexpensive to invest in a globally diversified stock portfolio. The simplest way is to buy a world stock index ETF. “Index” in this context simply means a single investment vessel that holds a lot of different shares in various stocks—in fact, hundreds or thousands of them. You’re in effect investing in multiple firms at once, rather than investing in Firm A, Firm B, and so forth. A few world stock index ETFs are similar—from companies like Vanguard, BlackRock iShares, State Street SPDRs, and Schwab. It’s easiest to buy one that simply holds the whole global stock market (e.g., available at the time of this book’s printing, and in the United States, from Vanguard,[4] iShares,[5] and SPDR[6]). They can be purchased easily online after opening an online brokerage account with, for example, Schwab, E*TRADE, or TD Ameritrade. These funds have low “expense ratios,” the fees their managements charge per year. For example, two of the lowest-cost world stock ETFs currently have expense ratios of 0.19%[7] and 0.24%,[8] respectively. Hence, for every $10,000 of the assets you’ve invested, you’ll pay either $19 or $24 in fees that year, depending on which you choose. These two world stock funds are divided in approximately the same way the stocks of the world are divided. One of them reports that it holds 45% in U.S. stocks, 45% in international developed-market stocks, and 10% in stocks of emerging-market countries like Indonesia, Turkey, and China. The other is similar.

World stock funds can be purchased as a single fund or as two funds. You can buy them as one low-cost fund that holds nearly all global stocks or an approximation thereto. This is the way we’ve presented this investment vehicle in our earlier list of the only investments you really need. You may get a better expense ratio, however, by buying two funds, one a U.S. domestic total market fund, the second an international fund comprising the rest of the world stock fund (all countries except the United States). For example, one U.S. domestic total stock market ETF currently has an expense ratio of only 0.05%, one-twentieth of a percent. The same provider’s total international stock index ETF has an expense ratio of 0.16%. If you combine these two funds in the same ratio as in the world stock fund (45% U.S., 55% international), your resulting expense ratio will be only 0.11%, as compared to the 0.19% expense ratio for the lowest-cost world stock ETF. (The low cost of the U.S. total market index fund may be due to the large size of its total assets, about 10 times that of the world stock ETF.) More information is available at the 3 Rules of Investing website.

ETF Versus Mutual Fund

You can buy an index fund as a mutual fund or as an ETF. It’s so messy to explain the differences between how an ETF and a mutual fund are organized that we think we’ll forgo the full explanation and just get to the implications. As we’ve mentioned, both of them are simply ways to buy a basket of stocks or bonds all in one single purchase. With a mutual fund, you can only buy into it once a day. An ETF, on the other hand, trades at any time in the market, just like a stock.

ETFs often have lower expense ratios—the fees you pay every year—than mutual funds. They can also have certain small tax advantages. So if there’s a choice between an index ETF and an index mutual fund, it’s usually better to buy the ETF.

The only time it’s not better to buy the ETF is if you’re going to dribble your purchases out over many small purchases over a period of time. When you buy shares in a mutual fund—as long as it’s a no-load fund, the only kind you should buy—you don’t have to pay for purchasing. When you buy shares of an ETF, however, you may have to pay brokerage fees (again, just as you do whenever you buy and sell a stock). The brokerage fees are generally less per share if you buy in a big lump than if you string out your purchases and buy in many small lumps. In fact, if you buy too many small lumps, you could overwhelm the ETF’s advantages because of all those fees. In that case, you’d be better off going with the mutual fund.[9]

What About Individual Stocks?

Buying a world stock index fund (or the combination of one domestic index fund and one international index fund) gives you the best-diversified equity portfolio you can get, and at the lowest fee. This is one-stop shopping at its best. Each of the two world stock ETFs we mentioned holds more than 5,000 different stocks and charges less than a quarter of a percent per year. The average mutual fund, by contrast, holds far fewer stocks and charges 1.4%—more than five times as much. The average mutual fund actually charges 28 times as much as the lowest-cost U.S. domestic total stock market index fund.

And that’s only the average fund. About half of them charge more than that. But here’s another point: the actual cost of the average mutual fund is probably much more than 28 times the cost of the lowest-cost total market index fund. The average equity mutual fund buys and sells stocks so much that its turnover (the ratio of stocks bought and sold to those held) can be nearly 100% a year or more. In other words, the basket of stocks in the fund gets totally transformed by the end of the year. The brokerage costs for all that buying and selling, which are taken out of the fund—but aren’t part of the quoted expense ratio—could add as much as another half-percent to the cost. So, diversified low-cost index funds are a good choice.

If you wanted to lower your costs even more, you could create your own basket of stocks by buying each one individually and holding onto them for many years. When you do this, there would be no cost to hold them after you bought them (i.e., no expense ratio), only the initial brokerage cost when you bought them (and, eventually, the brokerage cost to sell when you wanted to cash the stocks in much later).

However, it’s much harder to be well diversified if you buy your own stocks. Diversification among many investments cuts down on the risk of losing a lot of money in one or a few of them. If you take the do-it-yourself approach in building an “index” of stocks, you might wind up not buying enough different stocks to be truly well diversified. You might wind up buying a lot that were in the same industry, for example, or in industries whose securities prices tended to move together, like oil and gasoline.

What About Individual Bonds?

As 1 of the 10 possible investments you could consider, we mentioned bond index funds among the possible “extras” in your portfolio. These could consist of corporate bonds or government bonds or both. We do briefly discuss bonds later in this chapter; and as we’ll see in Rule #2, you don’t really need them for most purposes. Occasionally, they could add a small measure of diversification—and therefore lower risk—and possibly a small expected return, especially for corporate bonds (but with a little added risk—the risk of corporate defaults).

You could buy individual bonds, but you should buy enough different individual bonds to be well diversified, which may be even more difficult to do yourself than it is with stocks. The corporate bonds available for individuals to purchase are concentrated in only a few industries, making diversification challenging. If you buy a bond mutual fund or ETF instead, usually the fund “rolls over” the bonds—that is, it buys some and perhaps sells some—in such a way as to keep their average maturity approximately constant. If it’s an intermediate bond fund, for example, with an average maturity of, say, six years, then when some bonds mature, others will be bought to maintain the average maturity.

If you could buy your own bonds instead, you could arrange them so they produce your desired cash flows. You might organize them so they mature on different dates just as you need the funds, and keep them until they mature. That way, you only need to buy them once. This approach is called “laddering” the bonds.

Insurance Products: Income Annuities and Life Insurance

The last two products on our short list of investment vehicles are single-premium income annuities and low-cost term life insurance. We will be considering only “simple annuities,” which means that you pay a lump sum for them, and they provide you a stream of equal periodic payments, typically monthly, until death. The simple annuities available to you to buy could be inflation-adjusted or not—that is, the equal periodic payments could be in “nominal” dollars, for example $1,000 a month, or in “real,” that is, inflation-adjusted, dollars. We’ll also talk a little about life insurance in this section. We won’t talk about health insurance or any other kind of insurance protection.

Why only these kinds of insurance—simple annuities and term life insurance? Because they’re useful for the planning of lifetime cash flows; in particular, they insure you against early or late death, and the cash flows that each require. In addition, they’re simple enough that they’re not easy targets for product confusion, one of the two main sources of overcharging. Their simplicity also means that it’s more difficult for sellers to create the other source of overcharging, price confusion, because they have to compete with each other to sell the same basic, transparent product.

You should keep an astronomical distance between yourself and annuities with names like “variable annuity” and “equity-linked annuity.” These are among the most egregious examples of products that charge incredibly high fees because people don’t understand them.

Insurance companies truly do provide a service when they offer easy-to-understand simple annuities because they can diversify the longevity risk (the risk of living long and thus drawing a large cash flow) as no individual can. And the insurance companies themselves can benefit from selling that valuable service even with fully transparent pricing, so they have less need, as well as less ability, to create price and product confusion.

A simple annuity can be a single-premium immediate annuity (SPIA) or a deferred-income annuity (DIA). With an SPIA, the purchaser pays a lump sum at, let’s say, age 80. Then the insurer guarantees the purchaser a fixed payment (usually paid monthly and perhaps inflation adjusted) starting immediately, until the purchaser’s death.

Maybe even more useful, DIAs make payments to the annuitant (purchaser) not immediately but at some date later than the date when the annuitant pays the premium. For example, you could pay a lump sum at age 65 and start collecting at age 80. Obviously the premium for this sort of arrangement will be much less than if payments start immediately.

Annuities can help individuals guarantee their lifetime cash flow needs toward the ends of their lives, in a way that no other investment vehicle can do. If they wish to guarantee that they’ll be able to leave a bequest, term life insurance can make that guarantee. If an investor’s cash flow goals are clearly specified, then it’s possible to plan, using a few calculations, the best mix and timing of TIPS, annuities, and life insurance toward the end of the investor’s life, since the only uncertainty is the date of death and not the amount or timing of the payments. The calculations are beyond the scope of this text, but the reader may explore them at the 3 Rules of Investing website.

The Risk Factor

Insurance companies can diversify the risk of any single annuity by selling many annuities. Some of their annuitants will die early and some will die late, so the insurance company’s net total payouts are fairly predictable, if actuarial life expectancies remain roughly the same.

When you purchase an annuity or life insurance, the insurance company is guaranteeing that you’ll receive payments decades in the future. What’s to guarantee that the insurance company will be around that long and that it will have the money to pay you?

This is an important question. After all, in the mid-2000s, the London-based Financial Products division of AIG, a huge insurance company, sold so-called credit default swaps (CDSs) that were supposed to insure against a fall in the market values of certain securities called CDOs (collateralized debt obligations). But the Financial Products division didn’t have anywhere near enough money to pay off the buyers of this insurance. This happened not decades after people had bought CDSs but rather within a year or two after the Financial Products division had promised to pay off if the CDOs fell. Even the whole of AIG didn’t have enough money because the payoffs were so big; and, anyway, AIG itself was not liable for the promises of its Financial Products division. In the end, the purchasers of the insurance got paid off in full because the U.S. government stepped in and made good on the payoffs that had been promised by AIG’s insurance contracts.

But what if the government didn’t step in? In fact, many people believe it was a mistake for the U.S. government to pay off AIG’s promises in full. The next time something like this happens (we hope it won’t, but it very well might), the government is less likely to pay in full. How can we be sure that the insured payments on an annuity and on a life insurance contract, due decades in the future, can be reliably counted on?

Well, we can’t, but the same goes for any promised payments, even those promised by the U.S. government. Every investment has a risk. The question is, how big is that risk?

In the case of annuities and life insurance, the risk can be assessed and minimized. Annuities are offered by large well-known insurance companies like Prudential, Metropolitan Life, Principal, and Mutual of Omaha. That doesn’t guarantee they’ll be able to pay—just look at AIG, an even bigger insurance company, and its CDSs—but it helps. Also, these insurance companies are rated by the same ratings agencies that rated CDOs: Moody’s, S&P, and Fitch, and also by A. M. Best, which rates only insurers. Granted, faith in ratings was severely undermined by their role in the financial crisis, but it helps to check the ratings of your insurers anyway.

Joe Tomlinson, an actuary and financial planner, looked into the question of the safety of annuities in an article in Advisor Perspectives.[10] He found that the payment of annuities is backed up by U.S. state Life and Health Guaranty Associations, as well as a nationwide coordinating body for guaranty associations, the National Organization of Life and Health Guaranty Associations (NOLHGA).[11] States cap the amount of the guarantee, with $250,000 of the premium being a common cap. Tomlinson concludes, “History shows that annuities have traditionally been an extremely safe investment. Insurance company insolvencies have been few, companies in trouble have often sold business to healthy insurers, and guaranty associations have provided an additional safety net.”

In short, it seems reasonably safe to expect that an annuity will pay in full—about as safe as any investment short of United States Treasury bills—but it’s not absolutely guaranteed. The best thing to do is to divide up the annuity amounts so that each premium is less than the $250,000 (or sometimes $500,000) that your state of residency caps the guarantee.

What about life insurance? Suppose you’re 75 years old and you want to leave $250,000 to a grandchild whenever you die. You can’t be sure you can do it because you might run out of money first, if you live long. You could do it, though, with level term life insurance. With that kind of insurance, you pay the same amount every year, and the contract—you could make it for $250,000—pays off when you die. That way it just becomes another predictable cash outlay. For more information on calculating a mix of TIPS, annuities, and life insurance, see the 3 Rules of Investing website.

Other Possible Investment Vehicles

You don’t really need anything else besides what we’ve discussed so far: GIPS, global stocks, annuities, and term life insurance. (The latter two are, of course, only for individuals, not for institutional investors who are managing funds like pensions and endowments.) GIPS and global stocks cover almost the whole range of risk and return. That means you can select how much risk you want to take by combining those vehicles. Also, by combining GIPS and world stocks, you diversify your portfolio further, in a way, by splitting your risk between the risk of global governments failing and the risk of global corporations failing.

However, if there is little or no cost for adding them, a few other investment vehicles may occasionally increase diversification somewhat and reduce risk a little. You could also increase risk, if desired, possibly adding a little more expected return but also a greater chance of loss. What’s most important is that you shop for investment vehicles that carry a very low fee and then use them judiciously. Let’s explore a few examples.

Possible Higher Returns with Bonds, Real Estate, and Riskier Vehicles

To keep risk reasonably low but try to get a better rate of return, instead of government-issued GIPS, you could buy a diversified set of individual corporate bonds, holding them to maturity. (Unfortunately, as we mentioned earlier, inflation-protected versions of corporate bonds are generally unavailable.) However, in a true global economic disaster, they may be much less safe than government bonds.

History suggests that bond mutual funds or ETFs may not be very useful over investment horizons of 30 years or more,[12] but for shorter horizons, low-cost bond funds could play a role. REIT funds (real estate investment trusts) can also help to diversify because they invest in real estate including land, buildings, homes, or farms. In the portion of your portfolio that is not guaranteed by GIPS, you could mix in some low-cost bond index funds and perhaps REITs to moderate the risk and price fluctuations of the more volatile global equity fund. Intermediate-term bond funds, with a shorter time to maturity, will fluctuate in value less than long-term bond funds, but they also will usually offer a higher return than short-term bond funds. To diversify globally, you should complement a domestic bond fund with an international bond fund.

Other ways to increase risk in hopes of getting a higher rate of return include, as we’ve already mentioned, buying a basket of risky stocks, one at a time, and holding them for the long term. There are also very low-cost mutual funds or ETFs that invest in riskier sectors of the stock market, such as emerging markets. You might opt for these if you’ve already controlled for the risk inherent in your other investment components.

“Socially Responsible” Investment

An increasing number of people have become interested in “socially responsible” investment—that is, investing in companies and projects that seek to achieve social and environmental goals.

Socially responsible investing is certainly no worse than other active stock-picking strategies. Strong arguments can be made why it is better than most, yet, conventional investment wisdom has long held that by investing in a socially responsible manner, investors give up something in investment return. This view is wrong, because the strategy of picking stocks by estimating whether they will benefit society in the future is certainly no less likely than any other stock-picking method to produce a good return. Nevertheless, like any active stock-picking strategy, socially responsible investing entails higher costs than passive investing—that is, investing passively in market index funds. Therefore, any would-be socially responsible investor needs to take those costs into consideration. However, at the time of this writing, socially responsible mutual funds are available at expense ratios (annual management expenses) less than 0.30%. Socially responsible funds may also be less diversified across industries than total market index funds.

Alternative Investments

Only very rarely is there an unconventional investment (sometimes called an “alternative” investment) that makes sense and is not too expensive to invest in. Normally they are too difficult to find, their fees are too high, and it takes too much effort to determine (if it’s even possible) whether they’re any good. The cost/benefit analysis of alternative investments is simply unattractive. As we’ve pointed out, no more than one in a thousand such products can provide any benefit.

Some more adventuresome readers, however, may want to play around with other investment vehicles in that part of their portfolio in which they’re willing to take more risk. If you can buy individual stocks, then why not other risky investments, if the cost of buying them is not too great?

An example of just such an investment appeared in a 2007 article about hedge fund manager John Seo. (A “hedge fund” is a basket of a variety of types of investments that is available only to private, usually very wealthy, investors, or to institutional investors like endowment funds and large pension funds, not to smaller investors. But remember that this does not by any means imply that hedge funds are generally better investments; see Deadly Temptation #6 in Part II.) Seo’s hedge fund consisted of “catastrophe bonds.”[13] Catastrophe bonds (or CAT bonds) are issued to the public to cover claims that an insurance company may receive for damages from earthquakes, hurricanes, floods, and other large-scale disasters. CAT bonds really are an “uncorrelated” asset class, meaning there’s no reason an earthquake or tornado should hit at the same time as a market meltdown—there’s no correlation between these events. This quality makes CAT bonds a good way to diversify (reduce) risk. On top of this, Seo was not charging the usual sky-high hedge fund fees but way lower fees, just a fourth of a percent. If you can find an investment opportunity like this and then are willing to dedicate the large amount of time it takes to analyze it to make supersure it’s that one-in-a-thousand sensible deal—and its fees don’t obliterate whatever possible advantage you might get out of it—then go ahead and dabble in it. As long as it doesn’t put you at risk of not meeting your most basic goals, an investment like this could be worth your while. (It should be noted that at the time of this writing, CAT bonds are available for purchase only to institutional investors.)

Furthermore, if you’re a very wealthy investor or even the administrator of a large institutional fund like a pension fund or an endowment, you could consider investing directly in, for example, technology start-ups that could help to solve the world’s energy or water or health problems. It’s better to invest directly, by providing finances (capital) straight to the founders of a company, if you can, than through investment vehicles offered by the financial industry, like hedge funds or private equity funds, because you’ll avoid their high fees.

An investor who is sophisticated enough to know that 99.9% of the investments available are not worth thinking about at all can keep her mind open. Occasionally, there’s something worth considering, but frankly, you don’t really need it. GIPS and world stocks will do you fine.

SUMMARY OF RULE #1

1. You can ignore 99.9% of the investments offered by the financial industry and limit yourself to, at most, 10 specific low-cost investment vehicles.

2. The least risky of these vehicles is government inflation-protected securities, and the most risky is a low-cost world stock index fund or ETF.

3. In addition, if you’re an individual investor, you may consider simple annuities (though not other kinds of annuities) and term life insurance.

RULE #2

Look Only Forward

Now that we’ve pared the number of investment vehicles you should consider by about 99.9%, we’ll reduce them even further, and we’ll delve into how you should use them. If you read only one section of this book, this is it. It will tell you what your winning investment strategy is. This advice applies whether you’re an individual creating her lifetime saving and spending plan, or the administrator of a multi-billion-dollar pension or endowment fund.

First, let’s note how different this will be from the usual investing frenzy that people go through. They’ll often comb through the past history of the thousands—indeed, tens of thousands—of available investment alternatives, or hire a high-paid advisor or consultant to comb through it for them and make recommendations. Then they’ll often choose based mostly on which ones had better returns historically, completely ignoring the perfectly accurate admonition required by the U.S. Securities and Exchange Commission: past results are no guarantee of future performance. Then once these typical investors have made their choices, they’ll stress constantly about how well their investments are performing, wondering if they should replace them with others.

We won’t do any of that. In fact, we’ll never look back, only forward. Our rules offer a basic foundation that is different from traditional practices. Instead of constantly looking backward at historical performance, our strategy requires investors to envision their future goals and aim toward them.

THE ULTIMATE IN SIMPLICITY—AND SOPHISTICATION

Here is your basic goal, whatever kind of investor you are: you would like to get as much return as you can from your investments, and you would like to be as certain as possible of getting it. These two objectives are at war with each other, because that which is certain is low risk, and that which is low risk is less likely to produce a high return. To put it another way, a guaranteed safety net is more expensive than a shaky net. Therefore, the guaranteed safety net gives you a lower return on your investment than the less expensive shaky net does—if it works out.

You will have to compromise. You will have to decide how much of a guaranteed safety net you can afford—how much you want for certain and are willing to pay for—and how much you can only gamble for. As Rule #1 made clear in the last chapter, all you really need are two investments. One, GIPS, provides the strongest safety net you can possibly get, though at a cost; the other, a world equity fund, provides as much risk as you should prudently take—that is, as much of the risk that is likely to provide a commensurate reward. These are at poles on the risk scale; see Figure 1.

FIGURE 1 The Only Two Investments You Need

Now let’s explore how these two investments can be applied to two related scenarios: one featuring Beth, a reasonably well-off individual investor planning a lifetime saving and spending schedule; and another starring Ralph, an investor who is not well-off but is getting by, and is also making his lifetime financial plan.

LOOKING FORWARD WITH TWO INVESTORS

Beth is the reasonably well-off investor of category 2 that we described in the introduction; Ralph represents the getting-by investor of category 3—not struggling and lacking the finances to invest prudently, but not financially comfortable like Beth, either. Both investors are looking forward to that time when they’ll need to withdraw from or cash in their investments.

Beth: A Well-Off Investor

Suppose our reasonably well-off investor Beth has household income of a comfortable $150,000. She starts saving at age 35 and saves the same amount—let’s say $15,000—each year, then retires at 65 and dies at 94. What Beth invests at age 35 (see Figure 2) grows in her investment account to become her retirement income at age 65; what she invests at age 36 grows to become her retirement income at age 66; and so on to ages 64 and 94, respectively. Thus, each dollar resides in Beth’s account 30 years. That’s enough time, according to most stock market experts (based on the history of world stocks that we’ve already presented), to create a high probability that stocks will offer a good return.

FIGURE 2 Beth’s Plan

But other experts, including Zvi Bodie, a professor of finance at Boston University and author of a leading textbook on investments, point out that there’s still a substantial risk.[1] In Japan, for example, stocks fell 80% from their height in 1989. Their prices were still more than 60% below their peak 24 years afterward.

Furthermore, some histories of stock markets have observed that most studies are of the “survivors”—the countries whose stock markets have survived to the present. The obvious 20th-century examples of countries whose stock markets did not survive are Russia and China, where stocks lost all their value after the revolutions of 1917 and 1949. Those losses in Russia and China, however, were already included in the 2013 study of historical world stock returns that we cited in the introduction. Their impact on world stock returns over the period 1900-2012 was a lowering of only 0.34%.[2]

Still, there’s no guarantee you won’t lose money, possibly quite a lot, in the stock market, especially if you’re not globally diversified. So for a very solid safety net and peace of mind, if you can afford it, you may want to place some of your investments in GIPS. Beth needs to ponder exactly these points and decide how much to put in the safe investment—the GIPS—and how much in the risky one—the world stock ETF. Here on, we’ll assume Beth’s safe investment is specifically U.S. Treasury inflation-protected securities (TIPS).

Here’s a way she can decide. She can decide how high a safety net she wants and can afford for near-absolute protection against the worst-case scenario. Then she can buy TIPS accordingly and put the remainder in a global stock index ETF. For example, suppose Beth decides she wants an absolute ironclad guarantee, come hell or high water, of at least an additional $22,000 annually from her investments in retirement over and above her Social Security payments. Either she or her advisor calculates that investing half her portfolio, 50%, in TIPS will secure that guarantee.

Now look at Table 2, Beth’s annual income after age 65. The entry in the 50% row of the second column—the “Expected” column—shows the nominal and real income from her investments ($82,688 and $44,138, respectively) that Beth can expect after age 65 from her SWS portfolio (Simplify Wall Street—remember?) consisting of 50% TIPS/50% global stock index ETF, if future real returns on world stocks equal their historical averages, and if inflation is what it is expected to be. The entry in the last, “Worst possible case” column in the first row shows what she’ll get in the worst possible case, if world stocks drop by 100% losing all of their value.

TABLE 2 Beth’s Annual Income After Age 65

If Beth decides, on the other hand, that she can afford to use only 25% of her portfolio to ensure maximum security, the results are in the 25% row. This approach gives her better expected income than if she invested 50% in TIPS, but with a lower guaranteed safety net.

The Ups and the Downs of Beth’s Scenario

The scenario we just assumed—the “expected” one—in which Beth gets a real return of 5.0% and a nominal return of 7.2% on her world stock investment, is the one we deem the most likely, given what we know now. But to get an idea of what else can happen, let’s look at the high and low historical 30-year returns shown in the third and fourth columns of Table 2.

In the introduction, we pointed out that over the years 1900-2011, the worst three-decade real return (i.e., the inflation-adjusted type of return, or actual purchasing power) on global stocks—which occurred from 1910 to 1940—was 3.4% and the worst nominal return (i.e., growth in ordinary dollars) was 4.8%. The best 30-year real return was 9.9% and the best nominal return was 13.2%. Table 2 shows what Beth would receive in income with the lowest and highest 30-year historical returns in both the 50% TIPS and 25% TIPS cases. In the best historical scenario, 13.2% nominal return and 9.9% real return, her nominal annual income with 50% TIPS would be $340,000, and with 25% TIPS it would be almost $480,000! (The corresponding real incomes would be $139,000 and $197,000, respectively.)

Why did we single out this last result for an exclamation mark? Only to point out that if you’re tempted to play lottery with the stock market—no matter what the odds—in hopes of securing a life of leisure, the world stock ETF is as good a place to do it as any.

And just in case you’re going to say, “Well, Beth’s no Warren Buffett,” then give her portfolio 60 years of investing, as he has with his, instead of 30, and the same numbers compound to an annual income of $13 million a year with 50% TIPS and $19 million with 25% TIPS!

Notice how simple this is. With the 50% TIPS strategy, all Beth does, each year, is buy about $7,500 of U.S. TIPS and $7,500 of a global stock index ETF. That’s it. That’s all she has to do. She pays almost nothing in fees. She never touches her investments until she retires. She never looks back to see how they’ve done. Then she just makes annual withdrawals.

Not only is this strategy simple, but it will provide Beth with the best return she can possibly get for the risk she takes—bar none.

Why Only the Two Assets?

You might need reminding as to why Beth would opt for only these two investment vehicles. Remember, GIPS and the world stock ETF represent the two poles of risk (Figure 1), so if you combine them in different ratios, you can fall wherever you want on the risk scale. Taking more risk than the global stock ETF by investing in a less diversified portfolio may not be able to assure that your expected return will be better.

But there’s another reason to limit Beth’s portfolio to these two particular assets. Referring back to Table 2, we can understand the reason by asking, Why not increase the GIPS rate with just a little more risk by putting it in corporate bonds with a 1.2% higher rate? The reason is: that really reduces diversification in case of a very, very bad global scenario. If global stocks do terribly over some 30-year period, then that means that global corporations are failing. Hence, many corporate bonds may default. However, government bonds may not be affected, certainly not as much, because most sovereign governments can print money to pay their debts. And if the debts are inflation protected, as GIPS are, then it won’t matter to the investor if that money printing causes inflation.

In other words, the combination of GIPS and world stocks also diversifies you between government and corporate issuers of securities—a type of diversification, especially as a cushion against very bad scenarios, that few people ever mention but that is important.

Ralph: A Getting-by Investor

Now let’s come to Ralph, the getting-by investor. Ralph only earns $50,000 a year and can afford to save no more than $5,000 a year. Nevertheless, he will want to supplement his Social Security payments after he retires.

Ralph can’t afford the TIPS safety net that Beth can afford. He will need to invest all of his savings in the less secure safety net, the world stock portfolio. Let’s suppose he begins investing at age 30 and plans to retire at age 65. Each $5,000 he invests will be invested for 35 years. The last investment, at age 64, will be invested until he is 99, if he lives that long. Thirty-five years should be long enough to weather the ups and downs of the world stock market—at least it was historically. So Ralph stands a pretty good chance of getting the expected return of 5.0%—or at least not too much less, and maybe more—over each 35 years. At 5.0%, his $5,000 will grow to $27,580 a year in real dollars, or $57,000 in nominal/purchasing-power dollars.

Ralph’s portfolio, then, is actually a one-asset portfolio (or two assets if he splits the world stock ETF between a lower-cost U.S. domestic total market ETF and another, total international ETF). He is very likely to be OK, barring a disastrous scenario for the entire world economy, but he will be lacking the comfort and absolute security of Beth’s TIPS safety net.

A Necessary Postscript to Our Scenarios

We could stop talking here, except for two things. First, there’s still a little more to explain about the simple two-asset portfolio, nitpicky little details like tax implications. Furthermore, not every investor’s situation will be as easy to describe as Beth’s or Ralph’s. Not everyone starts investing in the two-asset portfolio early; some investors may be 50 or 60 or 70 years old and already have assets accumulated. The advice to those investors will be a little different, though the basic two-asset portfolio will be the same. And not everyone dies on the dot at 94 like Beth. There’s some risk of dying earlier or later. So we’ll talk about longevity insurance to ensure that you can keep withdrawing from your account as long as you live.

We’ll also talk a little about a few other investments you could make if you wanted to. You don’t really need them because the two we’ve already mentioned—GIPS and a world stock fund—cover the spectrum of risk and return reasonably well. But a few others wouldn’t hurt. You could use them if you really want to tinker.

And, then, of course, there’s probably this other problem: you don’t believe us yet. All of the other financial and investment noise you hear is still ringing in your ears, and it will keep on ringing. It’s an incessant drumbeat that you can’t get away from. You’ll be tempted to believe it. That’s why we’ll have to spend all of Part II telling you why it’s wrong.

We’ve told you how to use the two-asset portfolio in two specific cases, the case of Beth who starts investing at 35, retires at 65, and dies at exactly 94; and the case of Ralph who starts investing at 30 and retires at 65. Now we’ll have to add a small wrinkle or two to the story of Beth. Then we’ll go on to discuss other investors, whose stories may not be quite as neat and clear-cut as Beth’s or Ralph’s.

The Story of Beth Continued: Annuities and Taxes

Let’s add two more considerations to the story of Beth: longevity and taxes.

When we introduced Beth, we assumed she died when she was 94. Of course, that’s a bad assumption for any real person. You don’t know when you’re going to die. If she dies sooner, then she won’t need more from her investments than she planned. But if she dies later, she may not have enough. That’s where those annuities come in. If she’s still alive and kicking at age 94, she’ll need another fund of money to purchase an annuity.

That’s why she should really start investing a little earlier, at age 30 instead of 35, or else put away a little more each year. What she invests between age 30 and 35 will be sufficient to grow to enough money to purchase an annuity that will give her income if she needs it after age 94 for the rest of her life, however long that is.

Taxes are another serious consideration for Beth’s story. In general, taxes are very low with the two-asset portfolio compared to other portfolios. In fact, it would be hard to construct any portfolio with lower taxes. Nevertheless, taxes will have to be paid. Specifically, Beth will have to pay taxes on the capital gains each time she cashes in a portion of the world stock ETF (or the two ETFs that equal global coverage) after her retirement. (Capital gains are profits Beth makes when she sells a stock—simply her sale price minus her original purchase price.) This means her after-tax income will be a little less than the income we quoted earlier.

The world stock portfolio will pay dividends to the investor that also will be taxed. Those dividends can be automatically reinvested, but the tax on them must be paid each year if they’re in a taxable account rather than in a tax-deferred account (e.g., an IRA). The TIPS will also pay a small amount of interest each year on which U.S. federal tax must be paid. (TIPS are exempt from state and local taxes.)

In addition, U.S. federal income tax must be paid each year on any upward adjustment in the TIPS face value. For this reason, if there is a choice whether to hold the TIPS or the world stock ETF in a tax-deferred or taxable account, it’s usually better to put the TIPS in the tax-deferred account.

The dividends and interest paid by the ETFs and TIPS will be more than sufficient to cover these taxes. The amount of the dividends and interest remaining after paying taxes will be reinvested, along with each year’s additional contribution from Beth, in the TIPS or the world stock ETF.

Starting (or Continuing) Later

Not everyone creates their whole life’s saving and spending and investing plan as early as Beth and Ralph. In fact, most people don’t. And even if they do, they may look at their plan and results later as things evolve, and possibly make changes if their goals change.

When we suggested plans for Beth and Ralph, we planned them looking forward, not backward. Our only recourse to past history was that we used the long arc of investment history as a rough guide to what could be expected as a return on global equity investments. That same approach will guide us in making plans for people who start later or who, like Beth and Ralph, made plans early but update them later as time goes on. Before we discuss this further, let’s look at Beth’s plan again. That will help us to discover how we should treat other people’s plans.

Refer back to the picture of Beth’s plan in Figure 2. At each age from 35 to 64, Beth invests $15,000, divided between her two investment vehicles, TIPS and world stock ETFs. Each bar in Figure 2 shows the length of time over which the TIPS she bought in that year mature. All the TIPS she buys are 30-year TIPS. The TIPS she bought at age 35 mature (i.e., pay back their principal) when she is 65, just in time to pay part of her first year’s retirement income. The TIPS she bought at age 36 mature when she is 66, and so on.

The TIPS in Figure 2 look a little bit like a ladder. Well, that’s what they’re called, a “TIPS ladder” (a special case of a bond ladder). At any time, Beth holds in her portfolio a series of TIPS maturing in different years.

Now consider another well-off investor, Chuck. Unlike Beth, Chuck didn’t start right out using the two-asset portfolio. Let’s suppose he’s 55 years old now and has some money saved. He’ll also be contributing into his portfolio each year in the future. Look at Figure 3: we took Beth’s diagram and drew a straight vertical line through Chuck’s age. If you look at the gray bars to the right of Chuck’s age, you’ll see that at that age, Beth would have had a TIPS ladder consisting of one TIPS maturing in 10 years, one in 11 years, and so on, ending with one maturing in 30 years, the one she just bought.

FIGURE 3 Chuck’s Plan

If he has enough saved to do it, Chuck could buy that exact same TIPS ladder. Then he would be on almost an equal footing with Beth. Whatever is left over he could invest in the world stock ETF, as Beth did. That way, he’ll start out in a similar position on the ladder as Beth.

Well, maybe not quite. If his remaining assets happen to be exactly the same as what he would have had, if he had been investing the same amounts as Beth in the world stock ETF all along, then he would be in exactly the same position on the ladder as Beth. After that, Chuck could invest his contribution each year just as Beth does. Then everything would be the same as Beth.

One more thing: each investment that Beth made in the world stock ETF stayed there for 30 years before she withdrew it. Some of Chuck’s money outside of TIPS will now be invested for less than 30 years; for example, his first retirement withdrawal will occur in only 10 years. Investments in stocks can take wilder swings over shorter time periods than over longer periods. Chuck thus might want to take a little less risk than he would take if he invested it only in the world stock ETF.

Risk/Return Preferences

But why should that be? If Chuck’s portfolio is exactly the same as Beth’s—as if he had been investing like Beth all along—why wouldn’t he continue to do the same thing as Beth? Whether Chuck has exactly the same portfolio as Beth has now or not, what he should do with it depends on his attitude toward risk and return.

For example, we already made a tacit assumption about Beth’s attitude toward risk and return. We assumed that she needed to have a 100% guaranteed safety net at some level (depending on whether she opts for 50% or 25% TIPS). Falling below that safety net was forbidden. But we made no assumption about her attitude toward risk and return beyond that. We just assumed that investing in the world stock ETF would fit it.

We don’t really know if the world stock ETF would exactly fit Beth’s risk/return preferences—sometimes called her “utility function”—and for a good reason. We’ll never know Beth’s utility function.

Behavioral economists sometimes run experiments to determine people’s risk/return preferences. For example, they’ll ask someone, “Would you rather gain $2 or avoid losing $1?” They discover that people’s preferences are internally inconsistent. That is, people will give one response to one question about risk and return, and another to another question, but the responses often logically contradict each other. It’s impossible to find out with any certainty exactly what somebody’s preferences are. So the ideal of calculating exactly what somebody’s portfolio should be based on a precise determination of that person’s risk/return preferences, or utility function, and performing the kind of mathematical optimization that financial economists dream of is exactly that—a dream.

Nevertheless, we can know some things with reasonable certainty; and the theoretical concepts, even if they’re not very practical for most purposes, can help you do that. One of those things we can know with reasonable certainty is that the world stock ETF “dominates” other stock portfolios, in the sense that it has a lower probability of losses and better chance of gains because of its global diversification and lower fees.

There’s pretty good evidence that over a 30-year investment horizon, global stocks also dominate investments in bonds.[3] That’s why we didn’t consider putting any bonds in Beth’s portfolio (other than the government-guaranteed TIPS, held to maturity), because we assumed that over a period of 30 years, their probabilities of gains or losses would be dominated by the world stock ETF. For shorter periods, however, such as 5 or 10 years, including low-cost bond index funds in the portfolio may help to contain shorter-term risk.

When Concentration May Be Better Than Diversification

The opposite of diversification of investments is concentration of investments. If, for example, you deliberately decide to hold a portfolio of only 10 individual stocks instead of a global stock ETF, you would be said to be concentrated—you’d hold a concentrated portfolio.

What’s wrong with a concentrated portfolio? Well, in the standard “modern portfolio theory” (MPT) analysis taught in most university investment classes in finance, the more concentrated your portfolio, the more risk you’re taking on that won’t be rewarded with a higher expected return. There’s no point in taking on the risk of concentration if it won’t be rewarded.

But MPT defines risk very simplistically, as how much the market value of the investments fluctuates. This doesn’t always capture what you mean by risk. In fact, if you diversify, it reduces the risk that your return on investment will be low, but it also reduces the risk that it will be high.

Suppose that you actually wanted the risk that your return will be high and you don’t mind the risk that it might be low instead in order to increase the chance of a high return. Then you would have what economists would consider a very unusual—they might even say an irrational—utility function. People are supposed to get more utility—that is, satisfaction—from avoiding downside risk than they gain from an equal amount of upside opportunity. And normally, they do. But what if you’ve already eliminated all the downside risk that you care most about, by investing a portion of your portfolio in TIPS? If you’ve already eliminated the financial risk that really concerns you greatly by creating a strong safety net with some of your money, then you might consider what you have left as “play money.” With this money, you’d rather take a chance on doubling it even if there’s an almost equal chance it will be cut in half. Sure, you could diversify so that the chances would be between adding 10% to your money or losing 10%; but that would be less interesting. So you may want to concentrate, and why not?

We have to realize that “risk” is not always what MPT says it is—in fact, it usually is not.

Back to Beth and Chuck (and Even Ralph)

We’ve just given our rationale for using only the two-asset portfolio for Beth. What about Chuck? Some of his dollars will be invested not for 30 years but for only 10 or 20 years. Over that short a horizon, a bond index ETF will often have a lower probability of losses—based on history and also theory—than the world stock ETF.

Let’s assume Chuck has some bare minimum retirement income requirement, just like Beth. Once he creates a TIPS ladder just like Beth’s (assuming he has enough saved at 55 to do that), he will be guaranteed (by the U.S. government) to meet that requirement. But what should he do with the rest of his investments? What will fit his (purely conceptual) utility function?

There’s no sure way to answer this question. However, we do know one thing: if it’s not simply the world stock ETF all by itself, as it is for Beth, then it is some combination of the 10 investment vehicles we listed in Rule #1. That makes it a lot easier than if we actually had to consider 100,000 investment vehicles. The simple two-asset portfolio we introduced at the beginning (GIPS plus a global stock fund) can probably satisfy most people’s risk and return preferences, and we’ll show you in a moment how Beth and Chuck, and even Ralph, can continue to use it.

However, risk/return preferences—or utility functions—can come in many shapes and forms. There’s no scientific way to match them or even to determine precisely what they are. As long as you stick to the 10 basic low-cost investment vehicles and don’t take too much risk overall—or, for that matter, too little—you can mix and match them in a variety of ways.

Chuck and the Two-Asset Portfolio

Chuck, looking forward into his future, may not want to buy exactly the same TIPS ladder as Beth would have had by age 55. He may have a different amount left over in his portfolio after buying the same TIPS ladder than Beth would have had. This alone might cause him to divide his portfolio differently. Or he may simply perceive a shorter time horizon ahead of him and want to reduce his risk. He may want to increase the TIPS ladder relative to Beth’s, if he can afford the extra safety net.

But what if Chuck can’t afford a safety net? What if he’s more like Ralph than like Beth? Perhaps the assets he’s accumulated are more like what Ralph will accumulate at age 55, and they won’t be enough for him to afford a secure safety net.

The answer is obvious: then the “ladder” he gets on is the same one that Ralph would be on. Ralph didn’t expect to be able to afford a safety net, and unless something unusually and massively good happens, Ralph still won’t be able to afford it. Chuck is in a position as if he had started investing when Ralph started investing—he’s just in the middle of it. So his two-asset portfolio will be the one-asset portfolio, the world stock ETF, just like Ralph’s. And yes, he may be biting his fingernails, just like Ralph. But it’s the best he can do. Chuck will have to take more chances even if he doesn’t confine himself to the two-asset portfolio, no matter what mix of the 10 basic investment vehicles he chooses.

This brings us to another observation about Ralph, but it applies to Beth and Chuck, too. If Ralph’s investments do much better than expected by the time he reaches the age of, say, 55, he may decide he can afford a safety net now—or, if the investor were Beth or perhaps Chuck instead of Ralph, a bigger safety net. In other words, he may decide to “lock in” some of his gains by putting some of his investments in TIPS. This would be a sensible thing to do.

On the other hand, Ralph (or Chuck) may arrive at this age and find that his investments have fared far worse than predicted. In a case like this sad turn of events, he might need to work longer and/or live on less money than he had expected. This struggling-investor strategy may be disappointing if you were nurtured on the 1980s and 1990s notion that everyone will become well-off by investing. But it’s a reality.

SUMMARY OF RULE #2

1. Look only forward to your own future plans and goals. Don’t waste time poring over investments’ historical performance, and don’t waste money hiring someone to do so for you, either.

2. An investor decides how much to put in GIPS and how much to put in a global stock index fund—the only two investments really needed—depending on how high a guaranteed secure safety net she can afford.

3. The mix can be altered over time by projecting forward to see if she can afford to strengthen the safety net.

RULE #3

Tune Out Noise

Rule #3 will be short, because the chapters in Part II will explain in detail why it’s so important to follow this rule. For now, we’ll simply identify some of the noisy opinions and recommendations you’ll hear if you follow the two-asset portfolio route, and to all of it we say, “Tune it out.”

You’ll hear that you can and must beat the market, yet you aren’t trying to beat the market. Tune it out.

You’ll hear that you must diversify among many asset classes and many mutual funds or ETFs, yet you’re only investing in one or two. Tune it out.

You’ll hear that you must rebalance regularly to maintain constant allocations to asset classes, yet you aren’t rebalancing regularly. Tune it out.

You’ll hear that you must use a mathematical optimization model to perform an asset allocation, yet you haven’t run an asset allocation model. Tune it out.

You’ll hear that gold is a hedge against inflation, yet you aren’t investing in gold. Tune it out.

You’ll hear that scientifically designed funds perform better than total market index funds, yet you’re investing only in the global market index. Tune it out.

You’ll hear that you must reduce your risk as you age by using target-date funds with a “glide path,” yet you aren’t choosing a glide path. Tune it out.

You’ll hear that rich people invest in complex, sophisticated investments and you can also get those returns, yet you aren’t investing in complex, sophisticated investments. Tune it out.

You’ll hear that someone’s financial advisor or broker gets excellent returns for his or her clients, yet you aren’t using that financial advisor or broker. Tune it out.

You’ll hear that academic studies using a 3- or 4- or 18-factor model show it’s better to tilt your portfolio toward certain asset classes, yet you aren’t tilting toward those asset classes. Tune it out.

You’ll hear that you must monitor your funds’ performance, yet you aren’t even monitoring your funds’ past performance. Tune it out.

PART II

Investing’s 7 Deadly Temptations

A lie can be halfway around the world before the truth gets its boots on.

—CHARLES HADDON SPURGEON

RULE #3 described the sort of noise you’ll hear from Wall Street and the mainstream securities industry. Now the seven chapters of Part II will explain why almost all of that noise merely causes confusion and even sheer delusion, in many cases. The financial industry’s typical counterarguments only contribute to the misperception that many people think investing is too complicated for them and thus they need “expert” help.

We admit that the noise is very tempting—it’s a siren song. When you listen to Wall Street and the mainstream financial industry, you’ll think that they’re offering ways to make more money with your investments than you could make with much less help, using a simpler method. You’ll have to remind yourself, over and over again, not to listen.

In Part II, we elaborate on many of the enticements that strive to make you believe that you need an expert to manage your money—or at least to steer you through the confusion, using sophisticated advice that relies on complicated models and arcane information. We call these the 7 Deadly Temptations, but know that more than these core seven exist. Our bottom-line recommendation is that you stick to the simplest possible investment strategy and, ignoring Wall Street’s siren song, don’t vary from it.

DEADLY TEMPTATION #1

Beat the Market

We told you that you must tune out siren calls to “beat the market.” Now we’ll tell you why. The basic reason is this: You are extremely unlikely to find a manager, an advisor, or a strategy that will enable you to predictably beat the market. It will be even more difficult to know whether you have found one. It will certainly not be worth the cost to try.

THE ODD BEHAVIOR OF STOCK PRICES

In their textbook Principles of Corporate Finance, authors Richard A. Brealey and Stewart C. Myers report the following event in stock market history:

In 1953 Maurice Kendall, a British statistician, presented a controversial paper to the Royal Statistical Society on the behavior of stock and commodity prices. Kendall had expected to find regular price cycles, but to his surprise they did not seem to exist. Each series appeared to be “a ‘wandering’ one, almost as if once a week the Demon of Chance drew a random number … and added it to the current price to determine the next week’s price.”[1]

In other words, each change in price seemed to be drawn at random from a bowl full of pieces of paper with percent price changes written on them—“up 1%,” “down 0.5%,” “up 2/3%,” and so on. If that was how price changes were determined, what would be the use of trying to predict securities prices—or the stock market as a whole? Whatever you predicted, something random would happen that might agree with your prediction or might not, but it would be no better than any other prediction.

This phenomenon of random securities price movements was not fully explained for more than 10 years after that. In 1965, the economist Paul Samuelson wrote a paper titled “Proof That Properly Anticipated Prices Fluctuate Randomly.” The proof is really pretty simple. Think about it this way: Everything about a public company (a company with stock listed on a stock exchange) that can be made public is required by law to be made public immediately. Then, thousands of analysts leap on the information and recommend buying or selling the stock based on that information. The stock reaches a consensus price almost instantly. This is what people mean when they say that markets are “efficient”—there’s hardly any time at all between the release of a company’s public information and setting that company’s stock price.

So what will be its price tomorrow? Well, that depends on the information that’s publicized between now and tomorrow. It could be anything—anything, except for one fact we do know about it: it’s information we know nothing about now. So it is unpredictable information.

Unpredictable information is random information. It is as likely to drive the price of the stock up as to drive it down (well, it’s always very slightly more likely to drive it up because companies’ earnings do grow over time, on average). You can’t tell which.

And neither can professional investment managers—no matter how smart they are, no matter how sophisticated, no matter how good their advertising is at selling their management skills to you, and no matter how well their investments performed in the past.

This is just a fact. It’s provable, using real data—as much real data as you’d like. It’s been proven again and again and again, every decade at least and more, going all the way back to the 1930s. You cannot predict which stock, or which mutual fund, or which investment management firm, will do better in the future by looking at its past performance. Many learned books about investment underscore this fact, beginning with Burton Malkiel’s A Random Walk Down Wall Street as well as Michael’s previous book The Big Investment Lie.

THE HISTORY OF ACADEMIC STUDIES

The first widely known academic study of the investment performance of professional investors was reported in the July 1933 issue of the journal Econometrica.[2] The study is known as the Cowles Report, because it was performed by the Cowles Commission, endowed by investor Alfred Cowles. As reported in The Big Investment Lie:[3]

One of the first research projects of the Cowles Commission was to study whether it was possible to predict the movement of stocks and stock markets. The preliminary version of the 1933 report, titled “Can Stock Market Forecasters Forecast?” was summarized in a famous three-word abstract: “It is doubtful.”

The Cowles report reviewed the stock recommendations and market forecasts of sixteen financial services, twenty fire insurance companies, twenty-four financial publications, and those of William Peter Hamilton, the editor of the Wall Street Journal. In each case, the recommendations performed worse than the average common stock, by an amount ranging from 1.2 to 4 percent per annum. For example, the report’s summary states:

“Sixteen financial services, in making some 7500 recommendations of individual common stocks for investment during the period from January 1,1928, to July 1,1932, compiled an average record that was worse than that of the average common stock by 1.43 per cent annually. Statistical tests of the best individual records failed to demonstrate that they exhibited skill, and indicated that they more probably were results of chance.”

In other words, the stock forecasters not only performed randomly, but their performance was actually even a little worse than random.

In the 1964 book The Random Character of Stock Market Prices,[4] edited by MIT professor Paul Cootner, two papers by Holbrook Working and M. F. M. Osborne reported observing the same randomness in securities prices that Kendall had described to the Royal Statistical Society in 1953. Then in 1966, William F. Sharpe, who would later win the Nobel Prize in economics, performed a study of 34 mutual funds over two 10-year periods. He concluded, “Results actually obtained by the holder of mutual fund shares (after the costs associated with the operation of the fund have been deducted) fall somewhat short of those from the Dow-Jones portfolio. This is consistent with our previous conclusion that, all other things being equal, the smaller a fund’s expense ratio, the better the results obtained by its stockholders.”[5]

A widely cited 1968 article by Michael C. Jensen, then a professor at the University of Rochester College of Business, concluded from a study of 115 mutual funds over the period 1945-1964 “not only that [these] mutual funds were on average not able to predict security prices well enough to outperform a buy-the-market-and-hold policy, but also that there is very little evidence that any individual fund was able to do significantly better than that which we expected from mere random chance.”[6]

All of this evidence caused economics Nobel laureate Mer-ton Miller to declare, “If there’s 10,000 people looking at the stocks and trying to pick winners, one in 10,000 is going to score, by chance alone, a great coup, and that’s all that’s going on. It’s a game, it’s a chance operation, and people think they are doing something purposeful …but they’re really not.”[7]

Nevertheless, occasionally a research study produced some pattern in past data that appeared to be predictable. Because by that time it had become widely accepted in the academic community that the market was “efficient” and unpredictable, when professor G. William Schwert, also of the University of Rochester, studied these patterns in 2003, they were termed “anomalies.” After studying several claimed anomalies that had been observed in historical data, Schwert concluded with what we’ll call the “Schwert rule”: “After they are documented and analyzed in the academic literature, anomalies often seem to disappear, reverse, or attenuate.”[8] Schwert then continued: “This raises the question of whether profit opportunities existed in the past, but have since been arbitraged away, or whether the anomalies were simply statistical aberrations that attracted the attention of academics and practitioners.”

In other words, Schwert raises two possibilities. One is that the anomaly was a “real” phenomenon—an underpriced stock, for example—that if someone knew about could have been taken advantage of; but as soon as it was studied and publicized, the opportunity disappeared. The second possibility is that the investigator who found the anomaly had really only detected a random pattern in the data, the result of too much sifting and processing of the data—a practice called “overfitting.”

Studies on stock performance have continued to be performed on a regular basis. For example, in 1997, Mark M. Carhart, then at the School of Business Administration at the University of Southern California, studied a sample of 1,892 mutual funds over the years 1962-1993 and concluded, “The results do not support the existence of skilled or informed mutual fund portfolio managers.”[9] And in a 2010 study of 2,076 mutual funds from 1975 to 2006 by Laurent Barras, Olivier Scaillet, and Russ Wermers,[10] reported in the New York Times,[11] the authors, using a method called the False Discovery Rate test, concluded that 99.4% of the managers had no stock-picking skill, while the 0.6% that appeared to have some skill was “statistically indistinguishable from zero.” In other words, it could easily have been a statistical fluke.

The strong conclusion, as one commentator stated it, is that “when you pay extra to ‘beat the market,’ you end up, in the long run, paradoxically underperforming the market by, at least, the amount of your fees.”[12]

THE ANECDOTAL HISTORY OF STAR PERFORMERS

Nevertheless, when people hear the results of these statistical studies, they still don’t believe them. It’s like a smoker who hears the results of the statistical studies that show that smoking causes cancer but doesn’t believe it will happen to him. He’ll say something like, “Well, Homer down the road is 94, and he’s been smoking for 80 years and is still fine.” Homer’s case, however, doesn’t disprove the statistical result. Homer was just lucky. The problem is that people hear all the time about star investors who have spectacularly outperformed the market, and they ask, “Well, what about him or her?”

There are three possible answers. One is that, like Homer, that star investor was just lucky. Another is that, possibly like Homer, he did something that we don’t understand that somehow staved off cancer—or beat the market. But we’ll really never know which. And the third answer is …just wait. Because far more often than not, those star performers subsequently wind up plummeting to the ground. Let’s take the history of these spectacular rises—and falls.

In fact, to start with a well-known example, one of us, Michael, was once exchanging emails with a very prominent financial journalist on the topic of the possibility of beating the market. The journalist was knowledgeable about investing and inclined to agree, but he said, “Yes, but what about Bill Miller?” Unfortunately, Michael’s journalist friend wasn’t quite up on the story of Bill Miller—he had old information.

The Wikipedia entry on Bill Miller[13] provides a very interesting history and analysis of Miller’s investment performance from 1991 to 2005, when he beat the stock market (the Standard & Poor’s [S&P] 500 index) 15 years in a row, becoming a legend. One analyst calculated that the probability of this happening at random was only 1 in 2.3 million—making it all but impossible that Miller’s performance was only luck (but the Wikipedia site reports that another analyst disagreed with this probability calculation).

In any case, it didn’t last. The Schwert Rule kicked in, as it almost always seems to. According to a May 2012 Forbes article, Miller’s subsequent performance over the next five years was an appallingly bad return of -30.05%, compared to the market’s (S&P 500) return of +10.48%.[14] This outcome “left many latecomer investors far worse off than if they had never followed the ‘hot money’ into Miller’s fund in the first place…. To add salt on the wound for recent ‘hot money investors’ Miller’s fund charged a sky high expense ratio of 1.77%.”

This is not an atypical result for investors who think that when they see that an investment manager has done well, then they should invest with that investment manager. And we won’t even mention Bernie Madoff again.

Two investment managers whose names are still legendary are Julian Robertson and George Soros. But few seem to know that while they both experienced long hot streaks, they both performed extremely poorly at the end of the 1990s. Robertson’s funds, for example, underperformed the S&P 500 by 40% in 1999.[15] Since this decline occurred right after his funds had the most money invested in them—$23 billion—this means it is possible that more investors may have lost money investing with Robertson than made money. Yet his reputation as an investing guru lives on.

John Bogle, the founder of the Vanguard Group of mutual funds and a much-admired pioneer of index investing, reports in his book Don’t Count On It[16] the sad story of Gerald Tsai, a star investor of the 1960s. “Tsai was the inscrutable manager who had turned in a remarkable record in running Fidelity Capital Fund—+296 percent in 1958-1965 compared to a gain of 166 percent for the average conservative equity fund.” Nevertheless, he subsequently racked up by the end of 1974 the worst eight-year performance record in the entire mutual fund industry, according to Bogle—a cumulative loss of 70%.

Here’s another example of reversal of investment performance from The Big Investment Lie:[17]

A striking example of the point that past performance does not predict future performance is a story in the July 11,2003, issue of The Economist. The Economist’s story relates the astonishing fact that the top ten mutual funds for the three years from the end of 1996 to the end of 1999 were all among the worst-performing 7 percent of mutual funds in the next three years.

If you had invested at the beginning of 2000 in the top ten performers for the previous three years, you would have experienced a brutal come-uppance. In the next three years, you would have lost 70 percent of your investment. Many investors actually did this—a large proportion of them on the advice of professional investment counselors.

At any time there will always be some investment professional who has had an outstanding record of performance—in fact, this has to be true, even if performance really is completely random. If you toss a thousand pennies 10 times, one of them will randomly come up heads 10 times in a row, even though the chances of that happening are only one in a thousand.[18] And there are many thousands of investors.

But if you see performance like that and assume it will continue, take John Bogle’s title as your guide: don’t count on it.

THE TREND IS YOUR FRIEND

People seem to use the little motto “The trend is your friend” to mean that whatever investment has performed well recently will keep on performing well. But that is a mistake.

For example, many people invest in mutual funds. They have a choice of mutual funds to invest in. So they have to choose. These mutual funds always provide information about their historical returns—typically one-year, three-year, and five-year investment returns. Almost invariably, people choose to invest in the fund with the best one-, three-, and five-year returns. Many voices in the industry—financial advisors, representatives of the mutual funds, industry experts, and even impartial mutual fund screeners—suggest this is logical and the best practice. It even feels good to do research and hunt down the best products.

But it makes no sense.

“Now, come on,” you say, “how can that make no sense? If you want to choose a lawyer, you’ll want the one that has the best record of cases won to cases lost. If you want to choose a surgeon, you’ll want to choose the one with the best record of successful operations as compared with unsuccessful ones. If you want to choose a baseball player, you’ll choose the one with the best record of hits, home runs, and runs batted in. How can you not choose the mutual fund with the best investment record?”

The reason is simple: because that record bears no—repeat no—relation to how well that fund will do in the future.

“Huh? You mean that if some star investment manager has done well in the past and has become a celebrity, that doesn’t mean he’ll do well in the future?”

That’s right—no, it doesn’t. Just the information that his historical record is good tells you nothing, absolutely nothing at all, about how well he’ll perform in the future. That’s why the U.S. Securities and Exchange Commission (SEC), the regulatory agency that governs much of the investment business, requires mutual funds to put words on its advertising that read something like “Past performance is not necessarily indicative of future results” or “Past performance is not necessarily predictive of future results.” The trouble is, these words aren’t strong enough. The truth is that past investment performance has no ability at all to predict future results. It’s not just not necessarily predictive; it’s not predictive at all.

What does that mean, exactly? Well, suppose you took 20 funds that performed below average in the past five years and 20 that performed above average. Which group do you think will have more funds performing above average in the next five years?

You would assume more of the historical better-performers will perform well in the next five years than the worse-performers. But you’d be wrong. On average, it’s just the same. A fund that performed well in the past will have no greater likelihood of doing well in the future than one that performed badly.

Twice during his career, as part of his job, one of us (Michael) has worked with very large, private databases of the performance of professionally managed investment funds. The first time, he had access to the largest database of professionally managed institutional investment funds in the world (pension funds, endowment funds, foundation funds, union funds, profit-sharing funds, and others), which was privately owned by the brokerage firm A. G. Becker & Co. The second time he had access to the database of investments managed or advised by one of the largest brokerage and investment consulting firms in the United States.

In both cases, he performed studies exactly like the one we just mentioned. He looked to see how professionally managed investment accounts ranked in one five-year time period and divided them into the best and worst performers, then looked to see how they ranked in the next five-year time period. There was absolutely no consistency from one time period to the next. There was no way to tell how a fund would rank in the future by looking at how well it had done in the past.

This phenomenon has been studied by a long series of academic investigators over several decades, using a variety of methods.[19] The results are always the same. Past performance is not at all predictive of future performance. In fact, the only thing that is predictive of after-fee investment performance is the level of fees itself: the higher the fees, the worse the fund will perform.

Unfortunately, we’ve seen people get tripped up by their language again and again. After Michael’s book The Big Investment Lie was published at the beginning of 2007, he did a number of speaking engagements. One time, he shared the platform with another man, who had also written a book. What was the nature of the man’s message? He said, “If your investments are not performing well for you, you should change them.”

What can be wrong with that? So Michael asked him, “Do you agree that you can’t predict the performance of an investment?” Well, he did—Michael knew he did, because he had said so in his talk. “So do you mean that if your investments have not performed well for you, then you should get rid of them because they won’t perform well in the future?”

No, he said, I mean if they’re not performing well for you, why would you keep them?

Michael was exasperated—he couldn’t seem to get clear that “not performing well” meant the same as “haven’t performed well”; and “haven’t performed well” meant it was history, and it wouldn’t help you predict how investments would perform in the future.

This is the tricky problem with trying to get people to understand that you just can’t use past performance to predict future performance. The assumption that you can is embedded deep in our consciousness and even in our language.

CHARTING: MUCH ADO ABOUT NOTHING

Nevertheless, lots of people use charts and statistics about past performance to try to predict future performance. This practice is called “charting,” and here’s everything you need to know about it: it doesn’t work. Or maybe the more accurate thing to say is it doesn’t work statistically.

That is, it doesn’t work just because it works sometimes or for a while. You might play the slot machines in Vegas, and if you say to yourself every time you pull the lever on a slot machine (or whatever you do these days), “Mama needs a new pair of shoes” and you go on a winning streak, it doesn’t mean that in the future you’ll continue to win as long as you keep saying, “Mama needs a new pair of shoes.” It doesn’t work in principle, and it doesn’t work empirically. That is, if someone studied a group of people who said to themselves, “Mama needs a new pair of shoes” every time they pulled the lever, and another group that didn’t say that to themselves, one group wouldn’t do any better than the other. That’s true even if you happened to go on a winning streak once when you said to yourself, “Mama needs a new pair of shoes.”

The same thing is true of mutual funds, and every other kind of investment. They’ll win sometimes, and they’ll lose sometimes. Sometimes they’ll go on a winning streak. But it doesn’t prove that they can keep it up. Whatever they’re doing is the equivalent of saying to themselves, “Mama needs a new pair of shoes.”

OK, we understand that this is hard to believe. Investing isn’t like playing the slot machines. In investing, there are things to know, real things. If you know them, you can predict.

What things? What makes you think there aren’t also real things to know when you play the slot machines? Slot machines are machines, so they have gears, connecting chains, or whatever; actually, these days they probably just have electronics. But still, they obey the laws of physics, like any physical device. How can that not be predictable, if you knew enough about how it works?

For some reason, we have no problem understanding that it doesn’t matter how much you know about how a slot machine works—you still can’t predict its result. It’s designed so you don’t know, and it’s designed well.

The same thing is true of a coin toss. Everybody assumes it’s random. You wouldn’t expect a theoretical physicist with a Ph.D. to predict the result any better than anyone else. So why do people assume that if you’re smart enough or know enough or do enough analysis, you’ll be able to predict the change in price of a stock?

Well, all right. We’ll have to admit it. There is a difference. It’s true that if you knew something about a company before anyone else knew it, you might be able to predict ahead of time how the company’s stock will move. If you were the only one who knew that Apple was about to announce a new, paradigm-busting invention tomorrow—or that it was about to announce that iPhone sales were not going very well—you’d have a pretty good idea what would happen to the stock price tomorrow, and you’d buy or sell the stock accordingly.

The trouble is, you don’t know things like that before anyone else knows them. And if you ever do know them, you’re probably a corporate insider, and it’s illegal for you to trade on that information.

The point is that there are many people who work full-time—and more than full-time, in most cases—in the financial industry who collect huge salaries and bonuses to try to get information like that about companies. If someone finds something that others don’t know, it won’t stay private for seconds. It will change the price of the stock so that it won’t be a good deal to buy the stock anymore (or sell it, if the news is bad), because the price will already have changed to reflect the information. It happens too quickly, just because there are so many people who get paid so well to watch out for it.

True, if people who do this didn’t get paid so well just to watch, there would be fewer of them doing it. Prices might not respond so fast to new information. But that’s not the situation now; it’s not even remotely close to the situation now. There are far too many people in the financial industry who get paid to watch out for this sort of thing. Why? Because so many people invest in the mutual fund that did the best over the past one, three, or five years. They’re paying the stock analysts’ salaries and bonuses by paying big fees to a mutual fund that might accidentally have done well in the past but won’t in the future.

Now, suppose you find out something about Apple that you think other people don’t know. How do you know they don’t know it? How do you know that Apple stock hasn’t already been priced to take account of that information? Could you tell whether it has or hasn’t by looking at the price of the stock? The answer is no. So not only are you unlikely to know important information about a company before anyone else knows it, you’re even more unlikely to know it and to know that no one else knows it. And if you don’t know both of those things, you won’t have any advantage in trading the stock.

Forget the whole idea of predicting the price of a stock, not to mention a whole portfolio of stocks like a mutual fund. It’s almost as unlikely that you can do it as that you’ll see a unicorn. It’s even less likely than winning the lottery—but the payoff isn’t as big.

And there’s a cost to just trying to predict the price of a stock or the returns on a mutual fund. If the cost is very low, it doesn’t matter much—you might as well try, if you want. But usually the price isn’t low—just like if you play the lottery a lot, you’ll pay and pay and pay and yes, there’s a chance you’ll win; but the chances are much greater that you’ll just pay, and lose.

Many people seem to believe that if you just trade stocks a lot—or currencies, or options, or some other thing—you can make a lot of money. This is the urban legend of day trading. We can’t tell you how many times we’ve heard this kind of story. Someone we meet or know will mention offhandedly that some friend of theirs, or relative, or someone they know, quit his job and just trades stocks, or options, or currencies, at home. Sometimes they’ll add that this person does very well.

Sorry, it just isn’t true. It’s so far from the truth that it’s a genuine, 100% colossal whopper. People don’t tell the truth about these things. It’s funny how often you discover that a few months later, people who quit their jobs to make money trading securities at home are back at their jobs. If it was working so well, why didn’t they keep on doing it? People who do this kind of trading tell you about their winners but they don’t tell you about their losers. And they don’t tell you what their net gains or losses are; often they don’t even know.

Many day traders and at-home traders who believe they can make money this way are prey for hawkers who sell “systems” or training courses at tuition fees of thousands of dollars to trade stocks, or options, or currencies, or commodities. The systems they sell can’t be verified to work, using any scientific procedure for verification; they just look complicated and technical enough to work.

Many people who trade for themselves at home pay for systems like these and pay a lot of brokerage fees. But they almost never make much, if any, money themselves. Of course, they often lose. What they’re doing is very much like gambling at Las Vegas—their results are random, and the house takes a substantial share of what they bet. Of course, like people who gamble at Las Vegas, sometimes they make a killing—but usually not for long. You won’t find anybody who started with $10,000 and spent four years trading at home who emerged with a million dollars.

UPS, DOWNS, AND DITCHING CONVENTIONAL WISDOM

Earlier we pointed out that there is no trend. But now we’ll say there may be an exception. What would a trend mean, anyway?

Suppose the chance that a particular stock will go up tomorrow is 50%. Now suppose on Tuesday the stock goes up. What are the chances it will go up on Wednesday? If Tuesday’s result and Wednesday’s result are independent of each other—that is, if there is no trend—then the chance it will go up on Wednesday is 50%, just like the chance on Tuesday. The fact that the stock went up on Tuesday hasn’t changed anything. Whether or not the stock goes up on Wednesday has nothing to do with whether it went up on Tuesday. Most studies and measurements have found that there was no tendency to trend. When you assess the chances of what will happen tomorrow, it doesn’t help you to know what happened yesterday.

But if there were a tendency to trend, then the fact that the stock went up on Tuesday would change the probability that it will go up on Wednesday. It will make it a little higher, say, 52%. More recent studies have, in fact, found some evidence of momentum in short-term price changes. That is, what goes up has a slight tendency to keep going up, and what goes down has a slight tendency to keep going down.

The momentum effect has been documented over short time periods, such as from month to month. But “reversion to the mean”—which means essentially “what goes up must come down,” the opposite of the momentum effect—has been observed over longer periods such as a few years. In fact, it must be observed over longer periods if there is a momentum effect over short periods.

Reversion to the mean says that at some time, momentum will not continue but will reverse. Observations of financial crises suggest that momentum is gradual and long-lived, but reversal is sharp, sudden, and unpredictable. You won’t get good long-run performance by relying on momentum.

What causes that momentum effect? That has not been studied very well, partly because the fields of finance and economics got stuck in “mainstreams,” which only study the same model over and over again with slight changes here and there. But the idea is something like this.

People, including professional investment managers, buy or sell securities not just by watching their prices but by watching one another. If they think other people are going to buy and drive the price up, then they’ll try to buy first, so they can take advantage of the rising price.

There’s an understandable tendency to think, “If I managed to run fast enough to the broker to buy before the price went up too much, then I should—shouldn’t I?—be able to run fast enough to sell later before the price goes down too much.” In other words, I’ll get in before the price goes up too much; then I’ll just yank my money out before the price drops.

What makes you think you can yank your investment out faster than the price drops? Obviously not everyone can succeed in doing that at once. You might succeed sometimes, but if you succeed only part of the time, you’ll be better off not even trying.

Then why is “Monitor your investment performance” such deeply embedded conventional wisdom? The standard procedure is to compare the return you have experienced in your investments to the return on a market index. But for what purpose? If beating the market index is something that can’t be done by skill but only by luck, what could possibly be the purpose of monitoring whether you did it or not?

If you were gambling at Las Vegas or Macao and monitored the performance of your bets on, say, a slot machine, what would be the purpose? Would it be to determine whether you should switch slot machines? If you did that, you’d probably be going from slot machine to slot machine and not getting any better results. Well, that’s what happens when you monitor your investment results.

When you play the slot machines, the only thing that really matters is how much money you have left and whether you really want to bet any more of it. The history of your performance doesn’t matter. It’s similar with your investments. What matters is how much money you have now and what investment strategy will increase or safeguard it in the future. The history of your performance is irrelevant.

That’s why we told you, when you hear the noise saying you must monitor your funds’ past performance and yet you are not monitoring your funds’ past performance, tune it out!

SUMMARY OF DEADLY TEMPTATION #1

1. Beating the market is impossible except by chance: future investment performance cannot be predicted, except for the impact of fees.

2. Monitoring past investment performance is pointless.

DEADLY TEMPTATION #2

Trust It All to Our Expertise

We told you in Rule #3 that if someone—a friend of yours, perhaps—tells you that a particular broker or financial advisor has done very well for her clients, and you should consider hiring her, you should tune it out. Wouldn’t it be great, though, to have someone take care of your investments? They would grow nicely, and you wouldn’t have to worry about a thing. It sounds very comforting to have someone just deal with the whole realm of investing for you, especially if you’re convinced it’s all too complicated and you just don’t want to think about it.

But is it worth giving up one-third to three-fourths of your investment gains for this comfort? If you knew that another, lower-cost advisor—offering simpler, less technical-sounding investment advice, or even no advisor at all—would provide you half again to three times more in investment gains, then how comfortable would you feel?

Because that is, indeed, how much it costs—even when the cost appears low as a percentage of your assets. Let’s go through the numbers to show what we mean.

Suppose that you’re 35 and you save $10,000 a year until you’re 65, and suppose your friend’s advisor charges 1% of investment assets annually. All else being equal, your real investment gains when you’re 65 will be only two-thirds what they would have been if you didn’t have to pay the 1%.[1] You will have lost a very large amount to pay your advisor.

This becomes much worse in the typical case where investors pay fees not only for an advisor, but for overly costly investment products that the advisor recommends. For example, if the investor pays a total of 2% in additional fees, in excess of the very low fees for a global index fund and TIPS, then the investor will lose 60% of her investment gains as a result of these fees. If the total fees are 2.5%—as they commonly are—then the investor will lose more than 70%. (See the 3 Rules of Investing website for more information.)

It’s not worth it. Not only that, but you may actually get a poor education about investing. Most often it is the advisor herself who is misinformed; she has absorbed the standard advice model that is so widely accepted—illogical and wrong though it may be—and she is just passing it on.

This calls for a massive reeducation campaign—for investors, advisors, financial media representatives, and many others. If it’s effective, it will change the industry. Many financial industry participants will be paid less or will find work elsewhere. Those who remain will be able to service more clients for lower fees each, because they will be able to tell the truth that it’s all very simple and can be done inexpensively.

Perhaps this is a Utopian vision, but an industry as distorted as this one is shouldn’t be able to survive in its current form forever.

QAIB REPORTS: INVESTORS BEHAVING BADLY?

For a number of years, a company named DALBAR has been publishing a report called the “Quantitative Analysis of Investor Behavior” (QAIB). The QAIB purports to show that investors make bad decisions and, as a result, their investments underperform the market by several percentage points. This material is a godsend to financial advisors because it implies that investors need to hire an expensive financial advisor to help them. It’s also been a godsend to DALBAR. It has captured headlines for years, perpetuating the myth that individual investors invest poorly, and therefore they do much worse than the market average.

Financial advisors have been purchasing the DALBAR report in droves. The sample version of the 2013 report[2] begins with an introduction titled “The Disease of Investor Underperformance.” In the lower-right corner of every page of the sample version is an entry that says “Compliments from Advisor Name.” In other words, “Your name, Ms. or Mr. Advisor, could go here if you purchase from DALBAR the right to distribute the report to your clients and prospects.” For $1,000, advisors can buy the right to make unlimited copies for their financial advisory clients and prospects. That $1,000 is well worth it because this report will show people how badly they’ll do if they don’t have “expert” help.

There’s just one thing: the DALBAR result is wrong.

First, let’s consider: If some group of investors underper-forms the market, then there must be another group that out-performs the market. That’s simply logical, because between them all investors are the market. So to make things add up, if some investors—say, the individual investors who are the main subject of the DALBAR study—systematically underperform the market, then there must be some other group that systematically outperforms the market. The trouble is, there is no evidence of any such group. One would think that if individual investors underperform the market, then it must be professional investors who outperform the market.

But they don’t. Study after study after study, as we’ve pointed out, shows that professional investors do not, on average and in aggregate, outperform the market. So it simply can’t be true that individual investors as a group systematically underperform the market.

So why does the DALBAR study seem to show that they do? It’s because of their methodology, and the way they calculate investor performance.

A Simple Explanation for DALBAR’s Misleading Results

We’ll first explain the problem in simple terms for those who don’t care to get into the technical details. Then we’ll explain more technically for those who do want to get into it.

DALBAR measures investor returns in one way and measures mutual fund returns in another way. The measure of investor returns is meant to take into account the timing of their cash flows, while the measure of mutual fund returns doesn’t.

From 1982 to 2000, the stock market rose enormously; whereas after 2000, it flagged until recently. But over that whole time period investors—especially baby boomers—were in an accumulation phase. Therefore, they had more money in their accounts in the 2000s when their stock investments performed poorly than they had in the 1980s and 1990s when they performed very well. Hence, they lost more in actual dollars in the 2000s than they had gained in the roaring 1990s and 1980s.

DALBAR interprets this experience as terrible timing. Yes, the timing was unfortunate, but it wasn’t because investors called turns in the market wrong, consistently buying at market highs and selling at market lows as interpreters of the study claim. In fact, the financial planner Michael Kitces has pointed out that even the DALBAR study shows that if an investor had simply “invested a fixed amount in equity funds every year, the investor return would have been 3.17% a year for 20 years, compared to the actual investor return of 3.49% a year.”[3] In other words, the average investor beat an investor who merely invested a fixed amount every year.

Kitces then asks, “Why isn’t that the headline?”

Why isn’t it, indeed? Because then the result wouldn’t appear to show how badly individual investors time their investments in mutual funds, and it wouldn’t show how much they need the help of financial advisors—and how much financial advisors need the DALBAR report. The bottom line is DALBAR’s messaging deceives the investor, who pays far too much for advice that does him no good, because the report’s incorrect conclusion convinced him that he needs it.

Furthermore, it can be very difficult to tell from the wording in the DALBAR report what its methodology is. One financial planner wrote, “I contacted DALBAR recently in an effort to confirm my understanding of the finer points of their calculations.”[4] They didn’t respond. This is not atypical. Often, providers of financial research that serves as fuel for their clients’ sales pitches don’t want too-close scrutiny of their methodology.

Technical Explanation of the Flaw in the DALBAR Report

There are actually two ways to calculate the rate of return on investment: the “time-weighted” return and the “dollar-weighted” return. Time-weighted returns are returns over a specific time period if you made no contributions or withdrawals from that investment. Dollar-weighted returns account for the amounts you added or withdrew from the investments, as well as the prices of any securities transactions during the time period. Obviously, the values of these two sorts of returns can be very different from each other.

Ever since a 1968 study titled “Measuring the Investment Performance of Pension Funds for the Purpose of Inter-fund Comparison” by the Bank Administration Institute (BAI), the principal method used to measure and compare the investment performance of managed funds like mutual funds has been the time-weighted rate. The time-weighted rate isn’t affected by how much cash goes into or out of the fund. How much cash came into or out of the fund isn’t under the control of its investment managers. So the time-weighted rate of return is supposed to be a “pure” measure of the performance of the fund’s investment managers. If the fund’s investors happened to throw a lot of cash into the fund just before the market took a dip, and consequently they lost a lot on the dip, that was presumably not the fault of the investment manager. The manager has no control over how much money comes into the fund or goes out. Only investors do.

The other measure of rate of return, however—the dollar-weighted rate—does take account of when and how much cash came in and out of the fund. If a big wad of cash came in just before a market dip, the dollar-weighted return would be worse than the time-weighted rate.

Let’s take a simplified example. Suppose a mutual fund goes up 100% one year and drops 50% the next (Figure 4a). An investor invests a dollar, which doubles (increases by 100%) to $2 in the first year, but then drops by half (-50%) in the second back to $1. Hence, the rate of return is 0%.

Now let’s suppose the investor invests a dollar each year (Figure 4b). The first dollar doubles to $2 in the first year. Then $1 is added to it to make $3, but the $3 is cut in half by the -50% return in the second year to end with $1.50.

In Figure 4b, the mutual fund’s rate of return is still 0% because it went up 100% in the first year and then down 50% in the second. But the investor’s return is different. The investor invested $2, one each year, but wound up with only $1.50.

Figure 4A Time-Weighted Rate of Return and Dollar-Weighted Rate of Return

Figure 4B Time-Weighted Rate of Return and Dollar-Weighted Rate of Return

The return the investor realized—the dollar-weighted rate of return—was a compounded annual average of -17.7%. The investor underperformed the mutual fund, whose return was 0%, by 17.7%.[5]

Did the investor time the market terribly? It may have turned out that way, but not because the investor was trying to time the market. The investor was merely routinely investing a dollar a year.

Why the Dollar-Weighted Rate Has Been Worse for Investors

If the dollar-weighted rate was worse for investors in mutual funds than the time-weighted rates achieved by the funds’ managers, DALBAR concludes it was because of poor investor decisions. But that’s not what caused it. The stock market performed very well during the 1980s and 1990s, when baby boomers and others were beginning to save. But it performed poorly in the 2000s, when their cash inflows were really accumulating.

The 2012 DALBAR report[6] says, “The average equity investor underperformed the S&P 500 by 4.32% for the past 20 years on an annualized basis.” Now look at Figure 5, which shows the performance of the Standard & Poor’s 500 index, a broad measure of the U.S. stock market, for that 20-year period. It zoomed up for almost the first half, then fluctuated but went down overall in the second half.

That’s why investors’ “timing was poor”: because the timing of their pattern of saving, which they really couldn’t help, resulted in by far the most being invested during the period, the 2000s, when investment performance was worst.

FIGURE 5 Time Period over Which Investors “Underperformed”

SUMMARY OF DEADLY TEMPTATION #2

1. With conservative assumptions and all else being equal over 30 years, a 1% of assets annual fee will cost an investor a third of future real investment gains, a 2% fee will cost 60%, and a 2.5% fee will cost 70%. No global or major country market disaster has ever lost investors any of these percentages over the same time period.

2. The widely cited result that individual investors in mutual funds underperform by several percent a year because they time the market irrationally and poorly is wrong.

DEADLY TEMPTATION #3

Precisely Calibrate Your Portfolio

We told you in Rule #3 that if you hear that asset allocation is the most important investment decision you can make—and that you must do it using a scientific optimization model—you should tune it out. Now we will tell you why you should tune it out.

Financial advisors will often give you the idea that they can very precisely control the risk and return of your portfolio. Their spiel often starts with “The most important thing is asset allocation.”

Back in 1986, a paper titled “Determinants of Portfolio Performance,” by Gary Brinson, Randy Hood, and Gil Beebower, was published in the Financial Analysts Journal.[1] It got a huge amount of attention. It was cited, and re-cited, and cited again and again. Almost all of the references made to the article got its message completely wrong. It became conventional wisdom that “94% of investment performance is due to asset allocation.”

Well, that’s not what the paper said. The coauthors thought it must have been their title that threw people off—“Determinants of Portfolio Performance.” But, really, the confusion about what the paper actually said was largely due to the fact that misinterpreting it gave financial advisors a new lease on life. It offered them a new marketing pitch, which often sounded right to them but was a misconceived mish-mash of half-truths and falsehoods that they frequently passed on to clients for very substantial fees.

What the paper said was not that 94% of investment performance was due to asset allocation. It said that 94% of the variability of performance was due to asset allocation. That’s obviously not the same thing. Anyway, what’s it all about?

“ASSET ALLOCATION”

In the Brinson, Hood, and Beebower study “asset allocation” meant how an investor’s portfolio was divided among stocks, bonds, “cash equivalents,” and “other” investments. For example, the 91 pension funds they studied had an average allocation of 57.5% to common stock, 21.4% to bonds, 12.4% to cash equivalents, and 8.6% to other. Cash equivalents means short-term investments like short-term U.S. Treasury bills, commercial paper (short-term loans to corporations), and the like. Other investments could be anything that doesn’t fit into stocks, bonds, or cash—like crates of wine, art, gold, and so forth.

Stock prices are more variable—they go up and down more—than bond and cash equivalent prices. So it’s no surprise that the way in which the portfolio is divided among stocks, bonds, and cash goes a long way toward determining how variable the portfolio’s returns are. What the Brinson and colleagues’ study showed was that asset allocation among stocks, bonds, and cash explained much more of the variation in a portfolio’s returns than what the specific stocks in the stock portfolio were, or the particular bonds in the bond portfolio. In other words, you could guess right 94% of the time how variable the portfolio’s returns would be just by knowing how it was divided between stocks, bonds, and cash—without knowing what stocks or what bonds.

Somehow that simple point got conflated by the investment advisory profession (financial planners, brokers, etc.) into “You need us to help you determine the correct asset allocation because it’s the most important determinant of your investment performance.” Not only that, but the investment advisory profession also started to define asset allocation as something more complicated than what it really is. “Asset allocation” became how to divide your portfolio among several “asset classes.” These asset classes included subcatego-ries of stocks like growth stocks, value stocks, small stocks, and so on.

We’ll now explain what’s wrong with this. The main thing that’s wrong with it is that a lot of financial advisors are charging an awful lot for advice that provides no benefit to their clients.

NOBEL PRIZE-WINNING TECHNOLOGY

Let’s start with the “Nobel Prize-winning” technology that’s supposed to do the asset allocation for you. Many financial advisors use this approach. They sometimes make much of how “sophisticated” the technology is.

But here’s what it does: nothing. To explain this, we need to go back in history a few years.

Harry Markowitz’s Breakthrough

Investment risk used to be perceived as an attribute of an individual asset. A broker or financial advisor who recommended a stock to a client was held by law to be in breach of his duty if the stock was too risky for the particular client. This view changed with the publication of Harry Markowitz’s paper “Portfolio Selection” in the Journal of Finance in 1952.[2] Markowitz’s most valuable contribution was to shift that view to the risk of a whole portfolio of investments. Because individual investments can be less than perfectly correlated, adding a risky stock to a portfolio might not add to the risk of the whole portfolio, but could even reduce it. That is, it might diversify the risk of the portfolio and thereby mitigate that risk. Markowitz went on to win the Nobel Prize in Economics in 1990, which made for a great marketing “hook” for the financial industry.

In his 1952 article, Markowitz stated this in a mathematical form. To do it, he needed to attribute three theoretical mathematical properties to securities:

each security’s future expected return on investment,

the variability of that future return, that is, how much it fluctuates over time, and

the correlation between returns on two securities, that is, to what extent they move up and down together.

Using these three properties, Markowitz constructed in theory what he called the “efficient frontier” of portfolios. To create it, he needed a number for each portfolio he could call its “expected return” and a number he could call its “risk.” Technically speaking, the expected return and variability of a whole portfolio can be calculated from the properties he attributed to the individual securities in the portfolio. Hence, in Markowitz’s simplification, each portfolio of securities has two characteristics: its expected return, and its variability. Markowitz interpreted the variability of the portfolio’s return—how much it fluctuates—to be the portfolio’s risk. In theory, if you plot all portfolios’ risks versus their expected returns, you get a graph that looks like Figure 6.

FIGURE 6 Markowitz’s Efficient Frontier

Markowitz concluded that the best—the most “efficient”—portfolios were those on the curved upper line, which he called the efficient frontier. Any portfolio on the efficient frontier had a higher expected return than any other portfolio with the same level of risk, and a lower risk than any other portfolio with the same expected return. Hence, no other portfolio is superior to it in both risk and return.

Applying Markowitz’s Theory

Markowitz is a mathematician who was employed at the time at the RAND Corporation. RAND had been the innovator of important applied mathematics techniques in the field of operations research. Markowitz was now confronted with the mathematical challenge of finding out exactly which portfolios lay on the efficient frontier.

The solution method he devised involved a mathematical technique called quadratic programming (not to be confused with computer programming). This methodology excited people in the field, especially in academia, as well as newly minted graduates of academic programs in finance. They set about trying to apply the Markowitz quadratic programming algorithm to create the efficient frontier of portfolios of public stocks.

The first thing they realized was the sheer magnitude of the number of inputs that the method required. There were thousands of listed stocks. Markowitz’s algorithm required an expected return and variability for each of them, and the correlation for each pair. If portfolios were to be constructed from 1,000 stocks, the algorithm would require 501,500 inputs.

The second problem was that they needed to obtain numbers for the inputs. The obvious approach was to use historical data to estimate the inputs. So they tried using historical average returns for the expected returns, historical variabilities for the variabilities, and historical correlations for the correlations.

When those numbers were fed into the model, the results that came out were absurd. A model that was supposed to reduce risk through diversification might instead say that the efficient frontier consisted of combinations of only two stocks. The reason was that those stocks were the ones that had the highest historical returns, and therefore they had been assigned the highest expected returns; or they might be the ones with the lowest historical variabilities, and therefore they had been assigned the lowest future variabilities.

No matter what they did, the modelers found that the outputs of the model made no sense. Eventually, they employed a combination of trial and error and mathematical reverse engineering to see what inputs would produce acceptable outputs. (“Reverse engineering” means starting with a result and working backward to determine how it was produced, then using that knowledge to re-create the result.) In the end, this is precisely how the Markowitz model is used. Its inputs are laboriously rigged to produce outputs that the modeler feels are acceptable.

Oftentimes analysts find it too difficult or too much trouble to rig the inputs to produce the desired outputs. In that case, they simply force the output variables—the portfolio weightings—to come out as they wish, by imposing constraints on them. In other words, the desired outputs are specified and then the inputs and constraints are created in such a way as to force the model to produce them. In the end, the Markowitz model may be a neat formula, but trying to apply it in the real world is hopeless because there’s no way to get meaningful data to put into it, so in the end you just have to fudge it.

PORTFOLIO OPTIMIZATION AND ASSET ALLOCATION

Originally, programs that ran the Markowitz model to try to find the efficient frontier of stocks were called portfolio optimizers. They were supposed to find the optimum combination of stocks for a particular level of risk that would lie on the efficient frontier. As we’ve already seen, this proved extremely difficult.

Because running the Markowitz optimizer on a large number of individual securities is so cumbersome, and constraining it to produce acceptable results is so difficult, it is now used only for “asset allocation.” That is, the number of assets in a portfolio is reduced to a small number, such as 10 broad asset classes. For example, the asset classes may include categories like small value stocks, small growth stocks, large value stocks, large growth stocks, perhaps mid-cap stocks (stocks of mid-sized companies), and short-term, intermediate, and long-term bonds.

This exercise still requires rigging the inputs and, usually, constraining the outputs to be within acceptable ranges. Running an asset allocation on 10 asset classes still requires values for 65 input parameters. The end result has been the complete Orwellian perversion of the Markowitz model. Although financial professionals often boast that their firms use it, they don’t, really—its implementers only go through the motions, using it chiefly as a sales tool. The model has proven useful only for its basic conceptual insights, not for its practical application.

How Asset Allocation Is Actually Used

Most financial firms that offer financial advisory services perform an “asset allocation” for their clients (or prospective clients), usually presented as a very important part of their service. They let people know that a sophisticated computer model is used to create the asset allocation. Frequently, they add that it employs Nobel Prize-winning technology.

Through trial and error or other means, the investment firm has created (or outsourced the creation of) inputs to the model that produce an acceptable efficient frontier of outputs. They then ask the prospective client for her current portfolio. From that they construct her current asset allocation.

Invariably, an asset allocation on the firm’s efficient frontier will have a higher expected return for the same level of risk. That’s because, quite simply, the firm has constructed the outputs of the model—the efficient frontier—so that they have higher expected returns at each level of risk than any other portfolio. So if you input any other portfolio, it will fall below the efficient frontier. Since there are an infinite number of other portfolios, they will all seem to be “inefficient.” The result, however, is due to the arbitrary inputs they have created, not reality. The efficient frontier could be any one of an almost infinite number of arbitrary efficient frontiers, and still serve the same purpose. The model is used as a marketing device, not to solve any real-world problem.

THE HOAX THAT EVEN GOOD ADVISORS PERPETRATE

In short, the idea that asset allocation needs to be done using a sophisticated mathematical model is essentially a hoax. A lot of the time advisors don’t even know it. They buy software from some vendor and it does the asset allocation for them. The results of the asset allocation are determined by what the software provider put into the model. The results are whatever the software provider decided they should be. The model has little or nothing to do with it.

But a lot of the advisors who use it believe it does. Even the good advisors muddy up what they get right with use of the asset allocation model that seems to have become—in spite of its actual uselessness—a sine qua non of investment advising. So let’s first explain what the good advisors get right.

Passive Versus Active Investing

We’re going to make a different use of the terms “active investors” and “passive investors” than the meanings we assigned to them in Part I. There, we called “active investors” investors who invest in their own business—that is, a business they run or participate in or are employed by. “Passive investors,” by contrast, were investors who only buy the stocks or bonds of a business that they otherwise have no involvement in. All of the investors we are talking about here are passive investors in that sense.

But now we will mean something different by “passive investors” and “active investors.” Instead of picking and choosing stocks, passive equity investors just buy the whole stock market—or they buy all the stocks in some particular subcategory of the whole market. Some passive investment vehicles consist of bonds and buy a cross-section of the bond market. In contrast, active investors try to pick stocks (or bonds) that will beat the market. They do this in spite of the fact that—as we showed in Deadly Temptation #1—study after study after study shows how futile this is. That’s why more and more investors believe it’s better to invest passively at low cost to try to match the market averages, not beat them.

The best advisors know this and will tell you. Because this is now so thoroughly proven—and has been for a long time, yet active investment managers still dominate the market by cunning marketing—there is a large and growing minority of investment advisors and managers who recommend and practice passive investing.

Passive investing, actually, was originally derived from theoretical considerations, as the logical implication of Marko-witz’s theories. Markowitz’s theories—as further developed by Nobel laureates James Tobin and William F. Sharpe—implied that the best thing you could do as a stock market investor is diversify to the maximum. Diversifying to the maximum meant buying the whole market. That’s why we recommend just buying a world stock index fund.

If you buy the whole stock market, all you have to do is hold onto it, because it will always be the whole stock market. You don’t have to do any buying and selling—or hardly any, mainly only when a company pays a dividend that has to be reinvested, or when a new company goes public. If you own a world stock ETF, all that gets done for you by the ETF’s manager—and for a very low fee.

Feeding the Fee Machine

However, if you’re an advisor and you simply recommend to your clients that to invest in stocks they should just buy a single mutual fund or ETF—a total stock market fund—they’ll think you’re not doing much for them. They’ll think, “Well, what do I need you for? I could’ve done that myself.”

If you’re an index fund investor whose advisor has done an asset allocation for you, he won’t recommend just investing in one equity fund—a total market index fund. There’s not much asset allocation to do if you only invest in one equity fund. He’ll recommend that you invest in several asset groups that are sometimes called styles: for example, a large growth index fund, a large value index fund, a small growth index fund, and a small value index fund. And you run your asset allocation program among those—plus some bond categories.

And what does your asset allocator spit out? Almost always, the asset allocator will combine all the stock fund styles in a mix that nearly replicates—guess what?—a total market index fund. So instead of buying one total market index fund, you’ll be buying four to six style index funds that together make up a total market index fund.

What’s wrong with this? Well, several things.

First, the style index funds tend to charge more than a total market index fund. For one thing, they have to keep track of which stocks are in the style category. A “large value” fund is a fund that holds stocks in companies that are bigger than average and are trading for lower prices than you might expect given their fixed assets. To know which stocks to put in it and to keep in it, you have to do some data screening and know when a stock moves in or out of the category. This is a more “specialized” field of investing, supposedly, than just running a total market index fund, so the fund manager probably charges at least a little extra.

But there’s more wrong—much more. Because stocks move in and out of the large value category, you have to buy and sell more often than a total market index fund buys and sells. And to whom do you sell and from whom do you buy? Why, you sell to or buy from someone who runs one of your other sector index funds. For example, if a large value company stock’s market-to-book value ratio (a measure of its price compared to its fixed assets) goes too high to be in the large value category anymore, and thus becomes a large growth stock, then you can sell it to the manager of a large growth index fund. So your large value manager is going to be selling that stock to your large growth manager. There’s no change in your portfolio, but you have to pay the two-way transaction cost.

That’s kind of pointless, isn’t it? And it adds to the cost. So the asset allocation hoax not only doesn’t benefit you but also costs you something.

But it costs you more than that, because it’s one of the main things that you supposedly need to pay your advisor for. And your advisor, even if your advisor is saving you a bundle by recommending that you invest in low-cost passive index funds instead of high-cost actively managed funds, is still charging you a bundle himself. A typical “fee-only” advisor charges 1% of assets. We saw in Deadly Temptation #2 what 1% of assets in fees can do to your investments. And when you combine that with the little extra that is charged for style index funds, your investment gains may wind up being cut in half.

Yet all the conventional wisdom says how important asset allocation is, and it all comes from misinterpreting that 1986 Financial Analysts Journal article and misapplying Marko-witz’s portfolio optimization theory.

SUMMARY OF DEADLY TEMPTATION #3

1. A 1986 article in the Financial Analysts Journal said that asset allocation explains 94% of the variation of returns, not 94% of portfolio returns.

2. “Nobel Prize-winning” optimization technology can be applied to asset allocation only by jury-rigging the inputs to create acceptable outputs.

3. The end result of the theories of Nobel laureates in finance is that the most efficient portfolio is one that mirrors the whole market, a total market index fund.

DEADLY TEMPTATION #4

Sleep Well at Night with Your Risk Under Control

Standard financial advice often includes a few misguided prescriptions that are intended to help control your investment risk. We’ll now explore some of these, and the reasons they’re misguided. We told you that if you hear that you must diversify among many mutual funds or ETFs and asset categories, you should tune it out. Now we will tell you why you should tune it out.

WHAT MODERN PORTFOLIO THEORY ACTUALLY SAYS ABOUT DIVERSIFICATION

The advice to be diversified comes from Nobel laureate Harry Markowitz’s 1952 paper. Markowitz, as we learned in Deadly Temptation #3, showed that if you diversify by buying a lot of stocks instead of only a few, then some of the stocks’ price fluctuations tend to cancel out, and you’ll have a portfolio that fluctuates less without reducing its expected return. As we explained before, reducing your portfolio’s fluctuations has been interpreted by the modern portfolio theory (MPT) folks as meaning the same thing as reducing your risk. It’s not really the same, though; portfolio fluctuation risk is not the risk that matters—the real risk is running out of money when you need it.

Markowitz’s efficient frontier assumed you’d get more expected return if you took more risk. Nobel laureate William F. Sharpe went further, by trying to quantify how much extra return you’d get by taking more risk (i.e., more variability of returns). But he showed that you would only get more return for that risk if you were completely diversified. If some of that variability weren’t diversified away, you wouldn’t be rewarded for it with a higher expected return.

As this theory developed, it wasn’t too long before people—theoreticians, initially—were starting to think about index funds, because they were as diversified as you could get. The common impression is that index funds were created because they are low in cost; but, in fact, they were first created because theory implied that a total market index fund was the most efficient portfolio possible. This perspective can be extended not only to stocks but to bonds, too, and even to real estate and other assets, creating a total market portfolio of all marketable assets.

There is no support in MPT for a procedure that some advisors now recommend—namely, slicing the equity market into styles such as small value, mid-cap growth, and so forth—and allocating to a fund for each style. This approach can only detract, in theory, from the efficiency of a single total equity market index fund. Moreover, as we explained in Deadly Temptation #3, it will add to the fees and transaction costs. It merely creates a perception of complexity and sophistication in a process that could be done much more simply.

There’s nothing wrong with diversification—it’s generally a good idea. What’s wrong is the way the concept can sometimes be distorted—confusing investors, complicating matters, and helping only to sell more high-priced products and services. Let’s look at some examples.

THE MUTUAL FUND DIVERSIFICATION MYTH

If you combine all stocks, you’ll eliminate the volatile movements that are characteristic of each stock alone—sometimes called that stock’s “idiosyncratic” volatility. You’ll have left only the volatile movements of the whole market. So it’s a good idea to hold a combination of all stocks.

Does it follow that it’s also a good idea to combine combinations of stocks—to combine mutual funds? No, not necessarily. It depends on what’s in those mutual funds. If they all contain the same stocks, what’s the use of combining them? You’ll still have the same basket of stocks as if you’d only bought one fund. It wouldn’t diversify you to combine several funds.

So would it help you to diversify if you bought 25 mutual funds? If you analyzed the stock holdings of most combinations of 25 equity mutual funds, they would tend to be very close to the whole equity market portfolio. Nevertheless, that’s what a lot of financial advisors recommend—25 mutual funds. They recommend it on the principle of “diversification,” as if diversification among equity mutual funds were the same thing as diversification among stocks.

Very often, if you analyze the basket of stocks held by the combination of 25 mutual funds that a specific advisor recommends—which can be done using, for example, the mutual fund evaluation company Morningstar’s “Portfolio X-Ray” program—you’ll find that what you’ve got is a total market index portfolio, but for much, much higher fees.

Why does an advisor recommend such a travesty of an index portfolio, sometimes called a “closet index” portfolio? Two reasons, one of them understandable and one very bad. First, the advisor may not realize that holding mutual funds in several classes is very similar to holding an index fund. Yet this has been the norm of the industry for years. In fact, for over three years, one of the authors of this book worked on asset allocation and manager/index/ETF selection for clients; and for a long while, he believed that he was doing the right thing for his clients.

The second reason a financial advisor might recommend multiple active mutual funds is the higher fees associated with it. Some advisors (though not those known as “fee-only” advisors) get a share of the fees paid to the funds they recommend—a kickback.

Mutual fund fees often have breakpoints at which the expense ratios go down. For example, some mutual funds charge one expense ratio for investments up to a breakpoint of perhaps $25,000, or $100,000, but a lower expense ratio for investments over the breakpoint. If an advisor recommends a large number of funds, the dollar amount in each fund may be below the breakpoint for lower fees, resulting in higher fees overall. This is one way (not a very pretty way, to say the least) to jack up the fees.

THE MYTH OF SCIENTIFIC “STYLE” OR “ASSET CLASS” DIVERSIFICATION

The “style” or “asset class” diversification myth would not be all that bad except that sometimes it provides support for the mutual fund diversification myth—and it’s also related to the myth of mathematically optimized asset allocation. The idea is that to be diversified in your equity portfolio, you have to diversify among several equity styles or asset classes with names like large growth, small growth, large value, small value, and mid-cap.

These are all subclasses of the whole set of stocks. If you buy a fund that concentrates on each of these styles, together your funds are fairly likely to approximate the whole stock market—in other words, they will add up to a closet index fund. However, the fees will be higher than if you bought only one total market index fund. Furthermore—as we explained earlier—the trading costs will be higher.

The only reason to buy a style fund would be if you wanted to “tilt” toward that style—if you believed, for example, that small-value stocks will do better than the market, so you wanted to overweight them. This is a form of active management, because it believes that a particular asset class will beat the market as a whole—usually because it beat the market in the past. But the Schwert rule suggests that there is no reason to believe such an anomaly will persist.

If, however, that’s what you believe, and you really want to follow that belief—and if you only want to “tilt” your portfolio in that direction—the best way to do that is to buy the total market index fund with most of your portfolio, then buy a small value index fund with the rest. That will overweight you in small value stocks more efficiently than distributing all your purchases among many style funds.

THE HEDGE FUND DIVERSIFICATION MYTH

One of the ways hedge funds are sold is as “diversification” for an investor’s portfolio. Hedge funds sometimes lay claim to diversification value through the very names of their funds. For example, “absolute return” is the name of a whole so-called asset class of hedge funds. Absolute return funds were meant to provide a return that wasn’t correlated with the stock market—that is, a return that wouldn’t go up when the market as a whole went up, and down when the market as a whole went down. They were supposed to provide an “absolute” return instead of a return that was “relative” to the fluctuations of the market. Their name implied the promise that they would go up in value whether the stock market went up or down.

Funny, they didn’t turn out that way. Absolute return funds were among the worst-performing hedge funds when the market as a whole plummeted during the financial crisis.[1] But they managed to get sold on their name, anyway.

Let’s take another kind of hedge fund sold as diversification. One category of hedge funds is called “long-short equity.” This was the type of hedge fund pioneered by hedge fund manager Alfred Winslow Jones, whom we will encounter in Deadly Temptation #6. In a long-short equity fund, the idea is to buy stocks you think will go up, and “short” stocks—that is, sell them—that you think will go down. If you’re right (a very, very, very big if!), you make money whether the market as a whole goes up or down.

Oddly, most long-short equity hedge funds don’t do it in equal amounts; typical is 70% long and 30% short. So they’re—on balance—“long” the market; that is, they’ll tend to fluctuate with the market. No matter: if they get the buys and sells right, the fund will do well, of course.

But there’s a big problem with the idea right out of the box. Mutual funds have shown quite clearly that professional investment managers don’t get it right. Mutual funds can only buy stocks or sell stocks that they own—they’re not allowed to short them—but as we showed in Deadly Temptation #1, they’ve proven from their record that they don’t get the buys and sells right any better than the proverbial chimpanzee throwing darts at the stock page of the Wall Street Journal.

Even though mutual fund managers can’t short stocks, if there’s a stock they don’t like, they could just not buy it and buy more of a stock they like instead. If they really knew which stocks were going to go down, they could easily beat the market that way. But they don’t. Their record of failing to beat a market index shows that they don’t know that.

So why would a long-short equity hedge fund know it if mutual funds don’t? The hedge funds’ argument is that before becoming hedge fund managers, these financial services pros were the best of the breed of mutual fund managers. They quit to start hedge funds because they have more investment freedom with unregulated hedge funds, and they’ll be paid more. Therefore, they say, because they’re the best managers and they’ll be freer to pursue their strategies, they’ll do much better than the average mutual fund manager.

But if they were previously mutual fund managers—and it’s true that many hundreds, even thousands of them have quit to start or to work for hedge funds—and if they were so good, how come they couldn’t raise the mutual fund performance average so it was at least better than the average of untrained individual investors? The argument simply doesn’t add up.

But it gets worse than that. Let’s just stipulate for a moment that maybe a long-short equity hedge fund might possibly give you a better-than-market return even after its ginormous fees. That, of course, wasn’t the sales pitch, though; the sales pitch was that it offers diversification.

If you’re getting into this hedge fund to diversify your investments, that must mean that you have other investments, too, that you want to diversify—among them equity investments. In fact, those other equity investments probably own some of those same stocks that this long-short equity hedge fund sells short.

You know, there’s a better way to do this. If one of your mutual funds (or maybe even one of your hedge funds) bought a stock, and then you invested in another fund, a hedge fund that shorts the stock, you could achieve the same objective by just not buying the stock. The effect of the diversification the hedge fund was supposed to give you is that in your combined portfolio you don’t have the stock because the buys and sells washed out. Yet you’re paying two different managers high fees, one to buy it and one to sell it.

What kind of ridiculous diversification is this? But that’s what actually happens.

THE TOTALLY MIS-SOLD TARGET-DATE FUNDS

Another effort to provide risk control, specifically to individual investors, has been offered by so-called target-date funds. At some point, target-date funds became very popular in the United States for people investing in their 401(k)s (though they might have been making a mistake investing in their 401(k)s in the first place, as we’ll point out in Deadly Temptation #5).

Target-date funds became popular because they can be used as the “default option” in a 401(k). If an employee who invests in a 401(k) has a choice of investment options but doesn’t choose one, the fund administrator can—by default—place her investments in a target-date fund. Target-date fund managers loved this because they wound up with all the investors who didn’t know what to do, which was most of them. And, of course, they charged significant fees for their target-date fund “expertise.”

Target-date funds are certainly not inherently harmful. But they suffer from the usual two problems: they don’t really do much for you, and they provide an excuse for higher fees. So let’s summarize these two points, which we’ll explain later:

Target-date fund strategies are so simple that anyone familiar with the websites of their mutual fund providers could execute the strategy on their own in a time measurable in minutes per year; so there is little reason for target-date funds to charge higher fees.

Every target-date strategy is equivalent to an even simpler constant-mix strategy; that is, a constant-mix strategy will produce the same probabilities of the same levels of wealth at a given future date, such as retirement.

The Target-Date Fund Principle

The idea of a target-date fund is that you want to take less investment risk as you age. When you’re young, you can invest in risky stuff—stocks, basically—because there will be plenty of time for things to work out. The assumption is that stocks can be very volatile in the short run, so if you invest in them for a short time period like a year or two, you could lose a lot (but you could gain a lot, too); but in the long run, their fluctuations will even out and you’ll have a high probability of gaining.

When you’re older and getting ready to retire (or maybe you’re already retired), you don’t want to take much risk. And you don’t have as long-term a time frame left to invest, so maybe stocks aren’t such a good idea anymore.

The idea of a target-date fund is very simple: invest a higher percentage in stocks when you’re young than when you’re old. One popular formula is that your asset allocation should be “100 minus your age”—that is, your allocation to stocks should be 100% minus your age, and the rest should be in bonds. For example, if you’re 30 years old, you should allocate 70% of your portfolio to stocks; if you’re 40, you should allocate 60%; and if you’re 60 years old, you should allocate only 40%.

This is awfully easy to do. If you invest in stocks by investing in only one world stock index fund, as you really should—or perhaps only one total U.S. stock index fund and one international stock index fund—and one bond fund (or one domestic and one international), you could do it yourself online, in a few minutes a year. (Well, that’s if you’ve already figured out how to navigate the funds’ website.)

As investing formulas go, this is certainly not terrible. It has the virtue of being simple. You could do it yourself easily. Marketers of target-date funds came up with a name for this formula for winding down your asset allocation—they call it the “glide path.” This creates the impression of coming in for a safe landing. It gives people the feeling that their risk is under control. It’s great for selling the funds.

There are, however, lots of other simple formulas that would be just as good and are easy to execute yourself. In fact, purveyors of target-date funds are all over the map on what strategy a target-date fund should use. Some of them wind down your allocation to stocks to almost zero when you’re 65; others wind you down only to about 60%.

The ones that did the latter got into big trouble in the financial crisis of 2007-2009. Investors who were invested in a “target-date” fund that they thought would protect them in their retirement, and who were close to retirement, and who were all nice and ready to coast down the runway of life on their glide path, suddenly saw their target-date fund lose 20% or 30% or even 40% of its value. So, after the financial crisis, a lot was written about the “failure” of target-date funds. But the failure was not of the funds—it was the failure of the marketers of the funds to make clear to investors (and to financial journalists) just what the funds’ risks were, and what the funds’ objectives were. Instead, they left investors (and journalists) with the impression that target-date funds protect you against risk when you’re almost retired. (The glide-path meme helped them do that.) After the crisis—after the fact—they backtracked to explain that significant risk taking is a good policy even when you’re at retirement age.

One Wall Street Journal article[2] on target date funds’ problems during the financial crisis said, “Of course, the selling point of target-date funds is their simplicity: Put your money into one fund based on when you expect to leave the work force, and the experts will take care of the rest.” Experts? What expertise? Target-date funds do the simplest thing in the world. Any idiot could do it—in a few minutes a year, as we said—but most people don’t want to be bothered. But to be led to believe they’re letting “the experts” take care of it? It’s hogwash! The trouble, of course, with believing that kind of bunk is that you allow people who present themselves as “experts” to charge you a huge bundle of money for what is, at worst, snake oil and, at best, a trivial clerical task that you could easily do yourself.

Target-Date Versus Constant-Mix Strategies

No evidence exists that a target-date strategy will ensure you of any specific level of retirement income—certainly not as well as TIPS and annuities do. In fact, the so-called target-date strategy of reducing your allocation to stocks over time, in accordance with a formula, can be shown to give exactly the same likelihood of the same results as keeping your allocation constant.

Suppose, for example, that you’re using the “100 minus your age” formula, and suppose you contribute 10% of your salary a year to your fund from age 25 to age 64. At age 25 your allocation will be 75% stocks/25% bonds. At age 26, it will be 74% stocks/26% bonds, and so forth, until at age 64, it will be 36% stocks/64% bonds. How will your results in retirement with the target-date fund compare with the results you would get if you just used a constant mix?

The answer: the results will be exactly the same. There is a constant mix (in this case 49% stocks/51% bonds) that gives you exactly the same chances of various levels of wealth accumulation at age 64 as the changing mix of the target-date fund.

For example, using reasonable assumptions, Table 3 shows the probabilities of various levels of wealth accumulation at age 65 with the target-date strategy and with an equivalent constant-mix strategy. The table shows that with both strategies, there is a 50-50 chance of accumulating at least $887,000, a 10% chance of accumulating less than about $640,000, a 1% chance of accumulating less than about $500,000, a 10% chance of accumulating more than $1,250,000, and less than a 0.1% chance of falling below $400,000.[3]

TABLE 3 Comparison of Probabilities of Various Levels of Wealth Accumulation at Age 65 Using Constant Mix and Target-Date Strategies

Percentile

Wealth accumulation with

constant mix

Wealth accumulation with

target-date strategy

1

$1,705,563

$1,710,120

2

$1,590,609

$1,588,872

5

$1,412,162

$1,392,903

10

$1,267,729

$1,255,064

20

$1,120,273

$1,110,014

30

$1,020,153

$1,017,868

40

$949,755

$946,749

50

$887,347

$887,581

60

$830,320

$831,646

70

$772,303

$778,793

80

$710,653

$718,674

90

$635,768

$649,575

95

$581,212

$601,623

98

$527,340

$550,373

99

$496,664

$524,279

99.9

$421,598

$448,336

Source: Study by Michael Edesess.

In short, there’s hardly any difference between the two. So how, exactly, does the target-date fund control your long-term risk, compared to the constant-mix strategy? The answer is it doesn’t. Target-date funds neither improve nor guarantee people’s retirement lifestyles, any more than a number of other arbitrary strategies.

Of course, sellers of these funds don’t make any of that clear—and in truth, much of this may not be known to them. All they have to do is leave the impression that “the experts will take care of the rest,” as the Wall Street Journal reporter wrote. And people believe it—especially when a financial journalist, one who is not sufficiently skeptical of what he is told and doesn’t have time to investigate deeply, says it, too, and in the Wall Street Journal, no less.

The problem is that if you buy into the idea that you need an “expert” to do something for you that is so simple, and if you pay that expert 1% a year to do it, suddenly something that is just a harmless rule of thumb becomes toxic to your finances, depleting your assets by an amount—as we showed in Deadly Temptation #2—that could range from one-third to three-fourths of your investment gains.

SUMMARY OF DEADLY TEMPTATION #4

1. Diversification among many mutual funds or fund sectors is often only “closet indexing”; it is a more inefficient and costly way to diversify across stocks than a total market index fund.

2. Hedge fund diversification is in most cases a myth. Hedge funds charge extremely high fees and will often merely duplicate or cancel out other holdings in the investor’s portfolio.

3. Target-date funds follow a very simple rule of thumb that does not necessarily benefit an investor but may be comforting; it is harmful, however, if it entails higher fees. Target-date investing also offers no greater risk reduction compared with a constant-mix strategy.

DEADLY TEMPTATION #5

Use the Strategies That Always Work

There are some things about investing you hear so incessantly that by now everybody just assumes they’re right. The conventional wisdom is so riddled with error that you should question virtually everything you hear. In this Deadly Temptation, we’ll address the following four things you hear that are either wrong or at least not always right:

The first thing you should always do is fund your 401(k) to the max.

You must be sure to rebalance your investments regularly.

Dollar-cost averaging always wins.

Gold is a hedge against inflation.

FUNDING YOUR 401(K) TO THE MAX—OR NOT

What?! Are we going to say it’s wrong to fund your 401(k) to the max? You may find this hard to believe, but yes, that’s what we’re going to say.

Now, it’s not always wrong, but it’s often the wrong thing to do, and it may be wrong more than half the time. So you need to think about it. You shouldn’t do it automatically.

To decide whether you should fund your 401(k) to the max, you need some information. Unfortunately, it may be extremely difficult to get the information you need. You may have to do a lot of demanding to get it, and even then you might not.

Back to Basics: Transferring the Risk from the Employer to the Employee

We need to provide a few basics about 401(k) plans. This discussion applies only to U.S. residents and employees. Other countries have similar plans, but everything could be different.

A 401(k) is provided as a retirement benefit to U.S. employees by their employer. Until the 401(k) plan was invented in 1980, most employee benefit funds were defined-benefit pension plans. That meant the payments employees would get when they retired were determined once and for all, usually as a percentage of their average salary in their last few years of employment. In a pension plan, it was up to the employer to make sure the employees got those payments. Employers had to both put money into the pension fund and hope to get a good return on its investments. If the fund didn’t earn a good enough return on investment, the employer had to put more of its own money into the fund to make up for that.

The 401(k) plan, instead of being a defined-benefit plan, is a defined-contribution plan. Instead of the amount of the future retirement benefit being defined once and for all, the contribution to the fund that both employee and employer make is defined. How much you, the employee, earn later is uncertain. It depends on the return on investment your 401(k) plan earns. This arrangement transferred the investment risk from employer to employee. Employees now bore the risk of not getting as much later as they hoped, if the fund’s returns didn’t turn out well.

Not only did the employer slough off the risk of committing to specific amounts of retirement benefits, but the employer had to contribute less to the fund. The contribution became the employees’ responsibility. Most employers made up partially for the loss of their burden by matching employees’ contributions to their 401(k) plans up to a certain amount.

Now, you’d think employees would be unhappy with suddenly getting stuck with all the investment risk. But remember when the 401(k) was invented. It was right at the dawn of one of the longest bull markets ever. In the 1980s and 1990s, investment returns were spectacular. Employees actually thought they were getting a bad deal with pension funds because they defined way ahead of time what they were going to get in retirement. They didn’t allow the employees to cash in on the great investment returns they heard about constantly. They wanted a piece of those. The 401(k)—where they could invest their own money and reap the high returns—seemed to give it to them.

Well, yes, until it didn’t, of course.

Rules of the Game

Let’s begin by summarizing the surprising result: a lot of the time—for perhaps about half of all 401(k) plans—you should not contribute as much as you can to your 401(k) plan. Let us explain.

Both employer and employee can contribute to the 401(k) plan. The U.S. government allows the employee to deduct the amount of her contribution from her taxable earnings, up to a maximum contribution, which was $17,500 in 2013 and 2014 (or $23,000 if you’re over age 50). This tax advantage is the main reason that financial advisors, and virtually all the advice you’ll hear, recommend that before you make any other investment, you should contribute as much as possible to your 401(k)—the maximum, if you can possibly afford it.

In addition to the employee contribution, the employer often provides a matching contribution. Usually, if the employee contributes nothing, the employer contributes nothing, either. But if the employee contributes $1,000, the employer will match that by contributing a percentage of it to the employee’s plan also. The website 401khelpcenter.com says that 27% of employers match employees’ contributions dollar for dollar up to a specified percentage of the employee’s pay, usually 6%; and 23% of employers match employees’ contributions 50 cents on the dollar up to a percentage of pay.

So if you have a 401(k) plan and your salary is $50,000 and your employer matches your contribution up to 6% of pay, then the maximum the employer will contribute in a year is 6% of $50,000, or $3,000. If your employer matches you dollar for dollar, then if you contribute $3,000 your employer will contribute $3,000. If your employer matches you 50 cents on the dollar, then you need to contribute $6,000 for your employer to contribute $3,000. If your salary is $100,000, your employer will match your contribution up to $6,000 instead of $3,000.

Yes, you should probably contribute to your 401(k) at least up to the point where your employer contributes the maximum. You could still go wrong with that, but it’s a good rule. But should you contribute more than that—up to the maximum that’s tax-deductible? Ninety-nine plus percent of advisors will say, of course you should: it’s tax-deductible! What could possibly be wrong with that advice?

You need to ask, “OK, I get a great tax benefit if I contribute to my 401(k) to the max, but am I giving anything up?” Yes, you could be giving up a lot. You’re giving up fees to the plan’s provider and its investment managers. Some 401(k) plan providers don’t charge too much. If your plan has one of those, then you should contribute to the max. But if the provider is not low-cost, you could be getting ripped off, and it could be for more than you gain from tax deferral.

Unfortunately, you may have a very hard time, with some plans, finding out how much you’re paying. Nobody seems to ask this important question, so even the employee benefits administrator at your company may not know. If you try but just can’t find out the total fees you’re paying, it’s a red flag. It means the plan provider doesn’t want to answer. It’s 99% certain that the fees are too high. If this happens, don’t contribute any more than the amount required to get the maximum employer match.

It may sound crazy, but if you can save more than enough to get the employer match, just pay the tax on the rest. Then skip the 401(k) and invest it in the lowest-cost, best-diversified, or lowest-risk investment available—a total stock market ETF or TIPS, or a combination of the two, as noted in Rule #2. You’ll be better off.

How Much of a Fee Makes It Not Worth It to Contribute?

Suppose you actually are able to find out what your total 401(k) fees are. If you are able to find out, how high can they be before you’ll be better off not to contribute?

Suppose you’re thinking of contributing $10,000 to your 401(k) plan and your tax rate is 20%; but it will be lower after retirement, 15%. Suppose you will withdraw your money in 30 years and your investments will earn 5% until then, of which 2.5% is dividends and income. If the fees in your 401(k) are 1.5%, you will earn 3.5% a year after fees—the 5% return less 1.5% fees.

If you don’t contribute the $10,000 to your 401(k) but take it in salary instead, you’ll have to pay $2,000 tax. That leaves you with only $8,000 to invest. But now you can invest it in low-cost index ETFs with fees of only about 0.2% or less because you don’t have to pay the 401(k) fees. Then here’s the answer:

Invested in your 401(k), you will have $23,858 in 30 years.

If you pay the tax and then invest outside your 401(k), you will have $27,933 in 30 years.

Hence, you’ll have $4,075—or 16% more—if you don’t put the money in the 401(k) than if you do.

You can do the calculation yourself if you’re adept with spreadsheet arithmetic. You should get a similar result. Our spreadsheet is available at the 3 Rules of Investing website.

Like all such calculations, the results depend on the numbers you put into them—the assumptions. It depends on the tax rates, fees, future return, and the portion of it that is income. But given the assumptions we’ve made in our example, we can calculate that the 401(k) fees would have to come down from 1.5% to 0.95% before it’s worth it for you to contribute to it.

According to the Society for Human Resource Management (SHRM), the average total plan cost for small 401(k) plans was 1.46% in 2012.[1] Of this, the average expense ratio of the investment vehicles offered by the plans was a high 1.37%. (Note that this does not include transaction costs in the funds—mostly brokerage commissions—which could be as much as an additional half a percent if turnover is high.) Hence, in small plans it will usually not make sense to contribute more than your employer matches. Even in large plans, SHRM says the average cost is 1.03%; so for about half of those it will not make sense to contribute the maximum but only what your employer will match.

SHRM gave only the average costs. In each plan, there may be a low-cost investment alternative. If there is a fund with much lower costs, it may make sense to invest in your 401(k). But the advice that you should “always contribute as much as you can to your 401(k)” is simply wrong.

THE OFF-KILTER ADVICE TO REBALANCE REGULARLY

Now let’s take up the myth of rebalancing. Everybody, but everybody, says you must rebalance your portfolio. The funny thing is, nobody ever really explains why.

In a way, the rebalancing myth is the illegitimate daughter of asset allocation. Asset allocation is something your advisor does when you start out. Let’s keep asset allocation simple: Suppose you start with an allocation of 60% to stocks and 40% to bonds. Your portfolio is $100,000, so you put $60,000 in stocks and $40,000 in bonds. A year passes. It’s a good year for stocks. At the end of it you have $75,000 in stocks and $42,000 in bonds. Your portfolio is now 64.1% stocks and 35.9% bonds. Should you do anything?

Oddly, the financial experts who put so much effort into trying to make the Markowitz optimizer work for asset allocation—and then just fudged the whole thing in the end by plugging in whatever numbers made it work (as we discovered in Deadly Temptation #3)—seem not to have thought about that. They didn’t address at the same time what you would do next, say, a year later. They were too busy optimizing—or trying to.

So what did they do when it dawned on them that you have to decide again later what your asset allocation should be? They just assumed it should be the same as the first time you ran the algorithm. In other words, they decided you needed to “rebalance” to the original allocation. Hence, if your portfolio changes over the year so that it has 64.1% in stocks and 35.9% in bonds, when your original allocation was 60% in stocks and 40% in bonds, that means you need to sell some stocks and buy some bonds to get back to the 60/40 mix. But why?

Curiously, this question is never answered. All the academic literature about rebalancing simply assumes you should rebalance—it never, ever explains why. For example, here is a quote from an article titled “Rebalancing” in the Journal of Portfolio Management:[2]

In general, investors have a target asset allocation that they seek to maintain. When a portfolio’s actual allocation deviates from its target allocation, the result will be tracking error compared to the target allocation. The key benefit of rebalancing is to reduce this tracking error.

The “tracking error” referred to is the error of departing from the 60/40 mix—or whatever allocation was originally decided on. But the article doesn’t explain why allowing the portfolio to depart over time from that mix is an “error,” or what “benefit” reducing this error provides. In fact, it doesn’t even make an effort to explain it; it just declares it. Yet the article is titled “Rebalancing”—you’d think it would say something about why you would do it in the first place. When academicians, for example, are asked to explain what objective is served by rebalancing, they’ll usually say it’s to keep your risk under control—to keep it “constant.” But it doesn’t keep risk under control—in fact, it can destroy your control over that risk.

How Rebalancing Can Destroy Your Control over Your Investment Risk

Here’s an example. One way to control risk is to put a portion of your portfolio into something very safe. Let’s suppose, for example, that you’ve just retired and you’ve done pretty well in your saving and investing: you have a million dollars.

Here is how you perceive risk. Let’s say that on top of your Social Security payments, you feel you must have a bare minimum of $20,000 in income a year, and you must leave at least half a million dollars to your children in 30 years. You refuse to take any risk that might endanger those bare minimum requirements.

Fortunately, you don’t have to. When you retire, lo and behold, there are 30-year U.S. Treasury bonds available that pay 4% interest. If you buy $500,000 of those they’ll pay you $20,000 a year in interest, and you’ll get your $500,000 back in 30 years to leave to your children. These Treasury bonds completely eliminate the risk, except the risk that the U.S. government won’t make good on its guarantee, which you consider negligible. And only half your money—$500,000 out of your $1 million—is needed to secure your minimum requirements. You can invest the other half in something more risky. Therefore, your strategy is to purchase $500,000 worth of U.S. Treasury bonds and take some risk with the other $500,000 by putting it in stocks. Your portfolio now consists of 50% stocks and 50% Treasury bonds.

The first year, the stock market has a bad year and loses 20%. Your stock portfolio goes down in value to $400,000. Meanwhile, you collect the $20,000 interest payment on the bonds, and they’re still worth $500,000.

Now your total portfolio is worth only $900,000. Fortunately, though, you still have the guarantee of at least $20,000 in interest income a year from the bonds and a $500,000 principal payment at the end. Those bonds serve as security against risk, cushioning you against the severe ups and downs that can take place in the stock market.

But your advisor—if you have one—tells you that you must rebalance. Now your stock portfolio is only 44.4% stocks and 55.6% bonds. So you must sell some bonds in order to buy some stocks and restore the 50/50 mix. Perhaps he says you must do this to keep your risk level constant. The thing is, it won’t keep your risk level constant—it will increase your risk. In fact, it will destroy your carefully laid risk avoidance strategy. You will have less Treasury bonds than you bought, so you will no longer be assured $20,000 annual income or a $500,000 principal payment when the bonds mature. You will be more exposed to stock market risk than you were, and more than you wanted to be. Not only that, but if the stock market goes up substantially, you will do much worse with the rebalancing strategy than by holding the Treasuries and the stocks without rebalancing. That’s because you will have more in stocks on average if you don’t rebalance than if you do.

The problem is that the “sophisticated” algorithms of modern portfolio theory don’t think beyond the immediate short-term time period. Hence, they define risk only as how much your portfolio could lose—or could fluctuate—in the very short term. This is like thinking of the risk of flying in a plane as if the risk were only how much the plane might vibrate in turbulence, instead of how likely it is to crash.

Will Rebalancing Increase Your Return on Investment?

Some illustrations suggest you’ll increase your return on investment by rebalancing. This can happen, of course, over certain time periods. But over other time periods—most, in fact—it won’t. There’s no mathematical or theoretical reason that rebalancing should increase your return in the long run or lower your risk. It’s strange that theorists who otherwise expend enormous amounts of effort proving in mathematical detail certain precepts of MPT, expend no effort on advancing a theoretical reason for rebalancing.

Rebalancing is merely an ad hoc device to connect other shaky parts of the MPT structure. The fact that the whole structure is so shaky doesn’t seem to be noticed by either the theorists or the practitioners. Everybody agrees that the structure is sophisticated—so that’s what it is.

In fact, history shows that rebalancing would have performed worse over long time periods than the obvious alternative: buy-and-hold. Rebalancing would have performed poorly compared with not rebalancing—in other words, doing nothing. For example, if a portfolio started out with 50% stocks and 50% bonds in each of the 58 30-year periods from 1926 to 2012, a rebalancing strategy would have beaten buy-and-hold in less than 12% of the 30-year periods, and for only two of those 58 30-year periods—3% of them—by more than 0.2%. The buy-and-hold strategy would have beaten rebalancing by an average of 0.8% per year. (For more information, see the 3 Rules of Investing website.)

The reason is that over long time periods, stocks have tended almost always to outperform bonds. That means that if you don’t rebalance, stocks will gradually become a larger percentage of the portfolio than bonds. But since stocks perform better in the long run, your portfolio will do better if more of it is in stocks. Hence, not rebalancing will outperform rebalancing. This point is easily shown to have been true historically (see the 3 Rules of Investing website for details).

What about performance on a risk-adjusted basis? That’s more difficult to measure; however, the theoretical result—that is, the result derived from MPT itself—would be that risk-adjusted performance will be the same whether you rebalance or not.

But if you don’t rebalance, doesn’t that mean you’ll wind up taking more risk than you intended to? From the example we gave earlier, the answer is obviously not. You should employ a reasoned-through long-run risk avoidance strategy, not the ad hoc strategy of rebalancing.

There’s nothing wrong with rebalancing per se. It’s one strategy you could use. It’s good to have some kind of discipline, and rebalancing could be a candidate. Of course, there’s something seriously wrong with rebalancing if it conflicts with another, perfectly good discipline that you’ve chosen—like the one in our example.

What’s wrong is to believe there’s some reason you must do it. It’s OK to do it if you think you need to impose a discipline on yourself so you won’t do stupid things on the spur of the moment—like putting all your money in a stock your “gut” tells you is going to take off. What’s really, really wrong is to believe that investment professionals possess some sophisticated set of knowledge that explains why they know that you must always rebalance. If you make that mistake, you’ll pay them for what you think they know—and lose a gigantic chunk of your money on their fees.

WHAT DOLLAR-COST AVERAGING CAN AND CAN’T DO

Dollar-cost averaging is a myth that’s usually harmless; in fact, it can be beneficial because it imposes a good discipline on a person with an intention to save regularly. It may even be an essential discipline for a struggling investor. But it’s another example of a strategy that people think can do something for their investments that it can’t do.

So let’s explain why dollar-cost averaging won’t do what many people think it can do. We’ll use a column that one of the most insightful economists in the world, John Kay, wrote about it as our starting point. A regular columnist for the Financial Times, Professor Kay stated the argument for dollar-cost averaging as follows[3] (we have changed his pounds to dollars for the U.S. reader; also, in Britain “shares” means shares of stock):

Here is a scheme for beating the market that really works. Imagine a volatile share that sells for 50¢ in odd years and $1 in even years. If you invest $100 every year in this share, over a 10-year period you will have accumulated 1,500 shares at an average price of 66.7¢, well below the average market price, which is 75¢.

In odd years you get 200 shares for your $100, while in even years you get 100 shares for your $100—which comes to 66.7¢ a share on average. It does sound like it beats the market. You buy shares at an average price of 66.7¢, but the average market price is 75¢. You’re buying at a discount on average—or so it seems.

But does it really beat the market? No, it’s kind of an optical illusion. It’s easy to check. Let’s try it for only two years. Look at Figure 7.

You invest $100 at the beginning of year 1 when the price is 50¢ (because year 1 is an odd year), which then doubles to $200 by year 2 because the price doubles to $1. Then you invest another $100, so you’ve got $300, which gets cut in half by the end of year 2 so you wind up with $150.

What did the market do? The market started at 50, and at the end of the second year it went back to 50 again, so the market was flat. No, you didn’t beat the market. You invested $200, but you wound up with $150. You lost 25% of your investment while the market lost nothing.

But what if we flip-flopped and let the price be $1 in odd years and 50¢ in even years? Then you start with $100, which halves to $50. You invest another $100, so you’ve got $150, which doubles to $300. This time you beat the market.

FIGURE 7 Dollar-Cost Averaging

So whether or not you beat the market depends on which year you started. Not very conclusive, is it? This is no better than random. Half the time dollar-cost averaging beats the market, and half the time it doesn’t—at least in John Kay’s example.

Does Dollar-Cost Averaging “Work”?

Financial advisors who advocate dollar-cost averaging are really just recommending that their clients invest regularly. If their clients think this strategy will help them beat the market, well, so much the better—the added incentive might help them maintain the discipline.

We’ve seen in one example that dollar-cost averaging sometimes “works” (beats the market) and sometimes doesn’t. It depended on where the market started. It showed at least that it doesn’t always work. But does it usually work? How often? To answer that question, we must first pose the obvious additional question: Compared to what?

Dollar-cost averaging is the best plan if you have no alternative—that is, if the only way you can invest is by setting aside a regular amount out of your paycheck at regular intervals. But if you do have an alternative, what would it be? It must be that you have cash on hand that you could invest earlier than at those regular intervals. So let’s compare dollar-cost averaging with investing all at once right at the outset.

Let’s assume the two alternatives are (1) taking a lump sum and investing it in stocks all at once or (2) dividing it into equal pieces and investing it in the stock market at equal intervals—say, monthly—over time. Which one is better? This is a comparison we can make.

The Historical Advantage of Lump-Sum Investing over Dollar-Cost Averaging

We ran historical simulations for every monthly-rolling one-year, three-year, and five-year period during the 83 years from 1926 to 2008. In each case, we compared the rates of return on (1) investing a lump sum at the beginning of each period in the Standard & Poor’s 500 index, and (2) dividing the lump sum into equal monthly amounts and investing it monthly over the period. The portion of the lump sum that is not invested in the stock market with dollar-cost averaging is invested in one-month U.S. Treasury bills while waiting to be invested in stocks. The results are shown in Table 4. This table shows that, on average, investing a lump sum all at once gave you a much better return than investing piecemeal with dollar-cost averaging. Investing a lump sum, however, also gave you a higher variability (standard deviation) of returns—they fluctuated more. You could interpret this higher variability (as MPT does) as higher risk.

TABLE 4 Lump-Sum Investing vs. Dollar-Cost Averaging

Source: Study by Michael Edesess.

So, as usual, higher return is available only with higher risk. The historical study makes it clear that dollar-cost averaging does not, by some miracle, transcend this risk-return relationship.

THE TRUTH BEHIND THE GOLD(EN) RULE

“Gold is a hedge against inflation” is right up there with the most frequently heard quotes on earth, like “better safe than sorry.” But what does it mean? Presumably, it means that if the dollar (or some other major currency) gets so inflated in a few years that you can only buy half as much or a tenth as much in consumer goods—say, food—with it, then gold will retain its value. You’ll still be able to buy the same amount of consumer goods with gold.

Well, it’s plainly not true. First of all, you can’t even buy food with gold. No one will sell it to you. You’ll have to go exchange your gold for dollars first.

So if the dollar gets so inflated that you can only buy half as much with it, does that mean that your gold will buy twice as many dollars as it did? It sounds right, but it doesn’t work. Gold isn’t used as a currency anymore and probably never will be. There’s no reason why it should retain any particular value in terms of a currency. It’s free to float, just like a lot of other high-value items that have value because they’re, say, nice to look at, or because speculators think they’ll be worth even more later—like fine art. Such items have prices that fluctuate wildly and unpredictably. Their prices bear little or no relation to inflation, especially in the short run—and that short run can be quite long indeed.

In an interesting and informative article, “The Golden Dilemma,”[4] Claude B. Erb, a former investment management company executive, and Campbell R. Harvey, a professor of finance at Duke University, examined the argument that gold is a hedge against inflation. They found no support for that argument. On the contrary, gold ownership poses a much greater financial risk than inflation itself.

Gold wasn’t actively traded until 1975, after President Nixon took the United States off the gold standard. Erb and Harvey present evidence that shows that the price of gold in all major currencies has fluctuated from 1975 to 2012 much more than the currencies’ exchange rates. Hence, over that time (the only period of record with floating gold prices), holding gold subjected the owner to more risk than merely holding the wrong currency.

Erb and Harvey explore whether gold can hedge against unexpected inflation by investigating whether gold price changes correlate with changes in inflation rates. If year-to-year gold price changes correlate with inflation rate changes, then gold could be used to hedge against unexpected inflation. But these researchers find no such correlation. Their conclusion is that gold does not hedge against unexpected inflation in the short run.

In the longer run—10-year periods—annualized gold returns have fluctuated much more widely than inflation rates. “There has been substantial variation in …10-year annualized gold returns: from as low as -6% per annum to as high as +20% per annum,” Erb and Harvey write. “Over the same time period, the low and high inflation returns were +2.3% per annum and +7.3% per annum. [This] suggests that gold is not a very effective long-term inflation hedge when the long-term is defined as 10 years.”

“Gold is a hedge against inflation” is just another myth, nothing more.

SUMMARY OF DEADLY TEMPTATION #5

1. If total fees in your 401(k) are more than 1%, it is better to invest only up to your employer’s match, pay taxes on the rest, and invest it in low-cost funds outside the plan.

2. No theoretical or empirical reason or expert knowledge implies that it is right to rebalance your portfolio regularly; in some cases, it can even cause you to lose control of your risk.

3. Dollar-cost averaging, while it can impose a useful discipline on an investor who needs to save regularly, provides no other investment benefit.

4. Gold is not an effective hedge against inflation; in fact, holding gold as an asset is much more risky than the risk of inflation itself.

DEADLY TEMPTATION #6

Do What Wealthy and Sophisticated Investors Do

Wealthy and “sophisticated” investors actually get more wrong than ordinary investors. What they get wrong is that they think that because they’re fielding a lot of money, they can buy the best expertise—and that, of course, will get them the best return. But in the investment management business, there’s no relationship between what you pay for professional help and what you get in investment returns. We suspect it’s going to be hard to convince you how wrong this idea is.

Wealthy and sophisticated investors got wealthy somehow, right? Presumably because they knew how to make money. So is it possible that they don’t know how to make money better than the rest of us when it comes to investing?

Yes, that is exactly so. But it’s worse than that. They know how to lose money by paying the highest fees you can possibly pay. It’s understandable why they would make that mistake.

HOW SOME PEOPLE GET VERY WEALTHY

Let’s start with a humorous light-hearted fictional example, which also has an air of truth about it.

In the 1986 movie Down and Out in Beverly Hills, Nick Nolte plays a homeless drifter named Jerry, who accidentally wanders into a rich man’s backyard and somehow winds up getting intimately involved with his whole family. At one point Dave, the rich man (played by Richard Dreyfuss), proudly—but a little sheepishly—shows Jerry how he got rich: coat hangers. OK, someone has to make coat hangers. If you sell enough of them you’ll get rich. This was an ironic touch of realism in the film. It implied that many rich people get rich on what seems like a tiny little business niche—and in fact it’s true.

Now let’s take a real example. One of us (Michael) knew a man—we’ll call him Bob—who retired with hundreds of millions of dollars. What did Bob make his money on? It seems that sheet metal is produced in humongous rolls, to be used in automobile and truck assembly lines and other mammoth production processes. There are also businesses that need sheet metal for relatively smaller applications, though they still need a lot of it. The manufacturers of sheet metal in humongous rolls for automobile manufacturers don’t want to bother dealing with those customers. Bob’s business consisted of buying those humongous rolls of sheet metal, cutting them into smaller pieces, and selling the smaller but still huge pieces to customers who needed smaller quantities. On that business, Bob made hundreds of millions of dollars. Bob knew very, very well how to buy, cut up into smaller pieces, and resell sheet metal.

Does that imply that Bob knew better than less wealthy people how to invest in, say, the stock market? No, not really. Unfortunately, his knowledge did put Bob in an even better position than less rich people to be taken for a ride by someone who does claim to know better than others how to invest—and who charges a substantial chunk of Bob’s wealth for it.

Bob, like many other people who acquire a deep expertise in a narrow field and make very good money at it, probably assumes that a professional in another field, who makes very good money at it, also has similarly deep practical expertise in that field. It seems like a reasonable assumption. Unfortunately, when it comes to the investment field, it’s wrong. Almost no one, if anyone, is truly expert at investing in the stock market.

THE SEA OF HEDGE FUNDS

Bob and other wealthy investors are targets for investments called “hedge funds”—of which there have been as many as 20,000, with thousands more hedge fund employees. Of those 20,000 hedge funds, less than half are still in operation now. (Some of the 20,000 were the same person over and over again—their hedge fund performed poorly and failed, so they closed it and started another.)

It wouldn’t be so bad if hedge funds were only for people who can afford to lose the money. But increasingly, these funds have also been sold to those who can’t afford to lose it—police, firefighters, and teachers whose retirement savings are invested in public pension funds. It’s one reason why some U.S. cities are in financial trouble—because their pension funds don’t have enough money in them to make good on the retirement benefits they promised. Let’s see how hedge funds have helped to do that to them, by first explaining what they are.

“Hedge fund” is a term that does not describe the way a hedge fund invests but has to do with their history. The origin of the modern hedge fund is often attributed to Alfred Winslow Jones, who founded a fund in 1949 that bought some stocks (called “going long” in them) while selling others (“going short”), thus creating a “hedge” against average stock market movements. If Jones were good at selecting stocks that would do better than the market average and buying them, and also good at identifying stocks that would do worse than the market average and selling them, then he would do well no matter whether the market average went up or down. This kind of strategy is now called “long-short equities.”

Subsequently, the term “hedge fund” was applied to any investment fund, using any strategy, which was exempt from the rules that govern investments for the broad public, like mutual funds. The U.S. Securities Act of 1933 exempted funds that accepted investments only from “accredited investors,” who were defined as either individuals with net worth greater than $1 million or income exceeding $200,000, or institutional investors with investment assets in excess of $5 million.[1] Hence, a hedge fund is any fund that does not market itself and accepts investments only from a small number of accredited investors.

In 2000, when the broad stock market started falling, the total amount invested in hedge funds was only $200 billion, much less than 1% of all investments. But then hedge funds set about luring institutional investors like pension funds. By 2007, investments in hedge funds had increased more than tenfold, to $2.1 trillion.

Simon Lack’s Revelation

Recently, a man named Simon Lack who formerly worked for JPMorgan as a hedge fund “seeder”—that is, he selected nascent hedge funds to jump-start by seeding them with JPMorgan money in exchange for a big percentage of the hedge funds’ fees—wrote a book titled The Hedge Fund Mirage.[2] In it, he describes how badly investors in hedge funds have done. In fact, his data showed that over the first decade of the 21st century, hedge fund investors lost money in hedge funds. Meanwhile, according to a study by the Financial Times, hedge fund investors paid 4.3% of their assets in fees to hedge fund managers and 3% to hedge fund brokers each year.[3] From 2001 to 2010, that amounted to an average of $98 billion a year—more than five times the cost, in today’s dollars, of the program that first put a man on the moon, the Apollo space program. In short, hedge fund investors paid gigantic fees to hedge fund managers and their brokers—making many of them billionaires—while the investors themselves not only didn’t get rich, they actually lost money.

Lack’s revelation that hedge funds have done very poorly was so irrefutable that even the chief executive of the hedge fund lobbying organization, the Alternative Investment Management Association (AIMA), could come up with only a lame rebuttal[4]—basically blaming the bad performance on investors for investing in hedge funds at the wrong times.

Hedge Fund Performance Measurement

You might have seen reports saying that hedge funds have performed very well, but they’re wrong. Let’s summarize some of the more blatant errors in hedge fund performance reporting.

Studies of hedge fund performance have yielded wildly divergent results. Unfortunately, the financial media uncritically pass on to the public the simple averages of reported hedge fund performance spun out by database providers, without noting the deep flaws in those figures.

First, hedge fund performance numbers are reported by hedge funds themselves to the database providers. They report their numbers only when and if they want to. It’s obvious that they’ll tend to report their performance only when it is good. So the reported figures will be on average much higher than the real ones. This is called self-selection bias.

Hedge fund databases are so full of biases, errors, and trumped-up approximations that they’re practically worthless. Among their biases that have been studied are:

survivorship bias (funds that didn’t survive—usually because of poor performance—aren’t in the database);

backfill bias (funds that have had above-average performance add their historical data to the database—that is, they “backfill” it—only after they decide that it was good enough);

selection bias (as mentioned, funds report only if they choose to); and

back-end bias (funds that crash and close in their final year don’t report that year’s data).

As a result, reports of average hedge fund performance are worthless even though they are dutifully reported by publications otherwise as upright as The Economist and the Financial Times, without appropriate caveats, when passed on by database providers.

Lack’s Other Enlightening Insights

Because of Simon Lack’s once-central position in the hedge fund industry, he is able to shed light on some interesting questions. There are too many to relay here, but we’ll highlight one: the central position of large banks like JPMorgan and why they are able to make so much money.

Lack’s JPMorgan job was selecting promising hedge fund managers for JPMorgan to invest in. The process is called “seeding” a hedge fund. If a hedge fund manager wants to start a fund, or accumulate more assets for a small fund that is already launched, the manager can go to JPMorgan (and other big seed-funders) to try to get them to seed the fund with a substantial investment.

In Lack’s operation, seed funding was $25 million, which JPMorgan would invest to help a fund get started with a pool of assets. In return, JPMorgan would receive 25% of the fees charged by the fund. If the fund was successful, JPMorgan’s share of the fees often dwarfed what it earned in returns on its $25 million seed investment. So JPMorgan didn’t actually care very much if the fund got a good return on investment, as long as it could be sold to plenty of other investors.

Meanwhile, a large bank that seeds a fund can introduce the fund to its clients and prospects, lending the fund an imprimatur that enables it to attract investors much better than it could without the backing of the bank’s brand. As long as it abides by the legal restrictions on hedge fund marketing, a bank can leverage its brand to sell hedge funds that it has seeded to its clients, and then reap enormous fees in return. (Few are aware that Harvard University’s endowment fund, managed by Harvard Management Company, has also benefited[5] from similar seed-funding practices.) It should be readily apparent what a conflict of interest this practice poses, as well as what enormous profits.

So again we see that investment professionals make their money chiefly on what are often slightly shadily procured fees paid by investors, not on their investment expertise.

Hedge Fund Fees

Hedge funds are a bad deal because of their fees. Typically a hedge fund charges a 2% “administrative fee” on assets invested, plus a “performance fee” of 20% of gains. Provisions vary, but usually the 20% on gains is not charged if the fund’s price has not exceeded its last “high-water mark”—the highest level it reached previously.

Several problems arise with such “asymmetric” fees—asymmetric because the manager charges you when the value of your investments goes up, but doesn’t give anything back when it goes down. In fact, he keeps charging at least the administrative fee, which by itself is very high. The biggest problem, of course, is that you could wind up paying a lot for performance that isn’t very good, because the fund’s value could go up and down like the teeth of a saw. When it goes up, you’ll pay the 2% administrative fee plus 20% of what you gain; but when it goes down, you’ll just lose and still pay the 2% administrative fee.

So you pay a lot. But this fee structure provides an incentive to the fund manager to exaggerate the heights of the saw teeth; it incentivizes the manager to take risks, because the manager makes a lot of money from investors’ performance fees when the risks pan out but doesn’t lose it back when they don’t. (Some hedge fund managers have substantial amounts of their own money invested in their funds, in which case they do have “skin in the game.”) For example, it’s possible (especially using derivatives like options) to construct a hedge fund investment strategy that posts nice gains in most years—say, 4 out of 5 or 9 out of 10—but in the 5th or the 10th year, it loses it all back and more. Meanwhile, the manager—unless he really had a lot of his own money invested—has netted a tidy sum on fees.

Not many hedge funds deliberately engage in this kind of strategy, but a lot of hedge fund strategies seem, after the fact, to have resembled it anyway. An awful lot of hedge funds did very well for a few years; then suddenly their performance was dreadful. For example, Long-Term Capital Management, the hedge fund that had two of the Nobel Prize winners who invented modern option theory and an ex-Federal Reserve governor working for it, had returns in the 30% to 40% range for a few years in the 1990s, and then it lost almost everything it had in 1998. Investors who were in the fund during its good days did terribly overall unless they were lucky enough to withdraw their money before it crashed. It was so bad (the fund had borrowed a lot of money from big banks) that the Federal Reserve worried, like it did in 2008, that the fund’s failure would cause a domino effect and threaten the financial system. So the Fed arranged to have a meeting of the big banks that had loaned to the fund to get them to buy it out.

AN ABSURD VARIATION: FUNDS-OF-FUNDS

Institutional investors and wealthy individual investors piled into hedge funds in the 2000s. A lot of them didn’t really know much about this practice, but they had heard that the “smart” money was being invested in hedge funds. Also, a lot of these people and institutions had expensive consultants who had to make it appear to clients that they were doing something special for the money they were being paid. The consultants get invited by hedge fund managers to posh conferences in garden spots like Monaco. (Michael wrote in The Big Investment Lie about a posh hedge fund conference he attended, gratis, in Geneva, Switzerland.) Not only do they get to be wined and dined in a luxurious location, but they also get infused with sophisticated-sounding jargon that they can spout to their clients, enhancing their apparent value.

What can these consultants recommend to their clients? If they recommend one hedge fund, it might go sour. Better not to take that chance. Also, there’s the “diversification” thing that we talked about in Deadly Temptation #5. So they might recommend a “fund-of-funds,” a kind of mutual fund of hedge funds—it combines several hedge funds in one investment vehicle.

Of course, the funds-of-funds have to charge a fee. We’re already in the stratosphere as far as fees go, so what’s a little more stratospheric? Funds-of-funds fees make it, frankly, almost impossible to make any money in hedge funds—for the investor. Of course, for the hedge fund and funds-of-funds providers, the whole setup is a wonderful blessing, remuneratively speaking. These people are not necessarily consciously corrupted—though surprisingly often they are. They may be just sifting through numbers, doing analyses, trying to find a hedge fund manager or an investment strategy that works.

And some hedge fund managers are actually onto something from time to time. The now-famous hedge fund manager John Paulson and his deputy Paolo Pellegrini correctly assessed the overvaluation of mortgage funds in 2006 and 2007 and made “the greatest trade ever” by betting against them, earning several billion dollars. But after that, Paulson’s next bet went sour and the fund lost half its value.[6] If, impressed by his 2007 trades, you had put your money in his fund, you’d have lost half of it.

Sometimes even the investor is corrupted—well, not exactly the investor; in the case of pension funds, it is the fund administrator. The administrator doesn’t actually have money on the line—she’s just the agent. If the administrator runs, say, a public pension fund, the real investors are the taxpayers and the pensioners. The administrator also gets wined and dined by the hedge fund coterie; they imbue her with lingo that makes the administrator sound sophisticated to her bosses and her constituency. Who could argue with that? She gets sucked in.

Then, if you know the real situation, and you try to tell it to the actual investors, it’s almost impossible to convince them what a bad deal they’re getting. It’s hard for them to believe that all those sophisticated winers, diners, and jargon speakers are putting such a colossal one over on them. The odd thing is, it’s right there on the bottom line, as Simon Lack showed—their investment performance has been terrible. And still people can’t believe it.

All this said, some hedge funds do perform very well occasionally, as do some mutual funds and even some novice home traders, but we’ve seen that the performance may be merely due to chance and may not last. Goldman’s Global Alpha hedge fund did very well for a while, too, luring new investors—but we’ll see in Deadly Temptation #7 what happened.

HELP WANTED: MANAGING PENSION, ENDOWMENT, OR FOUNDATION FUNDS

Suppose you were suddenly put in charge of the teachers’ pension fund in your city. You discover it has $2 or $3 billion in it. What would you do?

Probably you would panic. You would think, “I’ve got to get help!” No one wants to be held responsible in a situation like that. If the market drops more than 40% as it did in 2008, the fund might lose a billion dollars. Do you want to risk being blamed for that? But if you hire a consulting firm that tells you to do what most other consulting firms would tell you to do, and it loses a billion dollars, at least you can say, “Well, it’s what the consultants advised, and they’re an established and well-regarded firm and everybody else did it, too.”

Now suppose you run a firm that consults on the investment of pension funds. What are you going to do? Will you recommend to your clients a course of action that’s completely different from what other pension consulting firms recommend? No, you could get unlucky, and that course of action might cause your clients to do worse than anybody else. If your advice isn’t much different from the advice that competing consulting firms give, your business will survive. So after a while everybody does the same thing. Whatever has evolved becomes standard practice—even if it makes no sense.

That’s what has happened. Investment consulting to large institutional investors has become a Rube Goldberg machine, going through a whole series of ridiculous contortions to arrive at a simple end result—but for high fees, of course. (Rube Goldberg [1883-1970] was a cartoonist who drew incredibly complicated machines that did something very simple, like light a match.)

The Standard Method of Consulting to Institutional Investors

The method that investment consultants use with institutional investors such as pension funds resembles not so much a scientifically derived procedure but a set of practices of a cult religion that the cult’s adherents assume must have some reason for them, but nobody knows or remembers what the reasons are.

Here’s roughly how it works in a typical case. The consultant, after meeting with fund administrators to determine the fund’s needs, runs a “mathematically optimized” asset allocation to come up with a recommended division of the fund among several asset groups. Keeping it simple for now, let’s suppose these asset groups include some bonds and some stocks. The stocks are typically divided into about four or five equity-style categories such as large growth stocks, small value stocks, and so forth. We’ve already examined in Deadly Temptation #3 what running an asset allocation model using investment styles means—it means rigging it with inputs that will make it produce acceptable outputs.

Once the recommended portfolio has been divided into style categories, the process begins of selecting an investment manager for each style. The consultant supplies several potential managers for each style. Each of them is sent a lengthy questionnaire about their investment performance, their employees, how long each has been with the firm, any legal scrapes the firm has gotten into, and on and on.

After these questionnaires are returned, the field of possible managers for each style category is narrowed down to about three. The remaining candidates are asked in for an interview, a major event for investment management firms because they stand to make a lot of money if they win.

With the help of the investment consulting firm, the fund selects an investment manager for each style category. Then, as each investment manager proceeds to invest its portion of the funds over the next year or two or three, the consultants help monitor its performance to see whether it is experiencing “style drift.” A manager’s portfolio exhibits style drift if it starts to look different from the style the manager was supposed to manage. Suppose, for example, the manager was hired to manage a large-value portfolio. The portfolio exhibits style drift if its periodic returns have a high “tracking error” as compared with returns on a large-value equity index.

It’s a no-no for investment managers to exhibit style drift. If they do, then the firm’s managers are called on the carpet by the pension fund administrator and the consultant to explain why.

WHAT’S WRONG WITH ALL THIS?

In the investment field, the right way is the simple way. The complex way is so complex that it is nothing but a Rube Goldberg machine. In fact, the complex way turns out to be the same as the simple way, except that it is made to look more complex—and of course with much higher fees charged by all.

As we explained in both Deadly Temptations #3 and #4, the chances are good that the way the style allocation divides the equity portfolio is almost the same as the way the whole equity market is divided. A large institutional investor can invest in the whole global stock market using one or two index funds for an extremely low fee. In fact, the largest pension fund in the United States, the $270 billion California Public Employees’ Retirement System (CalPERS),[7] is moving toward indexing almost all of its investments, having decided on “just dropping all pretense that beating the market is a worthwhile goal and taking its tens of billions’ worth of retirement investments mostly, if not entirely, passive.”[8] By contrast, if an institutional fund does not index, then between the consultants and the managers the consultant helps the fund choose, the charges can easily be 10 times as much.

It’s even worse than that. A recent study by three Oxford professors showed that managers chosen with consultants’ help do significantly worse than if the fund had no consultants.[9]

Then why go through the charade of breaking the market up into style categories, screening, interviewing, and selecting a manager for each category and then making sure the manager adheres to its style? Think about it: who benefits from this process?

The managers, the consultants, and the fund administrator do. The managers and consultants benefit because they’re paid very, very well. The fund administrator benefits because he appears to be going through a proper due diligence process: it makes him look smart, and he can blame the managers and consultants if things go wrong.

Who does not benefit? The employees, the retirees, and the taxpayers—that’s who. They pay 10 times too much to get the same result they could have obtained for one-tenth the cost.

SUMMARY OF DEADLY TEMPTATION #6

1. Hedge funds for wealthy investors and institutional investors do not perform better than low-cost index funds, and they charge outrageously high fees.

2. The only people who really benefit from the Rube Goldberg-like complexities of the management of most institutional investment funds are the fund managers, consultants, and fund administrators—certainly not the target beneficiaries of these investments.

DEADLY TEMPTATION #7

Use Modern Scientific Financial Theory

This is often the deadliest of temptations—and one of the most brazen. Science encompasses an array of serious, well-developed fields such as physics, chemistry, and biology. Modern finance bears no resemblance to any of them. To apply the term “science” to the field of modern finance is to taint all sciences. Both science and engineering use mathematics in practical and sound ways. The mathematics used in these fields is both much more sophisticated and more practical than the mathematics used in the investment field.

Let’s explore some of the ways investment products and services sold by Wall Street and the financial industry use—or pretend to use—mathematics and “modern scientific financial theory.”

THE PERFORMANCE OF DISCIPLINED QUANTITATIVE INVESTMENT STRATEGIES

You may sometimes hear about “quantitative investment strategies” and wonder whether an approach that’s disciplined by mathematics and computer programs will add value. It sounds right, of course—you’d rather have a surgeon who went to medical school and learned about human anatomy and knows how to use the latest medical technology than one who doesn’t.

But remember that the general rule in the investment profession is totally counterintuitive: expertise doesn’t help; professionalism doesn’t help; sophistication doesn’t help; and, in fact, technology, in general, doesn’t help. That is, at least they don’t help you to “beat the market,” if that’s your goal.

The use of quantitative investment strategies is no exception to that rule. One problem is that this term isn’t clear. What, exactly, are “quantitative investment strategies”? Well, here’s one definition, offered in a study by the consulting firm Greenwich Associates that was commissioned by the large investment management firm BlackRock:

The term “quantitative strategies” is a general label applied to a broad and dynamic set of approaches that apply rigorous and systematic analysis to investing. Quantitative investors use multiple sources of information to design consistently applied investment processes to manage portfolios with typically greater breadth than fundamental strategies. Many quantitative managers’ approaches are differentiated from so-called “black box” strategies by the fact that investment teams have the ability to intervene to allocate risk dynamically if market conditions call for it, as opposed to implementing purely model-driven processes.[1]

Huh? A bunch of gobbledygook, no? But this is what you’ll typically get in write-ups that describe “sophisticated” investment approaches. For some reason, it’s assumed that if you don’t understand the explanation, you’ll think it’s sophisticated.

Some people still insist that a computer program will help them maintain some investing discipline and keep them from just following the herd. Wall Street Journal reporter Eleanor Laise squashed that notion in a 2010 article on the disappointing performance of quantitative funds, which were now trying to—guess what?—become less “quant” and more human:

Computer-driven mutual funds, chastened by a string of poor results and a wave of redemptions, are striving to bring more of a human touch to their investment decisions.

These so-called quantitative funds, which rely largely on computer models to select investments, have been on the fritz for several years. A group of 65 such funds tracked by investment-research firm Morningstar Inc. lagged behind 72% of their category rivals, on average, in the three years ended Aug. 27.[2]

That’s a pretty bad record. How come they did so poorly, considering that (or so you would think) they were each pursuing a disciplined objective strategy instead of just following the herd like silly human beings? According to Laise, “It became painfully apparent in recent years that many quant funds were focusing on the same types of data to pick stocks. So as some of these portfolios began to stumble, they all suffered in unison.”

So what was their response? Become more like humans. “Many of the funds’ managers are seeking to make their models a little more like people,” writes Laise, “by making them more responsive to changing circumstances. That can mean revisiting computer models more often, tweaking their components, or incorporating measures of macroeconomic risk rather than just stock-specific information.” In other words, they’re trying to put back that subjective human element, which the whole mission of quant funds was to take out.

THE PERFORMANCE OF INDEX FUNDS BASED ON SPECIALIZED INDEXES

Another marketing pitch that has gathered steam recently is built around so-called smart beta strategies. The marketing has often been effective, causing many investors to buy into it. Those who market these strategies frequently imply that there’s a mathematical truth that proves that the strategy is superior. But like many such claims in the investment field, it’s merely pseudo-mathematics—there is no such truth. Let’s backtrack to get to the origin of this deception.

The Trend Toward Indexing

For now, only a minority of investors invest in index funds, though the number of individual and institutional investors doing so is growing because of the overwhelming evidence that index funds do consistently better than other funds after fees. While index funds are gaining an increasingly large following, many investors may not hear the explanation of why index funds are better—they just remember hearing that index funds are good. So, some marketers have tried to jump on the bandwagon by using the word “index” to help market new products—even if the products aren’t really like index funds, and have higher fees.

Before we get into this, we need to define some terms, including “value stocks,” “cap-weighted,” and “fundamental indexing.

Value Stocks

A value stock is a stock in a company whose stock price is low compared to its “book value,” which is, roughly speaking, the total of the company’s assets—its desks, its chairs, its buildings, its machinery, and so on—valued individually and added together.

Value stocks have historically performed better than expected. Some possible reasons have been conjectured for this. One is that value stocks have risks that aren’t captured well in MPT’s only measure of risk, variability—which, as we’ve pointed out before, is not a very good measure of risk. Another reason that has been advanced is that people are irrational. As the hypothesis goes, they don’t understand that if the price of a stock is depressed, that means it’s a good buy, not a bad buy.

In any case, because of this observed tendency, a number of money managers have announced themselves “value” managers—that is, they prefer to buy value stocks and think they will outperform a market index that way. The results, as with any investment strategy, have been mixed; but in a way, this approach might be as good as any other investment strategy—except for the fact that managers of this strategy have less diversified portfolios and charge more than actual index funds.

Cap-Weighted Indexes

“Cap-weighted” is short for “capitalization-weighted.” The capitalization of a stock—or of the company that issues it—is the price per share multiplied by the number of shares outstanding. In other words, it is the total market value of the company if its shares could be sold all at once at the current market price. The total market value of all stocks together is the sum of their capitalizations. Hence, the whole market is “capitalization-weighted” or “cap-weighted.” A cap-weighted index fund mirrors the whole market—that is, it’s a microcosm of the market. It will perform exactly the same as the market as a whole does. Most market indexes, like the Standard & Poor’s 500, are cap-weighted. (“Market-weighted” is another term sometimes used to mean “cap-weighted.”)

Other weightings are possible. For example, an equal-weighted index holds one share of each company—or perhaps an equal dollar amount of each company. As a result, in an equal-weighted index, small companies will be weighted about the same as large companies. Therefore, a small company will be a much larger proportion of an equal-weighted index than of a cap-weighted index. Hence, an equal-weighted index has a bias toward small companies, as compared to the market as a whole.

Only a cap-weighted index mirrors the whole market—it’s the only one that everyone could own at once. And a cap-weighted index adjusts automatically to keep on mirroring the market even as stock prices change. These are the reasons why it’s the type of index used for most index funds.

Fundamental Indexing

Someone came up with the term “fundamental indexing” to mean a way of automating the picking of stocks by eliminating the “expert” and using a simple algorithm instead. Fundamental indexing algorithmically “tilts” the portfolio, usually toward value stocks because value stocks have often performed better in the past.

But as those who really understand the stock market know, past performance does not predict future performance. So even though the value stock effect has been somewhat persistent as such effects go, there’s no guarantee it won’t disappear in the future—especially if a lot of people buy into it. The very fact that people have noticed that a particular strategy for stock picking has worked, often means that it will start not to work anymore.

A lot of people do know that the past good performance of value stocks is no guarantee they’ll do well in the future—especially people “sophisticated” enough in investments to know why index funds have a big advantage over other funds. A better argument was needed to convince people that fundamental indexing is really better. That scenario gave birth to mathematical-sounding arguments claiming that any index that is not cap-weighted, even if the weightings are picked at random, will perform better than a cap-weighted index. Some of these arguments actually managed to get published in prestigious academic finance journals—even though the arguments are complete nonsense, as we shall see.

The Problem of Pseudo-Mathematics in the Investment Field

You may wonder how it’s possible that papers published in prestigious journals and studies performed by prestigious institutions could claim to prove something mathematically that can’t be true; but then, you might not understand the investment industry and its academic arm. The mathematics in the finance field is not sophisticated, nor is most of it of any practical use. The mathematics used in other fields—for example, aerodynamics and hydrodynamics, and many other fields of engineering and science—is far, far more sophisticated and complex. Moreover, it is used very effectively for practical applications, like making an airplane fly.

Not so finance. Mathematics in financial journals is—contrary to the common lay belief and contrary to the common belief even within the finance field—usually of extremely poor quality and often wrong. Mathematicians have finally begun to direct their attention to this problem. Recently authors of a paper critical of the pseudo-mathematics in the investment field declared, “We would like to raise the question of whether mathematicians should continue to tolerate the proliferation of investment products that are misleadingly marketed as mathematically founded.”[3]

The idea that any randomly chosen portfolio must outperform a cap-weighted market index makes no sense. Here is why.

Suppose you randomly pick some stock weights for a portfolio. Call this portfolio A. Now consider an index fund portfolio that is much bigger than portfolio A. We’ll make the index fund big enough that the number of shares of each stock in it is greater than those in portfolio A. If you take portfolio A out of the index fund, let’s call what’s left portfolio B. The two parts of the index fund—portfolio A and portfolio B—can’t both outperform the index fund. If one outperforms it, the other must underperform it, because together they are the index fund. So the claim that any randomly weighted portfolio will outperform a cap-weighted market index is absurd.

The Cass Business School Study

Nevertheless, The Economist magazine reported that a study by a prestigious business school, the Cass Business School of City University London, somehow concluded “that a system that randomly chose constituent weights for stocks, like chimpanzees throwing darts at share-price listings, beat the market.”[4] The eyebrow-raising part of this—apart from the fact that the conclusion is impossible—is the phrase “randomly chose constituent weights.” There are lots of ways to randomly choose constituent weights. So we got the study to see how they did it.

The Cass study limited itself to 1,000 stocks. It constructed a cap-weighted index of those stocks and computed its performance over the time period it studied. Then it set about defining a random-choice mechanism for randomly chosen portfolios. Here’s how they did it.

Imagine that the 1,000 stocks were 1,000 numbered balls in an urn. They picked balls out of the urn (i.e., stocks), one at a time, to create a “randomly chosen” portfolio. But each time they picked, they put the ball back, so it could get chosen again. They chose 1,000 shares this way.

But if they chose portfolios like that again and again and added them all together, they’d get a big portfolio with all the weightings the same. So their “randomly weighted” portfolios were constructed so that, in aggregate, they were an equal-weighted index. In other words, their randomly generated portfolios held more dollars in small stocks than the market as a whole.

It so happened that small stocks performed better than large stocks after the 1990s. That period was weighted heavily in the Cass study. (In the 1990s, small stocks performed worse than large stocks.) So the Cass results weren’t—as they seemed to be—a general mathematical statement about the inferiority of cap-weighted indexes. They were only about how small stocks performed in the historical period they studied.

But the marketers of “fundamental indexing” were gleeful. Their false claim that there’s some sort of mathematical reason why any non-market-cap-weighted index at all will outperform a cap-weighted index seemed to be validated—and in a respected and widely read publication.

INVESTING SCIENTIFICALLY USING THE 3- (OR 4-, OR 18-) FACTOR MODEL

The idea of factor models has somehow captured much of the part of the investment world that likes to believe it engages in “scientific” investing. The online dictionary Investopedia.com defines “factor models” this way:

Multi-factor models are used to construct portfolios with certain characteristics, such as risk, or to track indexes. When constructing a multi-factor model, it is difficult to decide how many and which factors to include. One example, the Fama and French model [named after finance academicians Eugene Fama and Ken French], has three factors: size of firms, book-to-market values and excess return on the market. Also, models will be judged on historical numbers, which might not accurately predict future values.[5]

Fama and French’s papers are exercises in running “regressions.” Regression analysis is by far the most frequently used—and overused—mathematical modeling technique in social science.

Regression is often explained to students in about the second year of high school—at least it was in our high school. There, it was called “least-squares fit.” You have data points that graph something like this (Figure 8a):

FIGURE 8A Plot of Points on an x-y Graph

You try drawing a straight line through them and measure its distance from each point, like this (Figure 8b):

FIGURE 8B Regression Analysis (Least-Squares Fit)

With a little math, you can find the line that minimizes the sum of the squares of those distances. This gives you the so-called least-squares fit (Figure 8c):

FIGURE 8C Least-Squares Fit Line (Regression Line)

This is also called “running a regression.” The line is called the “regression line.”

Let’s see how running a regression applies to investing. Imagine, for example, that the variable on the horizontal axis is the rate of return on the whole stock market in any given month, and the vertical axis is the rate of return on a particular stock portfolio. Then the regression line is supposed to be the underlying relationship between the rate of return on the portfolio and the rate of return on the market. The slope of the line is called the portfolio’s “beta.” The distances of the points from the line are the random portfolio-specific variations. The place where the line hits the vertical axis is the portfolio’s “alpha,” or risk-adjusted performance.

You could visualize these points in three dimensions instead. Imagine points in three-dimensional space with a plane drawn through them so as to minimize the sum of the squares of the distances of the points from the plane. Then the plane would express a linear dependence of one of the variables on the other two. Those two are called “factors.” The same thing can be done in higher dimensions with more variables, but you can’t visualize it.

The Fama-French Studies

In the simple example in Figures 8a-8c, portfolio returns were regressed against just one variable, the return on the market as a whole. This is the Capital Asset Pricing Model (CAPM), which was William F. Sharpe’s contribution to modern portfolio theory. Sharpe’s model says you get a higher expected return if you take more market-related risk (i.e., if you have a higher beta).

In two studies in 1992 and 1993, Fama and French ran regressions of stock portfolio rates of return against not just one but three variables: returns on the market as a whole, returns on “value” stocks (those with a low ratio of market price to book value), and returns on “small” stocks (those with low capitalizations). Their regressions concluded that historically just two factors—value stock returns and small stock returns—explained most variation in portfolio returns. This observation tended to imply that the more your portfolio varied like a value stock portfolio (or the more it was a value stock portfolio) and the more it varied like a small stock portfolio (or the more it was a small stock portfolio), the better its returns would be.

It should be noted that Fama and French ended both papers with similar caveats, saying in the 1992 paper, “Our results …are not economically satisfying.” They continue, “What is the economic explanation for the roles of size and book-to-market equity in average returns?”[6] In other words, they have no explanation for why they should have found these dependencies in the data when they ran their regressions.

Nonetheless, though lacking a good explanation for why these results occurred, most of the finance profession concluded—though without adequate justification, as Fama and French themselves noted—that value stocks and small stocks not only outperformed the market in the past but will do so in the future, too.

Fama and French updated their work in a more recent paper, “Size, Value, and Momentum in International Stock Returns,” published in 2011.[7] This paper, disappointingly, still does not attempt to explain the reasons that there should be a dependence of the returns of any stock on its degree of “small-ness” and/or “value-ness.” Instead, it is a continuation of the exercise of running regressions.

The authors did add a factor that does not appear in their earlier papers: the momentum factor. This addition is due to the relatively recent observation—noted in Deadly Temptation #1—that stocks have tended to perform better in the subsequent month or year if their previous month’s or year’s return outperformed the market. Fama and French’s data indicated evidence of the momentum effect (except in Japan)—as it has been found in other studies. Adding momentum as a fourth factor creates a better fit to returns in some regions, they say. The upshot of the 2011 study is a hash of conclusions and nonconclu-sions. Because so many countries and factors are tried, some produce what are apparently significant results, while others do not.

In Fama and French’s papers, the coauthors assume a scientific posture of objectivity—not even trying to interpret or explain their results. One reason, of course, would be that they can’t explain them. Another is that any explanation would be sheer speculation and therefore doesn’t belong in a scientific paper—another way of saying they don’t have a good explanation.

There’s nothing wrong with presenting results without explaining them, and leaving it to others to try to explain them. That’s the idea of seminal research; if your findings provoke discussion and efforts to interpret them, eventually leading to a new and improved theory, that’s great. (Fama, in fact, during the writing of this book, received the Nobel Prize for a different, more important seminal paper he wrote 50 years ago on the “efficient market hypothesis.”)

Regression formulas alone, however, are not a theory. They are merely patterns perceived in data—barren of explanation, and possibly accidents of randomness. Unfortunately, however, the results of these regressions are being treated as if they were a theory in and of themselves. This is not Fama and French’s fault—they said they don’t have an economic explanation for the results. But it’s the way their results are being treated, as if they were a theory and not just the unexplained results of regressions.

Lacking a theory, these results have no lasting or practical implications. Without an explanation for them, there is no reason why results mined from historical investment data can predict the future and thus identify a market-beating strategy.

The Goldman 18-Factor Model

Nonetheless, a good sales pitch is not to be sneezed at, especially if it involves representing how sophisticated and scientific the pitch person’s company is. And, of course, Goldman Sachs has to be as sophisticated and scientific as any. Thus, it came as no surprise to us to hear from an insider at Goldman Sachs that they use an “18-factor model” in their investment strategy.

You need to know something about Goldman’s investment performance—that is, the performance of their clients’ investments. A 2011 article describes how badly clients have done when they invested with Goldman:

A big chunk of GSAM’s [Goldman Sachs Asset Management’s] assets are its separate accounts—pools of money invested for institutions and wealthy individuals. EVestment Alliance LLC, an Atlanta-based research firm, tracks about $300 billion held in the accounts and finds that Goldman trailed its peers in 73.8 percent of the categories EVestment looked at during the five years ended on Sept. 30.

Chicago-based financial publisher Morningstar Inc. tracks Goldman mutual funds and found that the 338 fund share classes it looks at trailed the average return of their respective peers in every broad category, including U.S. diversified equity, non-U.S. stock and taxable bonds, over the 3-, 5- and 10-year periods ended on Dec. 31.[8]

It should be noted that Morningstar’s finding that Goldman mutual funds trail their “respective peers” means that their performance is way, way behind low-cost index funds, which generally beat about 80% or more of their peers.[9]

Yet, as this article notes, the “scientific” sales pitch—and the big financial industry brand name of Goldman—still draws investors who either don’t know or don’t care about this poor performance record.

SMALL STOCKS’ PERFORMANCE

In 1981, an academician named Rolf Banz published an article on small-stock returns over the years 1936-1975 in the Journal of Financial Economics. Banz’s paper showed that small stocks not only outperformed large stocks, but outperformed them on a risk-adjusted basis, too—they had a positive “alpha.” But after Banz studied them, the small stock effect—the small stock alpha—largely disappeared. Small stocks still often did better on the whole, but not on a risk-adjusted basis.

Much later, researchers Gary Miller and Scott MacKillop decided to do a quick regression on historical small stock returns.[10] They found what other people had found: that they didn’t exhibit much in the way of alpha, if any, during the years after Banz’s study.

This really shouldn’t be surprising. If some particular stock, or group of stocks, gets much more popular—often because of its past performance—people who want to buy it drive the price up. When the price of a stock is driven up, its future rate of return won’t be as good as it was before its price was driven up.

Miller and MacKillop then decided, just for the heck of it, to run their regression on Banz’s original data. They found no alpha. They were nonplussed. Why was there no alpha when Banz got an alpha?

We can answer that because we replicated their study.[11] They used a different kind of rate of return in their study. But it’s the rate of return you should use.

Rates of return can be very confusing. For example, mortgage lenders have to state a loan’s annual percentage rate (APR). That rate is calculated by taking the monthly interest rate and multiplying by 12 months.

The one-month period is arbitrary. You could just as well take the weekly rate and multiply by 52, or the hourly rate and multiply by 8,760, or the secondly rate and multiply by 31,536,000. If you keep time splitting, you eventually converge to the “continuously compounded rate” of return (CCR).

Let’s call the ordinary monthly rate the “holding period return” or HPR. By saying that the HPR is “the ordinary monthly rate,” we mean, for example, that if you start the month with $100 and don’t add anything and at the end of the month you have $101, then your HPR is 1%. By contrast, the CCR is 1.005%. The HPR and the CCR are slightly different. But there’s one attribute the CCR has that the HPR doesn’t have. It is “symmetric.” The chance that its value is -20% is about the same as the chance that its value is +20%. That’s not true of the HPR. The HPR is skewed—it’s significantly more likely it will be +20% than -20%.

Banz and virtually every other user of regression analysis in finance used the HPR. The researchers who backtracked his data (and we, when we replicated their work) used the CCR.

Which is better? They’re both rates of return. One is equivalent to the other, in the sense that one can easily be converted to the other. But for regression analysis—particularly to test whether the alpha you found was significant or not—statisticians consider it better to use the number that is symmetric: the CCR.

An arcane niche of mathematical statistics investigates such matters in depth, but we needn’t delve into it here. It is, however, generally assumed by those who apply regressions in fields of science and engineering that if the distributions are substantially skewed—that is, not symmetric—then the results may not be dependable. (Strictly speaking, the distributions should be normal—also called Gaussian—in order to apply significance-test statistics like the t-statistic.)

Well, it turns out that the HPR used in regressions often gives you a positive alpha when the CCR doesn’t, especially when the “dependent variable”—small stock returns, in our case—is particularly volatile. So the more the returns on some subgroup of stocks vary, the more likely it is that using the HPR will show they have alpha.

This discussion shouldn’t give you a lot of confidence in the statistical results obtained by financial researchers. The mathematical methods used in their research don’t satisfy the mathematician’s definition of “rigor.” Financial researchers frequently don’t define clearly what they mean by a particular variable or symbol in their mathematics, and they don’t check out adequately whether the assumptions of the methods they’re using are satisfied or not. They would be offended by what we’re saying, because they think they’re being rigorous—but they’re not.

We could write far more about what’s wrong with the use of mathematics in the investment industry and its claims to “science.” We haven’t even covered the mathematical models that failed dismally in the financial crisis of 2007-2009, or many other failures of mathematical models in finance.

Our bottom-line recommendation remains the same: when you hear claims that sophisticated mathematics or science is being used in the investment field, tune the noise out.

SUMMARY OF DEADLY TEMPTATION #7

1. The mathematics in the finance field is not sophisticated compared with most fields of science or technology; it is often full of holes or flat-out wrong, and most of it isn’t even of any practical use.

2. Investment strategies with mathematical-sounding names like “quantitative strategies,” “smart beta,” and “four-factor model”—like other investment strategies that are not quantitative—don’t produce superior investment performance, except by chance.

EPILOGUE

The Invisible Squeeze

By Michael Edesess and George Peacock

Since the global financial crisis (GFC) of 2007-2009, the world has become increasingly aware of two major problems. The first is the danger of systemic financial risk and breakdown. Due to the complexity and interconnectedness of the global financial system, a sudden domino-like series of failures seems possible at any time. The second problem is the justifiable concern over the financial industry’s concentrated political influence, achieved through contributions to political candidates, the revolving door between the financial industry and government positions, easy access to officeholders, and money spent for media campaigns. The simple advice that we recommend to you in Part I can help to mitigate these problems, through an important secondary benefit that we call the Invisible Squeeze.

In the late 18th century, economic philosopher Adam Smith coined the term “invisible hand” to describe how people acting in their own self-interest to maximize their gains may also benefit society, even if those people have no benevolent intent. In much the same way, when individuals and institutions (investors) seek to maximize their gains by pursuing the simple, low-cost strategy that we advocate in Part II, we assert that society will benefit as profits are squeezed from Wall Street and the financial sector. This profit squeeze will happen invisibly and unintentionally as investors pursue the best course to meet their own objectives.

PRODUCT AND PRICE CONFUSION: THE KEY CAUSES OF PROBLEMS

As we pointed out in the introduction, the high profits reaped in finance are the result of a unique combination of product and price confusion, enabling the industry to sell ordinary, inferior, and sometimes even worthless products at extraordinarily high prices. The proliferation of such products and services, facilitated by clever marketing, is the primary cause of investor confusion.

Customers of the financial industry can and should ignore the array of complex-sounding and opaque products, services, and advice that create no value but add enormous charges. We hope we have helped you understand that in finance, in the vast majority of cases, most features add no value as compared to simple low-cost alternatives. Avoiding these products and services will help increase transparency and reduce complexity, and stanch the torrential flows of fees from customers to the financial industry.

Beyond advising you to steer clear of complicated investment products and services, which can be relatively easy to spot, we also hope to have demonstrated that even mainstream investment options can have an enormous cost to your long-term financial health. Many basic mutual funds, for example, are able to charge 1% and higher annually based on an expectation of substantially outperforming a low-cost alternative, an expectation that is impossible to promise and rarely realized. These basic investment options drain your investment accounts unnecessarily and by a greater amount than most investors even dare imagine.

Widespread adoption of the investment strategy recommended in Part I would significantly reduce the impact of these problems. How? It’s simple. The more that investors implement strategies that avoid extra costs and complexity (with its unseen risks), the less fragile the system will become and remain. And the more each person chooses effective and low-cost investment options, the more industry revenues and size will shrink, reducing its influence. Each person who follows our recommendation could reduce his or her contribution to the investment industry’s revenue by as much as 75% to 90%. Investing in the Simplify Wall Street portfolio will help solve the problems of systemic risk and runaway political influence by simplifying the financial system, without making it less useful or efficient, and by reducing the huge and unproductive flow of funds from investors, large and small, to the superwealthy of finance.

THE RISE OF THE FINANCE INDUSTRY SUPERRICH, ANECDOTALLY

A guest article appeared in mid-2011 on the website of G. William Domhoff, a sociology professor at the University of California-Santa Cruz, by someone whom Domhoff knew years ago, an investment manager who works with very wealthy clients and chose to remain anonymous for obvious reasons.[1] The article was updated by the article’s writer in December 2013.[2] The writer, a very successful investment advisor, says, “I sit in an interesting chair in the financial services industry. Our clients largely fall into the top 1%, have a net worth of $5,000,000 or above, and—if working—make over $300,000 per year. My observations on the sources of their wealth and concerns come from my professional and social activities within this group.”

The writer points out that the 1% are not truly wealthy, however, until they reach the top half of the 1%. Moreover:

The higher we go up into the top 0.5% the more likely it is that their wealth is in some way tied to the investment industry. … Folks in the top 0.1% come from many backgrounds, but it’s infrequent to meet one whose wealth wasn’t acquired through direct or indirect participation in the financial and banking industries.

In the 2013 update, the writer concludes, “Wealth and income are streaming to the very top of the system and, particularly, to those who are direct or indirect beneficiaries of the financial industry…. The years 2009-2012 saw an enormous transfer of wealth upwards to the top 1% and, particularly, the top 0.1%.”

The writer shares other observations as well.

Recently, I spoke with a younger client who retired from a major investment bank in her early thirties, net worth around $8M…. Since I knew she held a critical view of investment banking, I asked if her colleagues talked about or understood how much damage was created in the broader economy from their activities. Her answer was that no one talks about it in public but almost all understood and were unbelievably cynical, hoping to exit the system when they became rich enough.

The writer concludes, “I think it’s important to emphasize one of the dangers of wealth concentration: irresponsibility about the wider economic consequences of their actions by those at the top.”

THE RISE OF THE FINANCE INDUSTRY SUPERRICH, EMPIRICALLY

Thanks to the recent work of several diligent economic researchers, a lot of empirical evidence has been gathered that shows how flows of funds from customers to the financial industry have greatly enhanced its wealth. For example, economist Thomas Philippon, an associate professor at New York University’s Stern School of Business, compiled total payments by the U.S. nonfinancial sector to the financial sector in a paper for the National Bureau for Economic Research.[3] Those payments came to almost 9% of the U.S. GDP in 2010, a near-doubling of finance’s share since 1980. The U.S. GDP in 2010 was $14.5 trillion; hence, 9% of that, or more than $1.3 trillion, was paid by the nonfinancial sector to the whole U.S. financial industry in 2010.

In another paper,[4] Philippon and coauthor Ariell Reshef, an assistant professor of economics at the University of Virginia, state that until 1990, workers in the financial industry earned the same compensation as workers with the same level of education in other industries. But by 2006, the average worker in finance received 50% more than those in other industries. The finance industry has not improved so much or in such a manner that it would explain or justify that significant increase. This difference was much more pronounced at the upper levels of the pay scale, say Philippon and Reshef. Workers in the top decile in finance earned 80% more than workers in the top deciles of other industries, and executives in finance earned 250% more than executives in other industries.

Who Receives This Increased Income?

Evidence points to the conclusion that much of the vastly increased flows from the nonfinancial sector to the financial industry in the United States in the last 30 to 40 years has gone to the very top of the nation’s income ladder.

Economic researchers Thomas Piketty of the Paris School of Economics and Emmanuel Saez of the University of California-Berkeley, find that “the share of national income accruing to upper income groups has increased sharply in recent decades, particularly in the United States…. This comes mostly from the very top. The top percentile income share itself has more than doubled, from less than 10% in the 1970s to over 20% in recent years.” Furthermore, Piketty and Saez say, “The top 1% alone has absorbed almost 60% of aggregate U.S. income growth between 1976 and 2007.”[5]

Evidence presented by three other researchers, Jon Bakija of Williams College, Adam Cole of the U.S. Department of Treasury, and Bradley Heim of Indiana University, narrows this down to the top 0.1% of income earners.[6] These researchers state that

the percentage of all pre-tax income (excluding capital gains) in the United States that was received by the top 0.1 percent of income earners rose strikingly from 2.2 percent to 8.0 percent between 1981 and 2006…. We find that executives, managers, supervisors, and financial professionals account for about 60 percent of the top 0.1 percent of income earners in recent years, and can account for 70 percent of the increase in the share of national income going to the top 0.1 percent of the income distribution between 1979 and 2005.

Of these, the largest portion, 18.4%, are directly employed in the financial professions; 15.0% are salaried nonfinance executives; and 13.6% are executives of non-finance-related closely held businesses.

Have the increased revenues to the financial industry and finance professionals produced any benefit? Defenders of the industry argue that it has developed highly efficient systems to transfer and diversify risk and to allocate financial resources where they are needed.

Yet in his research on this question, Philippon found that the financial industry is no more efficient at its job of financial intermediation—of transferring financial resources from their sources and allocating them to their uses—than it was decades ago when it received half as much as a share of GDP.[7] It merely gets paid more.

Of course, in any industry, most firms and most people seek to generate as much profit as possible. But when an industry doubles its share of the nation’s GDP and its average worker increases his or her pay by 50% relative to other workers in a few short years, we have to ask why that is so. We believe it is simple. The industry benefits from a series of lies, myths, and misdirections that confuse the consumer, who would otherwise be the natural check on and arbiter of market pricing. But you can change that—at least for your own investments.

THE SPIRAL OF INCREASING COMPLEXITY

As we noted in the introduction, the increasing complexity of financial products and services creates a wall of impenetrable, misleading, and deceptive information. This confusion adds greatly to the cost to customers of obtaining correct and truthful information and of doing financial transactions. Furthermore, the proliferation of products, strategies, and marketing noise tends to make even the more mainstream investment options harder to evaluate because they float in an overwhelming ocean of inscrutable information.

And when investors are confused, they seek ways to understand or even take advantage of the complexity. In turn, opportunistic providers of financial products and services exploit investors’ quest for this elusive and tantalizing “advantage” by producing increasingly complex-sounding and generally costlier offerings accompanied by cleverly designed marketing pitches. This self-feeding cycle does nothing but continuously perpetuate the confusion and complexity.

As we have all so recently seen and experienced, this complexity does not beneficially distribute risk throughout the financial system; on the contrary, it increases global financial systemic risk. For example, in the run-up to the GFC, both investors and regulators did not—and, worse and more likely, were not able to—properly evaluate the individual and systemic risks of pooled subprime mortgages and loans known as collateralized debt obligations (CDOs) and insurance contracts known as credit default swaps (CDSs). Ratings agencies were not able to evaluate them, either—or at least they had an incentive to allow their evaluations to be manipulated by their clients, the large investment banks that issued the CDOs. Neither investors nor regulators knew what was happening. But the financial industry did—or should have.[8]

When this kind of opacity builds up in the financial system, it puts the entire system at risk. The Global Financial Crisis, still too recent and casting too long a shadow for us to forget it, showed just how risky it is.

A LIFELINE TO A SOLUTION

As we’ve seen, the financial industry is what some have called a “rogue industry”[9] that poses dire risk to the entire global financial system. The enormous differentials between compensation in the financial industry and compensation in other industries, plus the huge size of the industry, have contributed to and solidified its economic and political power, and the widening of economic inequality. An upper echelon has emerged that is increasingly isolated from the socioeconomic realities of the vast majority of citizens. Furthermore, too many talented university graduates are drawn by the high pay to an unproductive industry—talent that could otherwise contribute to society in other areas.

Solutions to these problems have been sought through increasing regulation; in breaking up banks; in restoring moral values and good business ethics. But the banks have captured the regulatory system, and there has been no meaningful attempt to shrink them (and thus the risk). In fact, the largest banks are bigger than they were before the crisis. As for restoring moral values and good business ethics, we’ll merely say that that doesn’t fall under your control, and it’s a goal that is definitely not easy to accomplish.

But there is a simple, effective, and more obvious (once you think about it) solution, one that adheres to the basic laws of economics: the Simplify Wall Street (SWS) portfolio, introduced in Part I. It’s not only the best portfolio you can invest in for your own financial good; but the more people adopt it, the more it will squeeze from Wall Street a large portion of the profits that allow it to maintain its oligarchic hold on the regulatory process and maintain its oversized influence on the U.S. economy.

Investing in the SWS portfolio need not be an explicit “boycott” of Wall Street or the mainstream financial industry. You should pursue the SWS for your own good—because it’s the best investment strategy available. As if guided by Adam Smith’s “Invisible Hand,” however, each investor pursuing his or her own best investment interest will result in the best interest of society, because these choices collectively can significantly reduce systemic financial risk and concentrated economic power. The actions of individuals acting freely can accomplish what no regulation ever could.

We hope that we have cut through much of the confusion and made it clear that a very simple, least-cost option is easily available to all investors, whether individual or institutional. It will benefit you—and all of us as well.

NOTES

PREFACE

1. Richard Teitelbaum, “Blankfein Flunks Asset Management as Clark Vows No More Goldman,” Bloomberg, www.bloomberg.com/news/print/2011-01-25/blankfein-flunks-asset-management-as-jim-clark-vows-no-more-goldman-sachs.html.

INTRODUCTION

1. Robin Greenwood and David Scharfstein, “The Growth of Modern Finance,” available at Social Science Research Network, 2162179 (2012).

2. Greg Smith, Why I Left Goldman Sachs: A Wall Street Story (New York: Hachette Digital, 2012).

3. Greenwood and Scharfstein, “The Growth of Modern Finance.”

4. Steve Johnson, “Billions of Dollars Wasted on Investment Advice,” Financial Times, www.ft.com/intl/cms/s/0/f20eb888-213a-11e3-a92a-00144feab7de.html#axzz2oeYIzlYn.

5. Denise Appleby, “Saver’s Tax Credit: A Retirement Savings Incentive,” Investopedia, www.investopedia.com/articles/retirement/04/031704.asp.

6. Wikipedia, “Individual Development Account,” http://en.wikipedia.org/wiki/Individual_Development_Account.

7. Morningstar, “Ibbotson SBBI Classic Yearbook,” http://corporate.morningstar.com/ib/asp/subject.aspx?xmlfile=1414.xml.

8. “Treasury Inflation-Protected Securities (TIPS),” Wall Street Journal Online, Markets Data Center, http://online.wsj.com/mdc/public/page/2_3020-tips.html?mod=topnav_2_3020.

9. “Moody’s Credit Trends,” http://credittrends.moodys.com.

10. David Harper, “The Equity-Risk Premium: More Risk for Higher Returns,” Investopedia, www.investopedia.com/articles/04/012104.asp.

11. Credit Suisse, “Credit Suisse Global Investment Returns Yearbook 2012,” www.credit-suisse.com/investment_banking/doc/cs_global_investment_ returns_yearbook.pdf.

12. William Bernstein, Deep Risk: How History Informs Portfolio Design (Eastford, CT: Efficient Frontier Publications, 2013), p. 29.

13. Investment Europe, “Credit Suisse Global Investment Returns Yearbook 2013,” www.investmenteurope.net/digital_assets/6305/2013_yearbook_ final_web.pdf.

RULE #1

1. “Individual—TreasuryDirect,” Treasurydirect.gov, www.treasury-direct.gov/indiv/myaccount/myaccount_treasurydirect.htm.

2. An explanation of how inflation is calculated is at the 3 Rules of Investing website.

3. “Treasury Inflation-Protected Securities (TIPS),” Markets Data Center, Wall Street Journal Online, http://online.wsj.com/mdc/public/page/2_3020-tips.html?mod=topnav_2_3020.

4. “Vanguard Total World Stock ETF,” Personal.vanguard.com, https://personal.vanguard.com/us/funds/snapshot?FundId=3141&FundIntExt=INT.

5. “iShares MSCI World ETF (URTH): Overview—iShares,” Us.ishares.com, http://us.ishares.com/product_info/fund/overview/URTH.htm.

6. “ACIM—SPDR MSCI ACWI IMI ETF|State Street Global Advisors (SSgA),” www.spdrs.com/product/fund.seam?ticker=ACIM.

7. “Insider Trading Overview,” http://etfs.morningstar.com/quote?t=VT.

8. “URTH iShares MSCI World ETF URTH Quote Price News,” http://etfs.morningstar.com/quote?t=URTH.

9. Funds have different classes for different fees, and some are not available to everyone—some are only available to advisors; others have minimum investments. The different classes of mutual funds are explained on the 3 Rules of Investing website.

10. Joe Tomlinson, “How Safe Are Annuities?” Advisor Perspectives, www.advisorperspectives.com/newsletters12/How_Safe_are_Annuities.php.

11. National Organization of Life & Health Insurance Guaranty Associations, www.nolhga.com.

12. William Bernstein, Deep Risk: How History Informs Portfolio Design (Eastford, CT: Efficient Frontier Publications, 2013).

13. Michael Lewis, “In Nature’s Casino,” New York Times, August 26, 2007, www.nytimes.com/2007/08/26/magazine/26neworleans-t.html?pagewanted=all.

RULE #2

1. Zvi Bodie, “On the Risk of Stocks in the Long Run,” 2001, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=5771.

2. Credit Suisse, “Credit Suisse Global Investment Returns Yearbook 2013,” www.credit-suisse.com/investment_banking/doc/cs_global_investment_returns_yearbook.pdf.

3. William Bernstein, Deep Risk: How History Informs Portfolio Design (Eastford, CT: Efficient Frontier Publications, 2013).

DEADLY TEMPTATION #1

1. Richard Brealey and Stewart Myers, Principles of Corporate Finance (New York: McGraw-Hill/Irwin, 2003), www.er.ethz.ch/teaching/Chap13_Market-Efficiency.pdf.

2. Alfred Cowles, “Can Stock Market Forecasters Forecast?” Economet-rica: Journal of the Econometric Society (1933): 309-324.

3. Michael Edesess, The Big Investment Lie: What Your Financial Advisor Doesn’t Want You to Know (San Francisco: Berrett-Koehler, 2007), p. 94.

4. Holbrook Working, “Note on the Correlation of First Differences of Averages in a Random Chain,” and M. F. M. Osborne, “Brownian Motion in the Stock Market,” in The Random Character of Stock Market Prices, ed. Paul H. Cootner (Cambridge, MA: MIT Press, 1964).

5. William F. Sharpe, “Mutual Fund Performance,” Journal of Business 39, no. 1 (1966): 119-138.

6. Michael C Jensen, “The Performance of Mutual Funds in the Period 1945-1964,” Journal of Finance 23, no. 2 (1968): 389-416.

7. IFA.com, “Step 3: Accept That Stock Pickers Do Not Beat the Market,” www.ifa.com/12steps/step3/.

8. G. William Schwert, “Anomalies and Market Efficiency,” Handbook of the Economics of Finance 1 (2003): 941.

9. Mark M. Carhart, “On Persistence in Mutual Fund Performance,” Journal of Finance 52, no. 1 (1997): 57-82.

10. Laurent Barras, Olivier Scaillet, and Russ Wermers, “False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas,” Journal of Finance 65, no. 1 (2010): 179-216.

11. Mark Hilbert, “The Prescient Are Few,” New York Times, July 13, 2008, www.nytimes.com/2008/07/13/business/13stra.html?_r=1&.

12. Bankers-anonymous.com, “Who Killed Fund Performance? We All Did,” www.bankers-anonymous.com/blog/who-killed-fund-performance-we-all-did/.

13. Wikipedia, “Bill Miller (Finance),” http://en.wikipedia.org/wiki/Bill_Miller_%28finance%29.

14. Frank Armstrong III, “Bill Miller’s Unglorious End Points Out Dangers of Running with the Crowd,” Forbes, May 1, 2012, www.forbes.com/sites/greatspeculations/2012/05/01/bill-millers-unglorious-end-points-out-dangers-of-running-with-the-crowd/.

15. Wikipedia, “Julian Robertson,” http://en.wikipedia.org/wiki/Julian_Robertson.

16. John C. Bogle, Don’t Count on It! Reflections on Investment Illusions, Capitalism, Mutual Funds, Indexing, Entrepreneurship, Idealism, and Heroes (New York: Wiley, 2010).

17. Edesess, The Big Investment Lie, p. 91.

18. This intuitive statement has been oversimplified for purposes of illustration. It is true that on any series of 10 coin tosses, the probability they will all be heads is 1/1,024, about one in 1,000. However, that doesn’t assure that if 1,000 coins are tossed, then one of them will come up heads 10 times in a row. In fact, the probability that none of them will come up heads 10 times in a row is (1,023/1,024)1,000, which equals 37.6%. Hence, the probability that at least one will come up heads 10 times in a row is 100% - 37.6% = 62.4%.

19. The academic studies include the following: Mark M. Carhart, “On Persistence in Mutual Fund Performance,” Journal of Finance 52, no. 1 (March 1997): 57-82; Eugene F. Fama, “Market Efficiency, Long-Term Returns, and Behavioral Finance,” Journal of Financial Economics 49, no. 3 (1998): 283-306; Burton G. Malkiel, “Returns from Investing in Equity Mutual Funds 1971 to 1991,” Journal of Finance 50, no. 2 (1995): 549-572; Richard Roll and Robert J. Shiller, “Comments: Symposium on Volatility in US and Japanese Stock Markets,” Journal of Applied Corporate Finance 5, no. 1 (1992): 25-29; G. William Schwert, “Anomalies and Market Efficiency,” Handbook of the Economics of Finance 1 (2003): 939-974; Burton Gordon Malkiel, A Random Walk Down Wall Street: Including a Life-Cycle Guide to Personal Investing (New York: Norton, 1999).

DEADLY TEMPTATION #2

1. Assumes 4% real annual return. Calculation details are at the 3 Rules of Investing website.

2. Qaib.com. “Quantitative Analysis of Investor Behavior,” www.qaib.com/public/freelook.aspx?activeMenu=GLB-1.

3. Michael Kitces, “Does The DALBAR Study Grossly Overstate the Behavior Gap? (Guest Post),” www.kitces.com/blog/does-the-dalbar-study-grossly-overstate-the-behavior-gap-guest-post/.

4. Dan Hallett, “Does DALBAR Really Calculate Investor Returns?” http://thewealthsteward.com/2011/04/does-dalbar-really-calculate-investor-returns/.

5. A constant annual return of -17.7% will cause $1 invested at the beginning of year 1 followed by a $1 invested at the beginning of year 2 to end with $1.50 at the end of year 2. At a -17.7% constant annual return, the first $1 ends year 1 as 82.3¢. With the addition of a second $1 at the beginning of year 2 the investor now has $1.823, which when reduced by 17.7% results in $1.50 at the end of year 2.

6. DALBAR, “2012 QAIB Quantitative Analysis of Investor Behavior,” www.thewpi.org/pdf_files/dalbar.2012.roccy.pdf.

DEADLY TEMPTATION #3

1. CFA Institute, Financial Analysts Journal 42, no. 4 (July/August 1986), www.cfapubs.org/toc/faj/1986/42/4.

2. Harry Markowitz, “Portfolio Selection,” Journal of Finance 7, no. 1 (1952): 77-91.

DEADLY TEMPTATION #4

1. Emma Dunkley, “Absolute Return Funds’ Woes Mount,” The Independent, www.independent.co.uk/money/spend-save/absolute-return-funds-woes-mount-8348095.html.

2. Tom Lauricella, “For Retirement, ‘One Size’ Isn’t Always a Good Fit,” Wall Street Journal Online, http://online.wsj.com/article/SB123549381087960625.html.

3. More information and examples can be found on the 3 Rules of Investing website.

DEADLY TEMPTATION #5

1. Society of Human Resource Management, “401(k) Plan Fees Declined—Slightly—in 2012,” www.shrm.org/hrdisciplines/benefits/articles/pages/401k-feeseclined.aspx.

2. Seth J. Masters, “Rebalancing,” Journal of Portfolio Management 29, no. 3 (2003): 52-57.

3. John Kay, “An Averaging System to Pile Up the Pounds,” Financial Times, www.ft.com/cms/s/2/0ea67852-e971-11dd-9535-0000779fd2ac.html.

4. Claude B. Erb and Campbell R. Harvey, “The Golden Dilemma,” no. w18706, National Bureau of Economic Research, 2013.

DEADLY TEMPTATION #6

1. U.S. Securities and Exchange Commission, “Accredited Investors,” www.sec.gov/answers/accred.htm.

2. Simon Lack, The Hedge Fund Mirage: The Illusion of Big Money and Why It’s Too Good to Be True (New York: Wiley, 2011).

3. “Hedge Fund Gains Are Other Funds’ Losses,” Financial Times, www.ft.com/intl/cms/s/0/9c8fb776-7e49-11e1-b009-00144feab49a.html#axzz2OW3L74Gu.

4. “Hedge Fund Book’s Analysis Is Nothing New,” Financial Times, www.ft.com/intl/cms/s/0/91f418e0-3d06-11e1-8129-00144feabdc0.html#axzz21uC5BVLZ.

5. Stephanie Strom, “Harvard’s Invisible Fund-Raising,” New York Times, July 25, 2004, www.nytimes.com/2004/07/25/business/harvard-s-invisible-fund-raising.html?pagewanted=all&src=pm.

6. Kelly Bit, “Paulson’s Advantage Plus Fund Drops 51% in ‘Aberrational Year,’” Bloomberg, January 6, 2012, www.bloomberg.com/news/2012-01-06/john-paulson-s-advantage-plus-hedge-fund-drops-51-in-aberrational-year.html.

7. California Public Employees’ Retirement System, “Facts at a Glance,” www.calpers.ca.gov/eip-docs/about/facts/facts-at-a-glance.pdf.

8. Mitchell Tuchman, “Pensions: CalPERS Embraces Indexing,” Market-Watch, October 3, 2013, www.marketwatch.com/story/pensions-calpers-embraces-indexing-2013-10-03.

9. Steve Johnson, “Billions of Dollars Wasted on Investment Advice,” Financial Times, www.ft.com/intl/cms/s/0/f20eb888-213a-11e3-a92a-00144feab7de.html?goback=.gde_4117540_member_275707895#axzz2feMLxh9F.

DEADLY TEMPTATION #7

1. Greenwich Associates, “Uses of Quantitative Investment Strategies in Asia,” 2013, www.greenwich.com/greenwich-research/research-documents/greenwich-reports/2013/may/cus-blackrock-a-quant-2013-gr.

2. Eleanor Laise, “Bruised Quant Funds Seek a Human Touch,” Wall Street Journal Online, http://online.wsj.com/news/articles/SB10001424052748703649004575437290148983902.

3. David H. Bailey, Jonathan M. Borwein, Marcos Lopez de Prado, Marcos, and Qiji Jim Zhu, “Pseudo-Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-of-Sample Performance,” October 7, 2013, http://ssrn.com/abstract=2308659.

4. “The Rise of Smart Beta,” The Economist, www.economist.com/news/finance-and-economics/21580518-terrible-name-interesting-trend-rise-smart-beta.

5. Investopedia, “Multi-Factor Model,” www.investopedia.com/terms/m/multifactor-model.asp.

6. E. F. Fama and K. R. French, “The Cross-section of Expected Stock Returns,” Journal of Finance 47 (1992): 427-486; and E. F. Fama and K. R. French, “Common Risk Factors in the Returns on Stocks and Bonds,” Journal of Financial Economics 33 (1993): 3-56.

7. Eugene F. Fama and Kenneth R. French, “Size, Value, and Momentum in International Stock Returns,” Journal of Financial Economics 105, no. 3 (2012): 457-472.

8. Richard Teitelbaum, “Blankfein Flunks Asset Management as Clark Vows No More Goldman,” Bloomberg, January 25, 2011, www.bloomberg.com/news/print/2011-01-25/blankfein-flunks-asset-management-as-jim-clark-vows-no-more-goldman-sachs.html.

9. Rick Ferri, “Hard to Beat a U.S. Total Market Fund,” Forbes, www.forbes.com/sites/rickferri/2012/08/13/hard-to-beat-a-u-s-total-market-fund.

10. Gary A. Miller and Scott A. Mackillop, “Rethinking Small Caps,” Financial Advisor, www.fa-mag.com/news/rethinking-small-caps-8117.html.

11. Michael Edesess, “The Small Cap Falsehood,” Advisor Perspectives, www.advisorperspectives.com/newsletters11/The_Small_Cap_Falsehood.php.

EPILOGUE

1. G. William Domhoff, “An Investment Manager’s View on the Top 1%,” University of California-Santa Cruz, 2011, www2.ucsc.edu/whorulesamerica/power/investment_manager.html.

2. G. William Domhoff, “An Investment Manager’s 2014 Update on the Top 1%,” University of California-Santa Cruz, 2014, www2.ucsc.edu/whorulesamerica/power/investment_manager_2014.html.

3. Thomas Philippon, “Has the US Finance Industry Become Less Efficient? On the Theory and Measurement of Financial Intermediation,” no. w18077, National Bureau of Economic Research, 2012.

4. Thomas Philippon and Ariell Reshef, “Wages and Human Capital in the US Finance Industry: 1909-2006,” Quarterly Journal of Economics 127, no. 4 (2012): 1551-1609.

5. Thomas Piketty and Emmanuel Saez, “Top Incomes and the Great Recession: Recent Evolutions and Policy Implications,” paper presented at the 13th Jacques Polak Annual Research Conference, Washington, D.C., November 2012.

6. Jon Bakija, Adam Cole, and Bradley T. Heim, “Jobs and Income Growth of Top Earners and the Causes of Changing Income Inequality: Evidence from US Tax Return Data,” Williams College, Williamstown, MA, 2010.

7. Philippon, “Has the US Finance Industry Become Less Efficient?”

8. Matt Taibbi, “The Last Mystery of the Financial Crisis,” Rolling Stone, June 19, 2013, www.rollingstone.com/politics/news/the-last-mystery-of-the-financial-crisis-20130619.

9. Aaron Elstein, “Wall Street’s Gone Rogue, Says Grantham,” Pensions & Investments, www.pionline.com/article/20100517/REG/100519866.

GLOSSARY

401(k) plan A “defined contribution” employee pension plan in which participants contribute a pretax amount (possibly matched—up to a percentage—by the employer) and choose among investment choices offered by the plan.

403(b) plan Similar to a 401(k) plan but offered to employees of educational and nonprofit organizations.

Accredited investor “An accredited investor can be an individual or an institution. An individual accredited investor is one who has a net worth of at least $1 million or has had income of at least $200,000 for each of the last two years (or joint income with a spouse of $300,000).” See www.sec.gov/answers/accred.htm for details.

Active management Investment managers who pick stocks (or bonds in the case of bond funds) to try to beat market averages.

Alpha The percentage by which an investment manager beats a stock market average after adjustment for risk.

Asset allocation The percentages, adding to 100%, by which an investment portfolio is divided among major asset classes such as domestic stocks, foreign stocks, bonds, and real estate.

Asset-liability models Pertains principally to pension funds: probabilistic modeling of the future liabilities of the fund—that is, the value of the obligation to pay future pensions—as compared with the future assets of the fund.

Assets (investment assets) The portion of total funds that are available to invest in investment vehicles such as stocks and bonds.

Asymmetric fees A type of performance fee for asset managers where the manager is rewarded for overperformance without being charged a penalty for underperformance.

Backfill bias The bias introduced into a database of hedge fund performance when funds that have had above-average performance add their historical data to the database—that is, they “backfill” it—only after they decide that it was good enough.

Backtest Running a simulation of an investment strategy using stock (or bond) market data for a historical time period to see how it would have performed over that period.

Basis point A hundredth of a percent.

Behavioral economics/behavioral finance The study of how people make economic and financial decisions, given that they do not always act rationally.

Beta The extent to which a portfolio of risky securities of a certain type (such as domestic stocks) is effectively “leveraged”—that is, the extent to which the portfolio carries risk greater than (or less than) the capitalization-weighted average of all securities in the category. Beta = 1 if the risk is the same as that of the weighted average.

Bias The tendency for a statistical sample from a population to be unrepresentative of the population as a whole. For example, databases of hedge funds are unrepresentative of the population of all hedge funds, because the databases contain numerous biases.

Bond A form of security in which the capital provided by the investor is repaid with interest in fixed amounts on a fixed schedule over a fixed time period (usually interest is paid semiannually and the principal is repaid at the end of the time period).

Book value Roughly speaking, the total of the company’s assets—its desks, its chairs, its buildings, its machinery, and so on—valued individually and added together.

Brokerage A firm that is licensed to buy or sell securities, though large brokerages perform many other functions as well.

Buy-and-hold A strategy of buying a portfolio of securities and holding them for a long time, without any further trades.

Call option An option to buy a specified security (the “underlying”) at a specified future time (the “expiration date”) at a specified price (the “strike price” or “exercise price”). See also Option.

Capital asset pricing model The theoretical model introduced by Nobelist William F. Sharpe and others, saying that the expected rate of return on a security (or portfolio of securities), in excess of the risk-free rate (such as that of a Treasury note), is proportional to the portion of the volatility of the security (or portfolio) that is correlated with the market as a whole.

Capital gains The increase in price over time of a security (or portfolio of securities). Capital gains are “realized” if the security (or securities) is sold, thereby incurring a tax liability in a taxable portfolio. Gains are “unrealized” if the security (or securities) is not yet sold.

Capitalization-weighting Refers to the weighting of the securities in a portfolio or market index (i.e., a hypothetical portfolio). The portfolio or index is capitalization-weighted if the weight of each security in the portfolio is proportional to the total value of its shares outstanding in the market. See also Equal weighting.

CAPM. See Capital asset pricing model.

Commissions (trading commissions) Payments to brokers to execute trades.

Consultant (in the institutional investment context) A consultant to institutional investors who helps to design and track the investment portfolio and choose managers for that portfolio, and sometimes helps to align assets with liabilities.

Correlation (coefficient) A statistical measure of the extent to which two variables vary in the same direction or in opposite directions. The correlation coefficient ranges from an extreme of +1 (for moving together in the same direction in lockstep) to the opposite extreme of -1 (for moving together in opposite directions in lockstep). A value of 0 means no correlation between the two movements.

Covariance Covariance is proportional to the correlation coefficient but is not scaled to be between -1 and +1.

Data mining The practice of performing undisciplined, repeated data studies in an attempt to find patterns in historical data. The danger is that it will uncover patterns that are merely random results, indicative of no inherent cause or design and of no value in predicting future patterns.

Day trading The practice of frequent intraday trading of individual stocks. Day trading is in general a losing strategy because it generates substantial commission costs even at low commission rates.

Debt For a company, the portion of corporate capital that has been financed by issuing bonds or other fixed income, as opposed to the equity portion obtained by selling shares of stock.

Debt/equity ratio For a corporation, the ratio of the value of the portion of corporate capital obtained by issuing debt obligations such as bonds and other fixed income, to the value of the portion obtained by issuing shares of stock.

Defined-benefit pension plan A pension plan for the benefit of retirees in which the amount of retirees’ future benefits is fixed, and the sponsor is obligated to contribute to the pension plan sufficient amounts to ensure that those fixed benefits will be realized.

Defined-contribution pension plan A pension plan in which contributions by employer and employees are fixed while the amounts of future retiree benefits aren’t predetermined but are whatever results from the growth of the contributions through investment.

Directed brokerage A method of payment for incidental services provided by a brokerage firm, such as research studies, in which payment is in the form of an agreement to use a certain amount of that firm’s brokerage services. Also called “soft dollars.”

Discount broker As opposed to a full-service broker, a broker who provides no advice or research but merely executes securities trades as directed by the customer, for a lower commission than a full-service broker.

Discount rate The annual percentage by which future dollars must be discounted to arrive at their equivalent in present-day dollars. For example, if the discount rate is 5%, then $1.05 in one year is worth $1 now.

Diversification Reduction in the risk of fluctuation in the value of a portfolio, also called “volatility” or “variability,” by investing in a large number of securities. Maximum diversification is achieved with a capitalization-weighted portfolio of all market securities.

Dividends Discretionary cash distributions paid out of earnings by a corporation to holders of shares of its stock.

Dollar-cost averaging A practice of making regular periodic investments in stocks over a period of time.

Dollar-weighted rate of return The rate of return on investment taking into account the amounts added or withdrawn from the investment during the time period (also called the internal rate of return).

Dow (Dow Jones Industrial Average) A composite index of the prices of 30 large company stocks chosen by the Dow Jones Company, weighted by their share prices.

Earnings The profits earned by a corporation; corporate revenues after the deduction of costs and expenses.

Efficient frontier A theoretical curved line on the graph of portfolio return versus risk, representing the portfolio with the highest expected return at each level of risk (as computed using Markowitz’s mean-variance optimization methodology).

Efficient market theory The theory that the market prices securities efficiently—that is, that market prices reflect all available information.

Endowment funds Institutional investment funds owned by nonprofit organizations such as universities and foundations.

Equal weighting The weighting of the securities in a portfolio or market index (i.e., a hypothetical portfolio). The portfolio or index is equal-weighted if the weight of every security in the portfolio is the same—that is, if an equal dollar amount of every security is owned by the portfolio. See also capitalization weighting.

Equity A form of security (also called a share, or shares, of stock) in which the investor takes a percentage ownership of a company in return for his or her investment. Therefore, the capital provided by the investor to the company is repaid by the dividends that are paid out as a portion of the earnings of the company, or by the sale of the investor’s ownership at some future time. In comparison with the stream of payouts to the investor from a conventional bond, which are fixed, the payouts to the equity investor are varying and uncertain.

Equity-linked contracts A form of investment contract in which a security and an option are combined in such a way as to give the investor a combination of an equity-linked return (i.e., the value of the investor’s investment goes up if the value of the underlying equity security goes up), while the investor is also guaranteed a specified minimum return over a given time period.

ETF. See Exchange-traded fund.

Exchange-traded fund (ETF) A mutual fund (often an index fund) that is structured in such a way as to mimic a security—it can be bought or sold at any time of day, unlike a mutual fund which can usually be bought or sold only once a day. ETF index funds may, under certain circumstances, be lower in cost than ordinary mutual funds.

Expense ratio The ratio of the annual expenses of a mutual fund (exclusive of trading costs and front-end or back-end loads) to the value of the fund.

Factor model A model used to construct portfolios with certain risk characteristics, or to track specific indexes.

Financial planner (or Financial advisor) An advisor to individuals on investments and usually on other aspects of an individual’s finances, such as taxes, insurance, and estate planning. See also www.sec.gov/answers/finplan.htm.

Fixed income Securities that offer a fixed or steady stream of payouts over time in return for an initial investment. See also Bond.

Full-service broker A broker who offers investment research and advice as well as the service of buying and selling securities.

Fund of funds A fund in which an investor can invest that allocates its assets among other funds, adding an additional fee for the service of selecting and packaging the funds into one.

Fundamental indexing A way of automating the picking of stocks by eliminating the “expert” and using a simple algorithm instead. Fundamental indexing algorithmically “tilts” the portfolio, usually toward value stocks because value stocks have often performed better in the past.

Gains. See Capital gains.

Growth stocks Stocks in companies that are expected to have high future earnings growth rates and therefore, as opposed to value stocks, have high price-to-earnings and price-to-book value ratios.

Hedge funds Any mutual fund-like pooled funds that are unregulated or relatively unregulated because their investors are limited in number and kind (see Accredited investor), and are therefore presumably “sophisticated” and not needing government oversight. See also www.sec.gov/answers/hedge.htm.

Hedging Any method of using securities or (more commonly) derivative securities to provide insurance against the risk of loss in a portfolio of securities. Some hedge funds use hedging, some do not.

Incentive fee. See Performance incentive fee.

Index fund A fund that includes securities only according to a predetermined formula dictating the proportion of the securities in the portfolio, without regard to the current or past characteristics of the companies whose shares are owned. True index funds have very low expense ratios because they don’t pay anyone to analyze and choose securities for inclusion.

Institutional investors Investors that are entities such as corporate pension funds or nonprofit organization endowment funds.

Institutions. See Institutional investors.

Investment advisor “Someone who receives compensation for giving individually tailored advice to a specific person on investing in stocks, bonds, or mutual funds.” See also www.sec.gov/answers/invadv.htm.

Investment assets. See Assets.

Investment managers Companies that select and invest portfolios of securities for clients (also called “money managers”). Can be mutual fund managers or managers of separately managed accounts, such as for institutional investors or high-net-worth individual investors. Investment advisors and consultants often (for a fee, of course) select and recommend investment managers to investors. Investment management firms are among the most profitable, and most consistently profitable, firms in all of industry—in spite of the fact that on average they do not beat an ultra-low-cost, buy-and-hold portfolio. Similarly, investment advisors and consultants exhibit no ability to select investment managers who will outperform a buy-and-hold strategy.

Investment performance Generally means the risk-adjusted rate of return on investment.

Least-squares fit See Regression analysis.

Leverage The extent to which an investment is financed by borrowing.

Liquidity The ease and timeliness with which a security can be sold.

Load A one-time fee for purchase or sale of a mutual fund, either a front-end load or a back-end load.

Long-short equities A strategy of buying stocks that an investment manager believes will do better than the market average while simultaneously “shorting” stocks (borrowing them in order to sell them) that the manager believes will do worse than the market average. If the manager buys and sells equal dollar amounts, then in the unlikely event that the manager is correct in both these beliefs, he or she will do well no matter whether the market average goes up or down.

Market average The investment performance of a hypothetical portfolio containing all marketable stocks, or a specific subset of all marketable stocks (e.g., U.S. domestic stocks), usually capitalization-weighted but sometimes equal-weighted or weighted by the price per share (as in the Dow).

Mean-variance optimization An optimization algorithm proposed by Harry Markowitz in which security (or asset class) weightings within a portfolio of securities are derived in such a way as to maximize the expected return of the portfolio for a given level of variability (or standard deviation) of periodic returns. Application of the method stumbles on the fact that accurate prospective values for the inputs can’t be obtained.

Modern portfolio theory (MPT) The body of mathematical theories of investment, generally including Markowitz’s mean-variance analysis and the Sharpe et al. capital asset pricing model.

Money managers. See Investment managers.

MPT. See Modern portfolio theory.

Mutual fund “A company that pools money from many investors and invests the money in stocks, bonds, short-term money-market instruments, or other securities.” See also www.sec.gov/answers/mutfund.htm.

Option A transaction reserving to the buyer of the option the right—for a price—to make a specified future purchase, or a specified future sale, at a specified future price, and within a specified future time period or at a specified future time.

Passive management The practice of investing a portfolio of securities according to a formula that dictates the makeup of the portfolio, without regard to the characteristics of the companies underlying the securities; as opposed to active management, which attempts to pick securities based on the characteristics of the companies that issued them.

Pension funds Funds sponsored by corporations or governments dedicated to providing retirement and other benefits to employees.

Performance fee. See Performance incentive fee.

Performance incentive fee A fee paid to an investment manager that depends on the rate of return earned over a time period (usually a year) by the manager. A common performance incentive fee is 20% of gains. Because the manager reaps fees in case of gains but returns nothing to the investor in case of losses, performance fees are called “asymmetric.” “Asymmetric” performance incentive fees (fees charged on gains but not rebated on losses) aren’t allowed for mutual funds but are allowed for hedge funds.

Portfolio optimization See Mean-variance optimization.

Price/earnings ratio The ratio of the price per share of a stock to the earnings per share; can be different depending on the time period over which the earnings are calculated (or projected) and the definition of earnings.

Rate of return on investment The percentage by which an investment grows (positive) or shrinks (negative) over a specific time period. See also time-weighted rate of return and dollar-weighted rate of return.

Rationality A vague term applying to assumed investor characteristics, usually interpreted to mean, minimally, that investors prefer a higher return on investment to a lower return, and a lower risk to a higher risk.

Real Estate Investment Trusts (REITs) “Entities that invest in different kinds of real estate or real estate related assets, including shopping centers, office buildings, hotels, and mortgages secured by real estate.” See also www.sec.gov/answers/reits.htm.

Regression analysis The simplest statistical method of choice for pum-meling data to see if there are any patterns in the data. Also known in elementary mathematics classes as “least-squares fit.”

Regression toward the mean A tendency for a wandering random variable to eventually move back toward its average value.

REITs. See Real Estate Investment Trusts.

Return. See Rate of return on investment.

Risk assessment A routine procedure applied by investment advisors usually involving a brief questionnaire to categorize an investor-client according to how much investment risk the client is able or willing to assume.

Risk premium The increase in expected annual percentage return on a risky investment in excess of the return on a (virtually) riskless investment such as U.S. Treasury bills.

Risk-adjusted performance The rate of return on an investment, adjusted for the risk of the investment. For example, if the risk of an investment of $100 is leveraged 2-to-1 by borrowing $100, thus making an investment of $200, and returns 10% on the original $100, its risk-adjusted return is reduced by a factor of 2, resulting in a risk-adjusted performance of 5%. Several methods have been proposed for expressing risk-adjusted performance.

S&P 500. See Standard & Poor’s 500.

Security In investment, a contract or document showing ownership of a future contingent cash flow stream received in exchange for the investment of capital; usually a stock or a bond.

Self-selection bias Self-selection bias arises in any situation in which individuals select themselves into a group.

Socially responsible investments Equity investments that are selected in part by screening the issuing companies for certain corporate practices that are deemed socially responsible by the selector.

Standard & Poor’s 500 A capitalization-weighted index of 500 of the largest U.S. companies’ stock prices; currently comprises over 70% of the total capitalization of all U.S. companies.

Standard deviation A statistical measure of the variation or dispersion of a set of numbers; in investments, a measure of the variation or dispersion of periodic rates of return (e.g., daily or monthly)—that is, the volatility or variability of returns.

Stock. See Equity.

Stock market Any marketplace in which shares of stock are bought and sold.

Style allocation Usually, the percentages—adding to 100 percent—by which the equity component of an investment portfolio is divided among major equity asset classes such as large growth stocks, large value stocks, small growth stocks, small value stocks, international stocks, and emerging-market stocks; can also apply to the categories of bonds and real estate. See also Asset allocation.

Style drift A manager’s portfolio exhibits style drift if it starts to look different from the style the manager was supposed to manage.

Survivorship bias Bias in a database of investment funds over a historical time period due to the fact that data for funds that didn’t exist at the end of the time period aren’t included in earlier periods.

Target-date funds Mutual funds that change their asset allocation as the investor ages, generally investing a higher proportion in stocks when the investor is young and a lower proportion in stocks as the investor ages.

Time-weighted rate of return The rate of return on investment over a specific time period if the investor made no contributions or withdrawals from that investment.

TIPS (Treasury Inflation-Protected Securities) Securities issued by the U.S. Treasury similar to ordinary government bonds except that their principal values and interest payments are adjusted for inflation. See also www.treasurydirect.gov/indiv/products/tips_glance.htm.

Trading volume The number of shares traded per day.

Transaction cost The cost of buying or selling a stock or a bond, including brokerage commissions. When institutions (e.g., mutual fund companies, pension funds, investments banks, and hedge funds) make large trades, costs may include losses due to a change in the market because of the trade.

Turnover The percentage of the investments in a portfolio that is bought or sold annually. “Buy-and-hold” and passively managed portfolios generally have low turnover, while actively managed portfolios generally have higher turnover.

Value stocks Stocks in companies that have low price-to-earnings and price-to-book value ratios.

Variance Statistically, the square of the standard deviation; a measure of the variability of periodic rates of return.

Volatility The variability, or degree of fluctuation, of periodic rates of return, usually measured statistically by standard deviation or variance, and considered a proxy for risk.

Wealth management. See Investment advisor.

ACKNOWLEDGMENTS

Michael would like to thank Robert Huebscher, CEO of Advisor Perspectives, for publishing many of his articles and for allowing the use of portions of their content in this book.

We wish to thank the four reviewers engaged by Berrett-Koehler Publishers to review an early draft of the book: Kathy Scheiern, David Marshall, John Tobin, and John K. Romano. Their insightful comments helped us greatly with the book’s revision.

We also wish to thank our friends Karin Hibma and Kevin O’Brien, who attended our Author’s Day lunch at Berrett-Koehler and generously provided valuable feedback. And we wish to thank the two guests invited by Berrett-Koehler, Jeff Chow and Ian Mitroff, with whom we had enlightening conversations.

Most especially, we wish to thank Berrett-Koehler’s exemplary editorial, production, publicity, and marketing teams: María Jesús Aguiló, Charlotte Ashlock, Marina Cook, Michael Crowley, Kat Engh, Kristen Frantz, Kylah Frazier, Catherine Lengronne, Zoe Mackey, Neal Maillet, David Marshall (doubly for also being an insightful reviewer), Dianne Platner, Courtney Schonfeld, Katie Sheehan, Johanna Vondeling, and Rick Wilson; and also a particular thank-you to our tireless and expert copy editor and production manager Laura Larson and Michael Bass.

And a very special thank-you to Berrett-Koehler’s inimitable founder, president, and publisher, Steve Piersanti, who also served as our editor, and to Berrett-Koehler’s managing director Jeevan Sivasubramanian, who shepherded us through the whole process. Michael has had the good fortune to work with Steve and Jeevan and their excellent Berrett-Koehler team on two occasions now. We do not know how a publisher could be more helpful and author-friendly.

INDEX

401(k) plans, 29, 127, 135–141, 209

403(b) plans, 209

Absolute return hedge funds, 124

Accredited investors, 158, 209

Active investments, 29, 114–115, 123, 209

Advice on investments, tune it out recommendation on, 71–72

in deadly temptations, 73–190

Advisor Perspectives, 42

Age

and asset allocation, 127–128, 130

at death, planning for, 59

at retirement, 53

and risk reduction, 72, 127–128

at start of saving and investing, 51, 60–63

AIG Financial Products division, 40–41

Alpha value of portfolio, 182, 187, 188, 209

Alternative Investment Management Association, 159

Alternative investments, 45–47

Annual percentage rate on loans, 187

Annuities, 32, 59

deferred income, 39–40

equity-linked, 39

risk of, 40–42

single-premium income, 10, 30, 38–42

variable, 39

Asset, definition of, 209

Asset allocation, 105–118, 209

age affecting, 127–128, 130

capital asset pricing model on, 183, 210–211

in constant-mix strategies, 130–132

in diversification myth, 123–124

fees for, 5, 107, 115–117, 121

glide path in, 128–129

of institutional investors, 167, 168–169

Markowitz theory on, 108–112, 115, 117

100 minus your age formula for, 128, 130

optimization of, 112, 118

rebalancing of, 71, 141–147, 153

services of financial firms for, 113–114, 115–117

style in. See Style allocation target-date funds in, 128

tracking error in, 142, 168

tuning out noise on, 71

and variability of investment performance, 105–107

Asset classes, 29, 72, 107, 112

diversification of, 123–124

Asset-liability models, 209

Asymmetric fees, 162, 209, 216

Australia, historical performance of stocks in, 18

Back-end bias, 160

Backfill bias, 160, 210

Backtest, 210

Bakija, Jon, 196

Bank Administration Institute, 100

Banz, Rolf, 186–187, 188

Barras, Laurent, 80

Basis point, 210

Beating the market temptation, 71, 75–94

in active management, 114–115, 123

anecdotal history of star performers on, 81–84

in day trading, 90–91

in dollar-cost averaging, 147–151

in institutional investments, 169

in long-short equities, 124–125, 158

monitoring of investment performance in, 93–94

in quantitative investment strategies, 172

in random nature of stock market changes, 75–80

statistical analysis of performance in, 87

A. G. Becker & Co., 86

Beebower, Gil, 105, 106

Behavioral economics, 210

Behavioral finance, 210

Bernstein, William, 20

A. M. Best rating, 41

Beta strategies, 174

Beta value of portfolio, 182, 210

Bias, 210

back-end, 160

backfill, 160, 210

selection, 160

self-selection, 160, 218

survivorship, 160, 218

The Big Investment Lie (Edesess), 77, 83, 86, 164

BlackRock, Inc., 34, 172

Bodie, Zvi, 53

Bogle, John, 82–83

Bonds, 30, 210

corporate. See Corporate bonds

domestic, 30, 37

in exchange-traded funds, 38, 43–44

expected rate of return on, 13–14, 14t, 16–17

individually purchased, 30, 37–38

international, 30

laddering of, 38, 61–63, 67–68

maturity of, 16, 38

in mutual funds, 38, 43–44

percentage in portfolio, 106, 107, 141–147. See also Asset allocation

and rebalancing portfolio, 141–147

risk of, 17, 37, 43, 57

Treasury, 14t, 16–17, 143

in two-asset portfolio, 61–63, 67–68

yield of, 16

Book value, 175, 210

and market-to-book value ratio, 117

Brealey, Richard A., 75

Brinson, Gary, 105, 106, 107

Brokerage, 210

directed, 212

Brokers

discount, 212

full-service, 214

Buffet, Warren, 56

Buy-and-hold strategy, 79, 115, 210

compared to rebalancing, 145–146

California Public Employees’ Retirement System, 169

Call option, 210

Canada, historical performance of stocks in, 18

Capital asset pricing model, 183, 210–211

Capital gains, 60, 211

Capitalization weighting, 175–176, 177, 178–180, 211

Carhart, Mark M., 80

Cash equivalents in asset allocation, 106, 107

Cash flow

with insurance products, 38–40

laddering of bonds for, 38, 61–63, 67–68

in retirement. See Retirement income

Cass Business School study, 179–180

Catastrophe bonds, 46

Charting, 87–91

China, historical performance of stocks in, 53

City University London Cass Business School, 179–180

Closet index portfolio, 122, 123, 133

Coin toss results, 83, 88, 204n18

Cole, Adam, 196

Collateralized debt obligations, 40–41, 199

Commissions, 211

Computer models in quantitative investment strategies, 172–174

Concentration of investments

compared to diversification, 65–66

in corporate bonds, 38

Constant-mix strategy, 127

compared to target-date funds, 127, 130–132, 131t, 133

Consultants, 211

to institutional investors, 166–169

Continuously compounded rate of return, 188, 189

Contracts

equity-linked, 213

rate of return specified in, 15–16

Cootner, Paul, 78

Corporate bonds, 37

concentration of investment in, 38

expected rate of return, 14t, 17

risk of, 17, 43, 57

Correlation, 211

of securities in Markowitz theory, 108–112

Covariance, 211

Cowles, Alfred, 77

Cowles Report, 77–78

Credit default swaps, 40–41, 199

Credit intermediation, 1

DALBAR reports, 97–103

Data

mining of, 211

overfitting of, 80

Da Vinci, Leonardo, 9

Day trading, 90–91, 211

Deadly temptations in investing, 73–190

Debt, 211–212

and collateralized debt obligations, 40–41, 199

and equity ratio, 212

Deferred-income annuities, 39–40

Defined-benefit pension plan, 136, 212

Defined-contribution pension plan, 136, 212

Derivatives, 163

“Determinants of Portfolio

Performance” (Brinson, Hood, and Beebower), 105–106

Directed brokerage, 212

Direct investments, 46

Discount brokers, 212

Discount rate, 212

Diversification, 43, 119–126, 212

alternative investments in, 45–47

compared to concentration, 65–66

fund of funds in, 164

hedge funds in, 124–126, 133

individually purchased stocks and bonds in, 37–38

maximum, 115

modern portfolio theory of, 119–121

mutual funds in, 121–123, 133

rate of return in, 43–44

risk in, 43–44, 108, 111, 119–120

in style or asset class, 123–124

tuning out noise on, 71

world stock index funds in, 33–34

Dividends, 212

taxes on, 60

Dollar-cost averaging, 147–151, 148f, 153, 212

compared to lump-sum investing, 149–151, 150t

Dollar-weighted rate of return, 212

compared to time-weighted rate, 100–102, 101f, 102f

Domestic bonds, 30

Domestic equity index funds, 9–10, 29

in combination with world stock index funds, 34–35, 36, 58

expense ratios in, 34–35

Domhoff, G. William, 193–194

Don’t Count on It (Bogle), 83

Dow Jones Industrial Average, 79, 213

Down and Out in Beverly Hills (movie), 156

Earnings, 213

Econometrica journal, 77

Economic downturn (2007-2009), 21, 129, 189, 191, 198–199

The Economist, 83, 160, 179

Edesess, Michael, 77, 81, 85–87, 156, 164

Efficient frontier, 108–110, 109f, 111, 112, 113, 118, 120, 213

Efficient markets, 76, 79, 185, 213

Einstein, Albert, 23, 24

Endowment funds, 213

management of, 166–168

Equal weighting, 176, 179, 213

Equities, 29, 213

book value of, 175, 210

capitalization of, 175–176, 211

and debt/equity ratio, 212

in domestic equity index funds, 9–10, 29, 34–35, 36, 58

individually purchased, 30, 36–37

long-short, 124–126, 158, 215

percentage in portfolio, 106, 107, 141–147. See also Asset allocation

random movements in price changes, 75–80

rate of return on, 14, 14t, 17–20

and rebalancing portfolio, 141–147

risk premium for, 18

value stocks, 175, 177, 183–185, 219

in world stock index funds. See World stock index funds

Equity-linked contracts, 213

Erb, Claude B., 152

E*TRADE, 34

Europe, historical performance of stocks in, 18

EVestment Alliance LLC, 186

Exchange-traded funds, 30, 213

bonds in, 38, 43–44

compared to mutual funds, 35–36, 213

domestic, 9–10, 29, 34–35

expense ratios in, 34–35

international, 10, 29–30, 33–36, 115. See also World stock index funds

number of products available, 27

risk and rate of return, 43–44, 56

Expense ratios, 34–35, 214

of 401(k) plans, 140

of individually purchased stocks, 37

of mutual funds, 35, 36, 45, 79, 122

of socially responsible investments, 45

of star performers, 82

Factor models, 180–186, 214

of Fama and French, 180, 183–185

of Goldman Sachs, 185–186

regression analysis of, 180–185

False Discovery Rate test, 80

Fama, Eugene, 180

Fama and French factor model, 180, 183–185

Federal income taxes. See Taxes

Federal Reserve, 163

Fees

for asset allocation advice, 5, 107, 115–117, 121

asymmetric, 162, 209, 216

commissions in, 211

of consultants to institutional investors, 166

cumulative cost of, 95–96

in day trading and at-home trading, 91

of exchange-traded funds, 35–36

and expense ratios, 34–35. See also Expense ratios

in 401(k) plans, 139–141

in funds-of-funds, 164

in hedge funds, 126, 133, 159, 161, 162–163, 164

in individually purchased stocks, 37

for institutional investors, 166, 169

in insurance products, 39

load, 215

of mutual funds, 35–36, 78–79, 122, 193

performance incentive, 209, 216

as predictive of performance, 80, 86, 155

price confusion in, 3–4, 5

and profits in financial industry, 2–6, 191–200

in target-date funds, 127

and transaction costs, 219

in world stock index funds, 34–35

Fidelity Capital Fund, 83

Financial advisors, 28, 214

asset allocation services of, 5, 107, 113–114, 115–117

cumulative cost of fees paid to, 95–96

DALBAR reports on investment performance of, 97–103

tuning out noise about, 72

Financial Analysts Journal, 105, 117, 118

Financial crisis (2007-2009), 21, 129, 189, 191, 198–199

Financial industry, 1–9, 191–200

increasing complexity of products and services in, 198–199

invisible squeeze on, 191–200

misinformation and deception in, 8

political influence of, 191, 193

product and price confusion in, 3–5, 8, 28, 192–193

profits in, 2–6, 191–200

rise of superrich in, 193–198

silo effect in, 6–7

systemic risk in, 20–21, 191, 193, 198–199

use of term, 1–2

Financial planners, 214. See also Financial advisors

Financial Times, 147, 159, 160

Finland, historical performance of stocks in, 18

Fitch rating, 41

Fixed income, 29, 214

Forbes, 82

Foundation funds, management of, 166–168

401(k) plans, 29, 127, 135–141, 153, 209

expense ratios of, 140

fees in, 139–141

problems with maximum funding of, 135–141

risk in, 136–137

and target-date funds, 127

tax deductions for contributions to, 137–138, 139

403(b) plans, 209

France

government inflation-protected securities in, 33

historical performance of stocks in, 18

French, Ken, 180

Full-service brokers, 214

Fundamental indexing, 176–177, 180, 214

Fund of funds, 163–165, 214

fees in, 164

performance of, 165

Future performance of investment

expected rates of return in, 13–20, 14t

and look only forward rule, 49–69

prediction of. See Prediction of future performance

Gains (capital gains), 60, 211

Germany, historical performance of stocks in, 18

“Getting by” investors, 10, 11–12

one-asset portfolio for, 57–58

Glide path, 72, 128–129

Global Alpha hedge fund, 165

Global financial crisis (2007-2009), 21, 129, 189, 191, 198–199

Global government inflation-protected securities, 32–33

Global stock index funds. See World stock index funds

Gold, investing in, 71, 151–153

Goldberg, Rube, 166–167

“The Golden Dilemma” (Erb), 152

Goldman Sachs, 4–5, 20, 165

factor model of, 185–186

Government inflation-protected securities (GIPS), 9, 29, 30–33, 43

global diversification in, 32–33

safety of, 50

Treasury inflation-protected securities as. See Treasury Inflation-Protected Securities

in two-asset portfolio for well-off investor, 53–57, 59–68

Greenwich Associates, 172

Greenwood, Robin, 1, 6

“The Growth of Modern Finance” (Greenwood and Scharfstein), 1

Growth stocks, 214

Hamilton, William Peter, 78

Harvard University, 161

Harvey, Campbell R., 152

The Hedge Fund Mirage (Lack), 159

Hedge funds, 45–46, 157–165, 170, 214

absolute return, 124

biases and errors in reports on, 160

in diversification myth, 124–126, 133

fees in, 126, 133, 159, 161, 162–163, 164

in fund-of funds, 163–165

long-short equity, 124–126

number of products available, 27, 157

performance of, 159–160, 161, 165

seeding of, 159, 161–162

Hedging, 214

Heim, Bradley, 196

Historical performance, 14t, 15, 18, 49–50

charting of, 87–91

of dollar-cost averaging, compared to lump-sum investing, 150t, 150–151

of hedge funds, 159–160

lack of correlation to future performance, 49–50, 84–87, 177

of mutual funds, 78–79, 80, 84–85, 125–126

random movements of price changes in, 75–80

in rebalancing, 145–146

SEC on predictive value of, 85

of small stocks, 179–180, 186–189

of Standard & Poor’s 500 index, 103, 103f

of star performers, 81–84

in TIPS/global stock index combination, 54t, 55–56

trends in, 86–87, 92

of world stock index funds, 14t, 18, 53, 55–56

Holding period return, 188, 189

Hood, Randy, 105, 106

Incentive fees, 216

asymmetric, 209, 216

Income

and categories of investors, 10–12

in finance industry, 193–198

fixed, 29, 214

in retirement. See Retirement income

single-premium income annuities, 10, 30, 38–42

taxes on. See Taxes

Index funds, 33–34, 214–215

based on specialized indexes, 174–180

capitalization-weighted, 175–176, 177, 178–180, 211

domestic, 9–10, 29, 34–35, 36, 58

equal-weighted, 176, 179

exchange-traded, 35–36

in fundamental indexing, 176–177

of institutional investors, 169

international stocks in. See World stock index funds

mutual funds in, 35–36

performance of, 178

style, 116

total market, 116, 118, 120–121, 123

Indirect investments, 28–29

Individual Development Accounts, 13

Individually purchased stocks and bonds, 30, 36–38

Inflation

calculating rate of, 32, 202n2

gold investment as hedge against, 71, 151–153

government inflation-protected securities, 9, 29, 30–33. See also Government inflation-protected securities

rate of return adjusted for, 13, 14t. See also Real rate of return

Insider trading, 89

Institutional investors, 10–11, 158, 170

alternative investments of, 46

consulting to, 166–169

definition of, 215

fees paid by, 166, 169

hedge fund investments of, 158, 163–164

index funds of, 174

Insurance industry, 1

rating of companies in, 41–42

Insurance products, 38–42

guarantee limits in, 42

risk of, 40–42

single-premium income annuities, 10, 30, 38–42

term life insurance, 10, 30, 38–42

Interest payments, taxes due on, 60

International bonds, 30

International government inflation-protected securities, 32–33

International stock index funds. See World stock index funds

Investment advisors, 215

Investment managers, 215

of pension, endowment, or foundation funds, 166–169

selection of, 167–168

of value stocks, 175

Investment performance, 215. See also Performance of investment

Investors

accredited, 158, 209

average, DALBAR report on returns of, 98–99

categories of, 10–12

“getting by,” 10, 11–12, 57–58

institutional, 10–11, 215. See also Institutional investors

reasonably well-off, 10, 11, 51–57, 59–68

struggling, 10, 12, 147

superrich, 10–11, 21. See also Wealthy investors

Invisible hand, 191–192, 200

iShares, 34

Italy, historical performance of stocks in, 18

Japan, historical performance of stocks in, 18, 53

Jensen, Michael C., 79

Jones, Alfred Winslow, 124, 158

Journal of Finance, 108

Journal of Financial Economics, 186–187

Journal of Portfolio Management, 142

JPMorgan, 159, 161

Kay, John, 147, 149

Kendall, Maurice, 75, 78

Kitces, Michael, 99

Lack, Simon, 159, 161, 165

Laddering of bonds, 38

in two-asset portfolio, 61–63, 67–68

Laise, Eleanor, 173, 174

Large value funds, 116

Least-squares fit, 181, 181f, 182f, 215

Leverage, 215

Life and Health Guaranty Association, 42

Life insurance

risk of, 40–42

term, 10, 30, 38–42

Liquidity, 215

Load, 215

and no-load mutual funds, 35

Long-short equities, 124–126, 158, 215

Long-Term Capital Management hedge fund, 163

Look only forward rule, 49–69

Low income population

federal incentives for saving and investing, 13

as struggling investors, 12

Lump-sum investing, compared to dollar-cost averaging, 149–151, 150t

MacKillop, Scott, 187

Madoff, Bernie, 11, 82

Malkiel, Burton, 77

Managers of institutional funds, 166–169

selection of, 167–168

Market average, 215–216

Market-to-book value ratio, 117

Market weighting (capitalization weighting), 175–176, 177, 178–180, 211

Markowitz, Harry, 108

on asset allocation, 108–112, 115, 117

on diversification, 119–120

on risk and rate of return, 109–110, 120

Maturity of bonds, 16, 38

laddering in, 38, 61–63, 67–68

Mean-variance optimization, 216

Metropolitan Life, 41

Miller, Bill, 81–82

Miller, Gary, 187

Miller, Merton, 79

Modern portfolio theory, 65–66, 119–121, 216

capital asset pricing model in, 183

rebalancing in, 144–145, 146

risk-adjusted performance in, 146

Modern scientific financial theories, 171–190. See also Scientific financial theories

Momentum effect, 92–93, 184

Money managers. See Investment managers

Monitoring of investment performance, 72, 93–94

Moody’s, 41

Morningstar Inc., 122, 173, 186

Mortgage securities

overvaluation of, 165

subprime, 11, 21, 198

Multi-factor models, 180–186, 214

Mutual funds, 10, 29–30, 216

bonds in, 38, 43–44

compared to exchange-traded funds, 35–36, 213

in diversification, 121–123, 133

domestic, 9–10, 29

expense ratios of, 35, 36, 45, 79, 122

fees of, 35–36, 78–79, 122, 193

historical performance of, 78–79, 80, 84–85, 125–126

international, 10, 29–30

no-load, 35

number of products available, 27

rate of return, 43–44, 98–99

risk of, 43–44

socially responsible, 45

star performers in, 82–83

target-date, 218

time-weighted and dollar-weighted measures of performance, 100–102

Mutual of Omaha, 41

Myers, Stewart C., 75

National Bureau for Economic Research, 195

National Organization of Life and Health Guaranty Associations, 42

New York Times, 80

New Zealand, historical performance of stocks in, 18

Nixon, Richard, 152

Nobel Prize in economics, 78, 108

No-load mutual funds, 35

Nominal dollars, 13

in income annuities, 38–42

Nominal rate of return, 13, 14t

in bonds, 14t, 16–17

in equities, 14t, 19

in two-asset portfolio for well-to-do investor, 54t, 54–55

Norway, historical performance of stocks in, 18

One-asset portfolio, 57–58, 68

Opinions on investments, tune it out recommendation on, 71–72

in deadly temptations in investing, 73–190

Optimization, 105, 112, 118

of institutional investments, 167

mean-variance, 216

tuning out noise on, 71

Options, 163, 216

call option, 210

Osborne, M. F. M., 78

Overfitting of data, 80

Passive investment, 28–29, 114–115, 216

Past performance. See Historical performance

Paulson, John, 164–165

Pellegrini, Paolo, 165

Pension funds, 136, 166–169, 216

in defined-benefit plan, 136, 212

in defined-contribution plan, 136, 212

fund-of-funds investments of, 165

hedge fund investments of, 157–158, 165

management of, 166–169

Performance incentive fees, 216

asymmetric, 209, 216

Performance of investment, 215

in absolute return funds, 124

asset allocation affecting variability in, 105–107

beating the market in, 75–94. See also Beating the market temptation

in buy-and-hold policy, 79

charting of, 87–91

DALBAR reports on, 97–103

in day trading, 90–91

in dollar-cost averaging, 147–151

Fama and French studies of, 183–185

fees as indicator of, 80, 86, 155

in fund-of-funds, 165

in Goldman Sachs funds, 186

in hedge funds, 159–160, 161, 165

historical. See Historical performance

in index funds, 178

in long-short equities, 124–126

and look only forward rule, 49–69

in Markowitz theory, 108–112, 120

momentum effect in, 92–93, 184

monitoring of, 72, 93–94

prediction of. See Prediction of future performance

in quantitative investment strategies, 172–174

rate of return in. See Rate of return

on investment in rebalancing, 145–147

reversion to the mean in, 92–93

risk-adjusted, 68, 146, 217

in small stocks, 179–180, 183–189

in Standard & Poor’s 500 index, 103, 103f

of star performers, 81–84

time-weighted and dollar-weighted measures of, 100–102, 212, 218

trends in, 86–87, 92

in value stocks, 183–185

variability in, 105–112, 120

Philippon, Thomas, 195, 196, 197

Piketty, Thomas, 196

Portfolio

alpha value of, 182, 187, 188, 209

annuities in, 32, 38–42, 59

asset allocation in, 105–118. See also Asset allocation

avoiding temptations in advice on, 73–190

beta value of, 182, 210

concentration of, 38, 65–66

core investment products in, 9–10, 27–47

diversification of. See Diversification

efficient frontier of, 108–110, 111, 112, 113, 118, 120, 213

for “getting by” investor, 57–58

index or closet index, 122, 123, 133

of institutional investors, 167, 168–169

looking forward to future performance of, 49–69

modern portfolio theory of. See Modern portfolio theory

with one asset, 57–58, 68

optimization of. See Optimization

precise calibration of, 105–118

in quantitative investment strategies, 172–174

rebalancing of, 71, 141–147, 153

risk and return of. See Risk and rate of return

risk of whole portfolio, 108

simplifying options in, 27–49

style allocation in. See Style allocation

tuning out noise about, 71–72

with two assets, 51–57, 59–68

variability in performance, 105–112, 120

for well-off investor, 51–57, 59–68

“Portfolio Selection” (Markowitz), 108

Portfolio X-Ray program, 122

Prediction of future performance, 75–94

beating the market in, 75–94

charting in, 87

in day trading, 90–91

fees as factor in, 80, 86, 155

limitations of historical performance in, 49–50, 84–87, 177

in long-short equities, 124–126

in Markowitz theory, 108–112

momentum effect in, 92–93

Price confusion, 3–4, 5, 8, 28, 192–193

with insurance products, 39

Price/earnings ratio, 217

Principal Financial Group, 41

Principles of Corporate Finance (Brealey and Myers), 75

Product confusion, 4–5, 28, 192–193

with insurance products, 39

Profits in financial industry, 2–6, 191–200

in product and price confusion, 3–5, 8, 192–193

and rise of superrich, 193–198

Prudential Insurance Company, 41

Quadratic programming, 110

Quantitative Analysis of Investor Behavior, DALBAR report on, 87–93

Quantitative investment strategies, 172–174

RAND Corporation, 110

The Random Character of Stock Market Prices (Cootner), 78

A Random Walk Down Wall Street (Malkiel), 77

Rate of return on investment, 12–20, 217

in absolute return funds, 124

asset allocation affecting, 105–107

beating the market in, 75–94. See also Beating the market temptation

in bonds, 13–14, 14t, 16–17

in concentration of investments, 65–66

continuously compounded, 188, 189

contract information on, 15–16

DALBAR reports on, 97–103

in dollar-cost averaging, 147–151

dollar-weighted, 100–102, 212

in equities, 14, 14t, 17–20

expected, 13–20, 14t

in fund-of-funds, 165

in Goldman Sachs funds, 186

in hedge funds, 159–160, 161, 165

historical. See Historical performance

holding period return in, 188, 189

inflation-adjusted, 13, 14t. See also Real rate of return

in long-short equities, 124–126

in Markowitz theory, 108–112, 120

methods in calculation of, 187–189

nominal, 13, 14t. See also Nominal rate of return

prediction of. See Prediction of future performance

rebalancing affecting, 145–147

and risk. See Risk and rate of return

in small stocks, 186–189

for star performers, 81–84

time-weighted, 100–102, 218

in Treasury Inflation-Protected Securities, 16–17, 33, 54–55, 56

in world stock index funds, 14t, 18–20, 33, 54–56, 64–65

Rationality, 217

Real dollars, 13

in income annuities, 38–42

Real estate investment trusts, 30, 44, 217

Real rate of return, 13, 14t

in bonds, 14t, 17

in equities, 14t, 19

historical average, 15

in two-asset portfolio for well-to-do investor, 54t, 54–55

Reasonably well-off investors, 10, 11

two-asset portfolio for, 51–57, 59–68

Rebalancing, 141–147, 153

compared to buy-and-hold strategy, 145–146

rate of return in, 145–147

risk in, 143–145, 146

tuning out noise on, 71

Regression analysis, 180–189, 217

in Fama and French studies, 183–185

least-squares fit in, 181, 181f, 182f

of small stock performance, 187, 188–189

Regression toward the means, 217

Reshef, Ariell, 195, 196

Retirement income

in constant-mix strategy, 130–132

401(k) plans for, 135–141

for “getting by” investors, 11–12, 57–58, 68

hedge funds for, 157–158

pension funds for, 136, 166–169. See also Pension funds

rebalancing affecting, 143–144

target-date funds for, 126–132

taxes on, 59–60

TIPS ladder for, 61–63, 66

two-asset portfolio for, 54–57, 60, 61, 66–68

for well-off investors, 11, 53, 54, 60, 61, 63, 66–68

Return. See Rate of return on investment

Reversion to the mean, 92–93

Risk, 119–133

and age, 72, 127–128

assessment of, 217

and asset allocation, 105

of bonds, 17, 37, 43, 57

in concentration of investments, 65–66

diversification of, 43–44, 108, 111, 119–120

of equities, 17–18

in 401(k) plans, 136–137

in gold investments, 152

of insurance products, 40–42

and rate of return. See Risk and rate of return

in rebalancing, 143–145, 146

systemic, in global financial system, 20–21, 191, 193, 198–199

in target-date funds, 129, 132, 133

of TIPS, 31–32

of whole portfolio, 108

Risk-adjusted performance, 68, 146

alpha value in, 182, 187, 188, 209

definition of, 217

of small stocks, 187

Risk and rate of return, 43–44, 50–51, 217

adjustment of. See Risk-adjusted performance

factor analysis of, 183–184

individual preferences for, 63–65, 66, 67

in Markowitz theory, 109–110, 120

in two-asset portfolio for well-off investor, 53–57

Risk premium, 17, 18, 217

Robertson, Julian, 82

Rules of investing, 23–72

look only forward, 49–69

simplify your options, 27–49

tune out noise, 71–72

Russia, historical performance of stocks in, 53

Saez, Emmanuel, 196

Samuelson, Paul, 76

Scaillet, Olivier, 80

Scharfstein, David, 1, 6

Schwab Corporation, 34

Schwert, G. William, 79–80

Schwert rule, 79, 82

Scientific financial theories, 171–190

factor models in, 180–186

index funds in, 174–180

quantitative investment strategies in, 172–174

regression analysis in, 180–189

tuning out noise on, 72

Securities, definition of, 218

Securities and Exchange Commission, 49, 85

Securities industry, 1. See also Financial industry

Seeding of hedge funds, 159, 161–162

Selection bias, 160

Self-selection bias, 160, 218

Seo, John, 45–46

Sharpe, William F., 78, 115, 120, 183

Short sales, and long-short equities, 124–126, 158

Silo effect, 6–7

Simplify Wall Street portfolio, 9–21, 193, 200

with two assets, 54

Simplify your options, 27–49

benefits of, 24

core investment products in, 9–10, 27–47

Single-premium income annuities, 10, 30, 38–42

Small stocks, performance of, 179–180, 183–189

Fama and French studies on, 183–185

Smith, Adam, 191–192, 200

Smith, Greg, 4–5, 20

Socially responsible investments, 44–45, 218

Society for Human Resource Management, 140–141

Sophisticated strategies

scientific financial theories as, 173, 177, 178, 185, 189, 190

of wealthy investors, 72, 155–170

Soros, George, 82

South Africa, historical performance of stocks in, 18

SPDRs, 34

Spurgeon, Charles Haddon, 73

Standard deviation, 218

Standard & Poor rating of insurance companies, 41

Standard & Poor’s 500 index, 150, 218

average investor compared to, 103, 103f

as capitalization-weighted, 176

star performers compared to, 81–82

State Street SPDRs, 34

Statistical analysis of investment performance, 87

on rate of return, 187–188

regression analysis in, 180–189, 217. See also Regression analysis

Stock market, 218

random changes in prices on, 75–80

star performers on, 81–84

Stocks. See Equities

Struggling investors, 10, 12

dollar-cost averaging strategy for, 147

Style allocation, 116, 117, 120–121

definition of, 218

in diversification myth, 123–124

drift in, 168, 218

of institutional investors, 167, 168–169

Subprime mortgage securities, 11, 21, 198

Superrich in financial industry, 193–198

Superrich investors. See Wealthy investors

Survivorship bias, 160, 218

Sweden, historical performance of stocks in, 18

Switzerland, historical performance of stocks in, 18

Target-date funds, 126–132, 133, 218

compared to constant-mix strategy, 127, 130–132, 131t, 133

fees in, 127

glide path in, 72, 128–129

risk in, 129, 132, 133

Taxes, 59–60

on capital gains, 60

and credits for investments, 13

deduction for 401(k) contributions in, 137–138, 139

TD Ameritrade, 34

Temptations in investing, 73–190

beating the market, 71, 75–94

controlling risk, 119–133

precisely calibrating portfolio, 105–118

trusting professional expertise, 95–104

using modern scientific financial theory, 171–190

using strategies of wealthy and sophisticated investors, 72, 155–170

using strategies that always work, 135–153

Term life insurance, 10, 30, 38–42

Time-weighted rate of return, 218

compared to dollar-weighted return, 100–102, 101f, 102f

Timing of investments

company news as basis of, 89–90

and dollar-weighted rate of return, 100–103

TIPS. See Treasury Inflation-Protected Securities

Tobin, James, 115

Tomlinson, Joe, 42

Total market index funds, 116, 118, 120–121, 122, 123

Tracking error in asset allocation, 142, 168

Trading volume, 219

Transaction cost, 219

Treasury bonds, 143

expected rate of return on, 14t, 16–17

Treasury Inflation-Protected Securities (TIPS), 9, 29, 31–32, 33

definition of, 218–219

laddering of, 61–63, 67–68

percentage in portfolio, 54t, 54–55, 56, 64

rate of return on, 16–17, 33, 54–55, 56

risk in, 31–32, 56

taxes on, 60

in two-asset portfolio for well-off investor, 53–57, 59–68

Trusting expertise of others temptation, 95–104

Tsai, Gerald, 83

Tune out noise, 71–72

of deadly temptations in investing, 73–190

Turnover, 219

Two-asset portfolio, 51–57, 59–68

laddering of, 61–63, 67–68

percentage of assets in, 54t, 54–55, 56, 64

risk and return preferences in, 63–65, 66, 67

taxes on, 59–60

United Kingdom

government inflation-protected securities in, 32–33

historical performance of stocks in, 18

United States

domestic bonds in, 30

domestic equity index funds in, 9–10, 29, 34–35, 36, 58

government assistance to AIG, 41

historical performance of stocks in, 18

tax policies in. See Taxes

Treasury bonds in, 14t, 16–17, 143

Treasury Inflation-Protected Securities in. See Treasury Inflation-Protected Securities

United States Securities Act (1933), 158

Utility function, 64, 66, 67

Value stocks, 175, 177, 219

Fama and French studies on performance of, 183–185

Vanguard Group, 34, 82

Variance, 219

Volatility, 219

Wall Street, 1

Wall Street Journal, 78, 129, 132, 173

Wealth in finance industry, 193–198

Wealthy investors, 10–11, 21, 194

accredited, 158, 209

hedge funds of, 157–163, 163–164

sophisticated investment strategies of, 72, 155–170

Well-off investors, 10, 11

two-asset portfolio for, 51–57, 59–68

Wermers, Russ, 80

Why I Left Goldman Sachs: A Wall Street Story (Smith), 4–5

Working, Holbrook, 78

World stock index funds, 10, 29–30, 33–38, 43, 115

in combination with domestic equity index funds, 34–35, 36, 58

in combination with TIPS, 53–57, 59–68

expense ratios in, 34–35

for “getting by” investor, 57–58

historical performance of, 14t, 18, 53, 55–56

in one-asset portfolio, 57–58, 68

percentage in portfolio, 54–55, 56

rate of return, 14t, 18–20, 33, 54–56, 64–65

risk of, 50–51, 64–65

taxes on, 60

in two-asset portfolio for well-off investor, 53–57, 59–68

utility function of, 64

Yield of bonds, 16

ABOUT THE AUTHORS

Ann Woo

MICHAEL EDESESS is an accomplished mathematician and economist with experience in the investment, energy, and sustainable development fields. He is a visiting fellow in the Centre for Systems Informatics Engineering at City University of Hong Kong, partner and chief investment officer of Denver-based Fair Advisors, research associate of the EDHEC-Risk Institute, and senior fellow and project consultant at two Hong Kong-based think tanks. He is also affiliated with Franklnsight.com. He writes regularly for the online journal Advisor Perspectives and authored The Big Investment Lie (Berrett-Koehler, 2007). He was a founding partner and chief economist of the Lockwood Financial Group until its 2002 sale to The Bank of New York. Previously a consultant and provider of mathematical computer systems to institutional investors, Dr. Edesess worked with several of the largest banking and consulting firms. He has taught international finance, environmental economics, mathematics, and statistics at four universities. He has been interviewed on radio and TV and published in the Wall Street Journal, the Journal of Portfolio Management, and Technology Review, among other publications.

In addition to his work in investments, Dr. Edesess has been active in nonprofit organizations. He chaired the board of International Development Enterprises USA, which focuses on rural smallholders in developing countries, and also chaired the board of Rocky Mountain Institute, a prominent energy efficiency think tank, and the Rocky Mountain Advisory Board of Environmental Defense. Dr. Edesess holds a bachelor’s degree from MIT and a Ph.D. in pure mathematics from Northwestern University.

KWOK L. TSUI is a distinguished statistician whose work focuses on the statistics of quality, informatics, and data mining, and he is an expert on the problem of spurious patterns in big data. He has spent 24 years as a professor and department head at several major universities as well as a practical applied researcher. Prior to his university positions, he was for 4 years a member of Technical Staff at AT&T Bell Laboratories’ Quality Assurance Center. Dr. Tsui is currently head and chair professor in the Department of Systems Engineering and Engineering Management at City University of Hong Kong, before which he was a professor in the School of Industrial and Systems Engineering at the Georgia Institute of Technology. A recipient of the National Science Foundation Young Investigator Award, he has served in numerous positions in professional statistical societies, including fellow of the American Statistical Association and the American Society for Quality; elected member of the International Statistical Institute; U.S. representative in the ISO Technical Committee on Statistical Methods; department editor of IIE Transactions; chair of the INFORMS Section in Quality, Statistics, and Reliability; and founding chair of the INFORMS Section in Data Mining. In addition to his work on statistical quality and reliability, Dr. Tsui’s current research includes health informatics, data mining, and surveillance in health care and public health, prognostics and health management, calibration and validation of computer models, bioinformatics, process control and monitoring, and robust design and Taguchi methods in industrial engineering. Dr. Tsui holds a Ph.D. from the University of Wisconsin.

CAROL FABBRI, an experienced financial advisor, certified financial planner, and certified advisor in philanthropy, cofounded Fair Advisors in 2008. As the managing partner of Fair Advisors LLC, an independent financial advisory firm, she works with individuals and nonprofit organizations. Prior to that role, she worked as an advisor at Merrill Lynch and formerly spent a decade as an international business consultant after receiving an MBA at MIT’s Sloan School of Management. She leverages her consulting experience to educate her audiences and simplify the seemingly complex financial environment. She also works with individuals to achieve their goals using financial, estate planning, and philanthropic tools. Ms. Fabbri’s unique and clear perspective on personal finance issues is often quoted in the national press, including Forbes, Smart Money, and the Wall Street Journal. She is the coauthor of Personal Investing: The Missing Manual (O’Reilly, 2010). To address the broad need for financial literacy courses, she founded Fair Advisors Institute (FAI), a nonprofit dedicated to improving financial literacy through education and research. In 2012, FAI received the “Right on the Money” award for its work providing financial education to foster youth. Her efforts toward educating individuals about personal finance have been recognized locally and nationally. Ms. Fabbri received the TIAW World of Difference Award for her work economically empowering women and has been recognized as a Colorado Top Wealth Advisor. She recently received her black belt in taekwondo and lives in the mountains of Colorado with her husband and son.

Phil Humnicky/Georgetown Univ.

A 27-year investment industry veteran, GEORGE PEACOCK is a principal at Compendium Financial Investment Advisory, the founder and manager of the Purchasing Power Portfolio, the author of the Yoga of Investing blog, and the managing editor, investments for the online financial site FrankInsight.com. He entered the financial advisory business as an associate at American Express Financial Services (now Ameriprise). He also worked at the Wallace Financial Group, a regional insurance, investment, retirement, and financial planning firm, and Mullin Consulting, advising Fortune 1000 companies on niche compensation benefits for their senior executives. He returned to advising high-net-worth individuals and families when he joined the D.C. office of US Trust, which was later purchased by Bank of America. Mr. Peacock left Bank of America when the firm, like the rest of the investment industry, began relying increasingly on mathematical models whose output depended on a myriad of unknowable assumptions. At Euclid, he immediately established the Purchasing Power Portfolio, a precursor to the Simplify Wall Street approach described in this book. Mr. Peacock is the president of the Georgetown University Alumni Association and is an ex-officio member of the University’s board of directors. He is also a member of the External Advisory Board of the Georgetown University Alumni and Student Federal Credit Union, the largest student-run credit union in the country, and The Georgetown Chimes, an all-male a capella singing group. A part-time stand-up comic, he lives in Bethesda, Maryland, with his four children, Duncan, June, Hayley, and Mackenzie.

Also by Michael Edesess

The Big Investment Lie

What Your Financial Advisor Doesn’t Want You to Know

The investment advice and management industry is built on fraud: the idea that professional advisors can predictably and consistently help you get a better rate of return on your investments. Through fascinating and sometimes humorous anecdotes, straightforward explanations of investment theory, and rigorous evidence, Michael Edesess reveals just how badly investors are being scammed by this Big Investment Lie. He then shines a spotlight on the true cost of the industry’s useless advice, showing that a prudent independent investor, following a conservative strategy, can reap anywhere from 40 percent to over 100 percent more than an investor who falls for the Big Investment Lie.

Hardcover, 312 pages, ISBN 978-1-57675-407-8

PDF ebook, ISBN 978-1-60509-391-8

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Table of Contents

Cover Page

Title Page

Copyright Page

Dedication

Contents

Preface

Introduction

The Simplify Wall Street Portfolio

Part I The 3 Simple Rules of Investing

Rule #1

Simplify Your Options

Rule #2

Look Only Forward

Rule #3

Tune Out Noise

Part II Investing’s 7 Deadly Temptations

Deadly Temptation #1

Beat the Market

Deadly Temptation #2

Trust It All to Our Expertise

Deadly Temptation #3

Precisely Calibrate Your Portfolio

Deadly Temptation #4

Sleep Well at Night with Your Risk Under Control

Deadly Temptation #5

Use the Strategies That Always Work

Deadly Temptation #6

Do What Wealthy and Sophisticated Investors Do

Deadly Temptation #7

Use Modern Scientific Financial Theory

Epilogue

The Invisible Squeeze

Notes

Glossary

Acknowledgments

Index

About the Authors