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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
- Veröffentlichungsjahr: 2012
Bestselling author, Jack Schwager, challenges the assumptions at the core of investment theory and practice and exposes common investor mistakes, missteps, myths, and misreads
When it comes to investment models and theories of how markets work, convenience usually trumps reality. The simple fact is that many revered investment theories and market models are flatly wrong—that is, if we insist that they work in the real world. Unfounded assumptions, erroneous theories, unrealistic models, cognitive biases, emotional foibles, and unsubstantiated beliefs all combine to lead investors astray—professionals as well as novices. In this engaging new book, Jack Schwager, bestselling author of Market Wizards and The New Market Wizards, takes aim at the most perniciously pervasive academic precepts, money management canards, market myths and investor errors. Like so many ducks in a shooting gallery, Schwager picks them off, one at a time, revealing the truth about many of the fallacious assumptions, theories, and beliefs at the core of investment theory and practice.
- A compilation of the most insidious, fundamental investment errors the author has observed over his long and distinguished career in the markets
- Brings to light the fallacies underlying many widely held academic precepts, professional money management methodologies, and investment behaviors
- A sobering dose of real-world insight for investment professionals and a highly readable source of information and guidance for general readers interested in investment, trading, and finance
- Spans both traditional and alternative investment classes, covering both basic and advanced topics
- As in his best-selling Market Wizard series, Schwager manages the trick of covering material that is pertinent to professionals, yet writing in a style that is clear and accessible to the layman
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Contents
Foreword
Prologue
Part One: Markets, Return, and Risk
Chapter 1: Expert Advice
Comedy Central versus CNBC
The Elves Index
Paid Advice
Investment Insights
Chapter 2: The Deficient Market Hypothesis
The Efficient Market Hypothesis and Empirical Evidence
The Price Is Not Always Right
The Market Is Collapsing; Where Is the News?
The Disconnect between Fundamental Developments and Price Moves
Price Moves Determine Financial News
Is It Luck or Skill? Exhibit A: The Renaissance Medallion Track Record
The Flawed Premise of the Efficient Market Hypothesis: A Chess Analogy
Some Players Are Not Even Trying to Win
The Missing Ingredient
Right for the Wrong Reason: Why Markets Are Difficult to Beat
Diagnosing the Flaws of the Efficient Market Hypothesis
Why the Efficient Market Hypothesis Is Destined for the Dustbin of Economic Theory
Investment Insights
Chapter 3: The Tyranny of Past Returns
S&P Performance in Years Following High- and Low-Return Periods
Implications of High- and Low-Return Periods on Longer-Term Investment Horizons
Is There a Benefit in Selecting the Best Sector?
Hedge Funds: Relative Performance of the Past Highest-Return Strategy
Why Do Past High-Return Sectors and Strategy Styles Perform So Poorly?
Wait a Minute. Do We Mean to Imply . . .?
Investment Insights
Chapter 4: The Mismeasurement of Risk
Worse Than Nothing
Volatility as a Risk Measure
The Source of the Problem
Hidden Risk
Evaluating Hidden Risk
The Confusion between Volatility and Risk
The Problem with Value at Risk (VaR)
Asset Risk: Why Appearances May Be Deceiving, or Price Matters
Investment Insights
Chapter 5: Why Volatility Is Not Just about Risk, and the Case of Leveraged ETFs
Leveraged ETFs: What You Get May Not Be What You Expect
Investment Insights
Chapter 6: Track Record Pitfalls
Hidden Risk
The Data Relevance Pitfall
When Good Past Performance Is Bad
The Apples-and-Oranges Pitfall
Longer Track Records Could Be Less Relevant
Investment Insights
Chapter 7: Sense and Nonsense about Pro Forma Statistics
Investment Insights
Chapter 8: How to Evaluate Past Performance
Why Return Alone Is Meaningless
Risk-Adjusted Return Measures
Visual Performance Evaluation
Investment Insights
Chapter 9: Correlation: Facts and Fallacies
Correlation Defined
Correlation Shows Linear Relationships
The Coefficient of Determination (r2)
Spurious (Nonsense) Correlations
Misconceptions about Correlation
Focusing on the Down Months
Correlation versus Beta
Investment Insights
Part Two: Hedge Funds as an Investment
Chapter 10: The Origin of Hedge Funds
Chapter 11: Hedge Funds 101
Differences between Hedge Funds and Mutual Funds
Types of Hedge Funds
Correlation with Equities
Chapter 12: Hedge Fund Investing: Perception and Reality
The Rationale for Hedge Fund Investment
Advantages of Incorporating Hedge Funds in a Portfolio
The Special Case of Managed Futures
Single-Fund Risk
Investment Insights
Chapter 13: Fear of Hedge Funds: It’s Only Human
A Parable
Fear of Hedge Funds
Chapter 14: The Paradox of Hedge Fund of Funds Underperformance
Investment Insights
Chapter 15: The Leverage Fallacy
The Folly of Arbitrary Investment Rules
Leverage and Investor Preference
When Leverage Is Dangerous
Investment Insights
Chapter 16: Managed Accounts: An Investor-Friendly Alternative to Funds
The Essential Difference between Managed Accounts and Funds
The Major Advantages of a Managed Account
Individual Managed Accounts versus Indirect Managed Account Investment
Why Would Managers Agree to Managed Accounts?
Are There Strategies That Are Not Amenable to Managed Accounts?
Evaluating Four Common Objections to Managed Accounts
Investment Insights
Postscript to Part Two: Are Hedge Fund Returns a Mirage?
Part Three: Portfolio Matters
Chapter 17: Diversification: Why 10 Is Not Enough
The Benefits of Diversification
Diversification: How Much Is Enough?
Randomness Risk
Idiosyncratic Risk
A Qualification
Investment Insights
Chapter 18: Diversification: When More Is Less
Investment Insights
Chapter 19: Robin Hood Investing
A New Test
Why Rebalancing Works
A Clarification
Investment Insights
Chapter 20: Is High Volatility Always Bad?
Investment Insights
Chapter 21: Portfolio Construction Principles
The Problem with Portfolio Optimization
Eight Principles of Portfolio Construction
Correlation Matrix
Going Beyond Correlation
Investment Insights
Epilogue: 32 Investment Observations
Appendix A: Options—Understanding the Basics
Appendix B: Formulas for Risk-Adjusted Return Measures
Sharpe Ratio
Sortino Ratio
Symmetric Downside-Risk Sharpe Ratio
Gain-to-Pain Ratio (GPR)
Tail Ratio
MAR and Calmar Ratios
Return Retracement Ratio
Acknowledgments
About the Author
Index
Other Books by Jack D. Schwager
Cover design: John Wiley & Sons, Inc.
Copyright © 2013 by Jack D. Schwager. All rights reserved.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
Published simultaneously in Canada.
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Library of Congress Cataloging-in-Publication Data
Schwager, Jack D., 1948-
Market sense and nonsense : how the markets really work (and how they don’t) / Jack D. Schwager.
p. cm.
Includes index.
ISBN 978-1-118-49456-1 (cloth); 978-1-118-50934-0 (ebk); 978-1-118-50943-2 (ebk); 978-1-118-52316-2 (ebk)
1. Investment analysis. 2. Risk management. 3. Investments. I. Title.
HG4529.S387 2013
332.6—dc23
2012030901
No matter how hard you throw a dead fish in the water, it still won’t swim.
—Congolese proverb
With love to my children and our times together:
To Daniel and whitewater rafting in Maine (although I could do without the emergency room visit next time)
To Zachary and the Costa Rican rainforest, crater hole roads, and the march of the crabs
To Samantha and the hills and restaurants of Lugano on a special weekend
I hope these memories make you smile as much as they do me.
With love to my wife, Jo Ann, for so many shared times: 5,000 BTU × 2, cashless honeymoon, Thanksgiving snow in Bolton, Minnewaska and Mohonk, Mexican volcanoes, the Mettlehorn, wheeling in Nova Scotia and PEI, weekends at our Geissler retreat, the Escarpment, Big Indian, Yellowstone in winter, Long Point and Net Result.
Foreword
I was initially flattered when Jack asked me to consider writing the Foreword for his new book. So, at this point, it seems ungrateful for me to start off with a complaint. But here goes. I wish Jack had written this book sooner.
It would have been great to have had it as a resource when I was in MBA school back in the late 1970s. There, I was learning things about the efficient market theory (things that are still taught in MBA school to this day) that made absolutely no sense to me. Well, at least they made no sense if I opened my eyes and observed how the real world appeared to work outside of my business school classroom. I sure wish that back then I’d had Jack’s simple, commonsense explanation and refutation of efficient markets laid out right in front of me to help direct my studies and to put my mind at ease.
It would have been nice as a young portfolio manager to have a better understanding of how to think about portfolio risk in a framework that considered all different aspects of risk, not just the narrow framework that I had been taught in school or the one I used intuitively (a combination of fear of loss and hoping for the best).
I wish I’d had this book to give to my clients to help them judge me and their other managers not just by recent returns, or volatility, or correlation, or drawdowns, or outperformance, but by a longer perspective and deeper understanding of all of those concepts.
I wish, as a business school professor, I could have given this book to my MBA students so that the myths and misinformation they had already been taught or read about could be debunked before institutionalized nonsense and fuzzy thinking set them on the wrong path.
I wish I’d had this book to help me on all the investment committees I’ve sat on over the years. How to think about short-term track records, long-term track records, risk metrics, correlations, benchmarks, indexes, and portfolio management certainly would have come in handy! (Jack, where were you?)
Perhaps, most important, for friends and family it would have been great to hand them this book to help them gain the lifelong benefits of understanding how the markets really work (and how they don’t).
So, thanks to Jack for writing this incredibly simple, clear, and commonsense guide to the market. Better late than never. I will recommend it to everyone I know. Market Sense and Nonsense is now required reading for every investor (and the sooner they read it, the better).
Joel Greenblatt
August 2012
Prologue*
Many years ago when I worked as a research director for one of the major Wall Street brokerage firms, one of my job responsibilities included evaluating commodity trading advisors (CTAs).1 One of the statistics that CTAs were required by the regulatory authorities to report was the percentage of client accounts that closed with a profit. I made the striking discovery that the majority of closed accounts showed a net loss for virtually all the CTAs I reviewed—even those who had no losing years! The obvious implication was that investors were so bad in timing their investment entries and exits that most of them lost money—even when they chose a consistently winning CTA! This poor timing reflects the common investor tendency to commit to an investment after it has done well and to liquidate an investment after it has done poorly. Although these types of investment decisions may sound perfectly natural, even instinctive, they are also generally wrong.
Investors are truly their own worst enemy. The natural instincts of most investors lead them to do exactly the wrong thing with uncanny persistence. The famous quote from Walt Kelly’s cartoon strip, Pogo, “We have met the enemy, and it is us,” could serve as a fitting universal motto for investors.
Investment errors are hardly the exclusive domain of novice investors. Investment professionals commit their own share of routine errors. One common error that manifests itself in many different forms is the tendency to draw conclusions based on insufficient or irrelevant data. The housing bubble of the early 2000s provided a classic example. One of the ingredients that made the bubble possible was the development of elaborate mathematical models to price complex mortgage-backed securitizations. The problem was that there was no relevant data to feed into these models. At the time, mortgages were being issued to subprime borrowers without requiring any down payment or verification of job, income, or assets. There was no precedence for such poor-quality mortgages, and hence no relevant historical data. The sophisticated mathematical models failed disastrously because conclusions were being derived based on data that was irrelevant to the present circumstances.2 Despite the absence of relevant data, the models served as justification for attaching high ratings to risk-laden subprime-mortgage-linked debt securitizations. Investors lost over a trillion dollars.
Drawing conclusions based on insufficient or inappropriate data is commonplace in the investment field. The mathematics of portfolio allocation provides another pervasive example. The standard portfolio optimization model uses historical returns, volatilities, and correlations of assets to derive an optimal portfolio—that is, the combination of assets that will deliver the highest return for any given level of volatility. The question that fails to be asked, however, is whether the historical returns, volatilities, and correlations being used in the analysis are likely to be at all indicative of future levels. Very frequently they are not, and the mathematical model delivers results that precisely fit the past data but are worthless, or even misleading, as guidelines for the future—and the future, of course, is what is relevant to investors.
Market models and theories of investment are often based on mathematical convenience rather than empirical evidence. A whole edifice of investment theory has been built on the assumption that market prices are normally distributed. The normal distribution is very handy for analysts because it allows for precise probability-based assumptions. Every few years, one or more global markets experience a price move that many portfolio managers insist should occur only “once in a thousand years” or “once in a million years” (or even much rarer intervals). Where do these probabilities come from? They are the probabilities of such magnitude price moves occurring, assuming prices adhere to a normal distribution. One might think that the repeated occurrence of events that should be a rarity would lead to the obvious conclusion that the price model being used does not fit the real world of markets. But for a large part of the academic and financial establishment, it has not led to this conclusion. Convenience trumps reality.
The simple fact is that many widely held investment models and assumptions are simply wrong—that is, if we insist they work in the real world. In addition, investors bring along their own sets of biases and unsubstantiated beliefs that lead to misguided conclusions and flawed investment decisions. In this book, we will question the conventional wisdom applied to the various aspects of the investment process, including selection of assets, risk management, performance measurement, and portfolio allocation. Frequently, accepted truths about investment prove to be unfounded assumptions when exposed to the harsh light of the facts.
*Some of the text in the first two paragraphs has been adapted from Jack D. Schwager, Managed Trading: Myths & Truths (New York: John Wiley & Sons, 1996).
1Commodity trading advisor (CTA) is the official designation of regulated managers who trade the futures markets.
2Although the most widely used model to price mortgage-backed securitizations used credit default swaps (CDSs) rather than default rates as a proxy for default risk, CDS prices would have been heavily influenced by historical default rates that were based on irrelevant mortgage default data.
PART ONE
MARKETS, RETURN, AND RISK
Chapter 1
Expert Advice
Comedy Central versus CNBC
On March 4, 2009, Jon Stewart, the host of The Daily Show, a satirical news program, lambasted CNBC for a string of poor prognostications. The catalyst for the segment was Rick Santelli’s famous rant from the floor of the Chicago Mercantile Exchange, in which he railed against subsidizing “losers’ mortgages,” a clip that went viral and is widely credited with igniting the Tea Party movement. Stewart’s point was that while Santelli was criticizing irresponsible homeowners who missed all the signs, CNBC was in no position to be sitting in judgment.
Stewart then proceeded to play a sequence of CNBC clips highlighting some of the most embarrassingly erroneous forecasts and advice made by multiple CNBC commentators, each followed by a white type on black screen update. The segments included:
Jim Cramer, the host of
Mad Money
, answering a viewer’s question by emphatically declaring, “Bear Stearns is fine! Keep your money where it is.” A black screen followed: “Bear Stearns went under six days later.”
A
Power Lunch
commentator extolling the financial strength of Lehman Brothers saying, “Lehman is no Bear Stearns.” Black screen: “Lehman Brothers went under three months later.”
Jim Cramer on October 4, 2007, enthusiastically recommending, “Bank of America is going to $60 in a heartbeat.” Black screen: “Today Bank of America trades under $4.”
Charlie Gasparino saying that American International Group (AIG) as the biggest insurance company was obviously not going bankrupt, which was followed by a black screen listing the staggeringly large AIG bailout installments to date and counting.
Jim Cramer’s late 2007 bullish assessment, “You should be buying things. Accept that they are overvalued. . . . I know that sounds irresponsible, but that’s how you make the money.” The black screen followed: “October 31, 2007, Dow 13,930.”
Larry Kudlow exclaiming, “The worst of this subprime business is over.” Black screen: “April 16, 2008, Dow 12,619.”
Jim Cramer again in mid-2008 exhorting, “It’s time to buy, buy, buy!” Black screen: “June 13, 2008, Dow 12,307.”
A final clip from
Fast Money
talking about “people starting to get their confidence back” was followed by a final black screen message: “November 4, 2008, Dow 9,625.”
Stewart concluded, “If I had only followed CNBC’s advice, I’d have a $1 million today—provided I started with $100 million.”
Stewart’s clear target was the network, CNBC, which, while promoting its financial expertise under the slogan “knowledge is power,” was clueless in spotting the signs of the impending greatest financial crisis in nearly a century. Although Stewart did not personalize his satiric barrage, Jim Cramer, whose frenetic presentation style makes late-night infomercial promoters appear sedated in comparison, seemed to come in for a disproportionate share of the ridicule. A widely publicized media exchange ensued between Cramer and Stewart in the following days, with each responding to the other, both on their own shows and as guests on other programs, and culminating with Cramer’s appearance as an interview guest on The Daily Show on March 12. Stewart was on the attack for most of the interview, primarily chastising CNBC for taking corporate representatives at their word rather than doing any investigative reporting—in effect, for acting like corporate shills rather than reporters. Cramer did not try to defend against the charge, saying that company CEOs had openly lied to him, which was something he too regretted and wished he’d had the power to prevent.
The program unleashed an avalanche of media coverage, with most writers and commentators seeming to focus on the question of who won the “debate.” (The broad consensus was Stewart.) What interests us here is not the substance or outcome of the so-called debate, but rather Stewart’s original insinuation that Cramer and other financial pundits at CNBC had provided the public with poor financial advice. Is this criticism valid? Although the sequence of clips Stewart played on his March 4 program was damning, Cramer had made thousands of recommendations on his Mad Money program. Anyone making that many recommendations could be made to look horrendously inept by cherry-picking the worst forecasts or advice. To be fair, one would have to examine the entire record, not just a handful of samples chosen for their maximum comedic impact.
That is exactly what three academic researchers did. In their study, Joseph Engelberg, Caroline Sasseville, and Jared Williams (ESW) surveyed and analyzed the accuracy and impact of 1,149 first-time buy recommendations made by Cramer on Mad Money.1 Their analysis covered the period from July 28, 2005 (about four months after the program’s launch) through February 9, 2009—an end date that conveniently was just three weeks prior to The Daily Show episode mocking CNBC’s market calls.
ESW began by examining a portfolio formed by the stocks recommended on Mad Money, assuming each stock was entered on the close before the evening airing of the program on which it was recommended—a point in time deliberately chosen to reflect the market’s valuation prior to the program’s price impact. They assumed an equal dollar allocation among recommended stocks and tested the results for a variety of holding periods, ranging from 50 to 250 trading days. The differences in returns between these recommendation-based portfolios and the market were statistically insignificant across all holding periods and net negative for most.
ESW then looked at the overnight price impact (percentage change from previous close to next day’s open) of Cramer’s recommendations and found an extremely large 2.4 percent average abnormal return—that is, return in excess of the average price change of similar stocks for the same overnight interval. As might be expected based on the mediocre results of existing investors in the same stocks and the large overnight influence of Cramer’s recommendations, using entries on the day after the program, the recommendation-based portfolios underperformed the market across all the holding periods. The annualized underperformance was substantial, ranging from 3 percent to 10 percent. The worst performance was for the shortest holding period (50 days), suggesting a strong bias for stocks to surrender their “Cramer bump” in the ensuing period. The bottom line seems to be that investors would be better off buying and holding an index than buying the Mad Money recommendations—although, admittedly, there is much less entertainment value in buying an index.
I don’t mean to pick on Cramer. There is no intention to paint Cramer as a showman with no investment skill. On the contrary, according to an October 2005 BusinessWeek article, Cramer achieved a 24 percent net compounded return during his 14-year tenure as a hedge fund manager—a very impressive performance record. But regardless of Cramer’s investment skills and considerable market knowledge, the fact remains that, on average, viewers following his recommendations would have been better off throwing darts to pick stocks.
The Elves Index
The study that examined the Mad Money recommendations represented the track record of only a single market expert for a four-year time period. Next we examine an index that was based on the input of 10 experts and was reported for a period of over 12 years.
The most famous, longest-running, and most widely watched stock-market-focused program ever was Wall Street Week with Louis Rukeyser, which aired for over 30 years. One feature of the show was the Elves Index. The Elves Index was launched in 1989 and was based on the net market opinion of 10 expert market analysts selected by Rukeyser. Each analyst opinion was scored as +1 for bullish, 0 for neutral, and −1 for bearish. The index had a theoretical range from −10 (all analysts bearish) to +10 (all analysts bullish). The concept was that when a significant majority of these experts were bullish, the market was a buy (+5 was the official buy signal), and if there was a bearish consensus, the market was a sell (–5 was the official sell signal). That is not how it worked out, though.
In October 1990 the Elves Index reached its most negative level since its launch, a −4 reading, which was just shy of an official sell signal. This bearish consensus coincided with a major market bottom and the start of an extended bull market. The index then registered lows of −6 in April 1994 and −5 in November 1994, coinciding with the relative lows of the major bottom pattern formed in 1994. The index subsequently reached a bullish extreme of +6 in May 1996 right near a major relative high. The index again reached +6 in July 1998 shortly before a 19 percent plunge in the S&P 500 index. A sequence of the highest readings ever recorded for the index occurred in the late 1999 to early 2000 period, with the index reaching an all-time high (up to then) of +8 in December 1999. The Elves Index remained at high levels as the equity indexes peaked in the first quarter of 2000 and then plunged. At one point, still early in the bear market, the Elves Index even reached an all-time high of +9. Rukeyser finally retired the index shortly after 9/11, when presumably, if kept intact, it would have provided a strong sell signal.2
Rukeyser no doubt terminated the Elves Index as an embarrassment. Although he didn’t comment on the timing of the decision, it is reasonable to assume he couldn’t tolerate another major sell signal in the index coinciding with what would probably prove to be a relative low (as it was). Although the Elves Index had compiled a terrible record—never right, but often wrong—its demise was deeply regretted by many market observers. The index was so bad that many had come to view it as a useful contrarian indicator. In other words, listening to the consensus of the experts as reflected by the index was useful—as long as you were willing to do the exact opposite.
Paid Advice
In this final section, we expand our analysis to encompass a group that includes hundreds of market experts. If there is one group of experts that might be expected to generate recommendations that beat the market averages, it is those who earn a living selling their advice—that is, financial newsletter writers. After all, if a newsletter’s advice failed to generate any excess return, presumably it would find it difficult to attract and retain readers willing to pay for subscriptions.
Do the financial newsletters do better than a market index? To find the answer, I sought out the data compiled by the Hulbert Financial Digest, a publication that has been tracking financial newsletter recommendations for over 30 years. In 1979, the editor, Mark Hulbert, attended a financial conference and heard many presentations in which investment advisers claimed their recommendations earned over 100 percent a year, and in some cases much more. Hulbert was skeptical about these claims and decided to track the recommendations of some of these advisers in real time. He found the reality to be far removed from the hype. This realization led to the launch of the Hulbert Financial Digest with a mission of objectively tracking financial newsletter recommendations and translating them into implied returns. Since its launch in 1981, the publication has tracked over 400 financial newsletters.
Hulbert calculates an average annual return for each newsletter based on their recommendations. Table 1.1 compares the average annual return of all newsletters tracked by Hulbert versus the S&P 500 for three 10-year intervals and the entire 30-year period. (The newsletter return for any given year is the average return of all the newsletters tracked by Hulbert in that year.) As a group, the financial newsletters significantly underperformed the S&P 500 during 1981–1990 and 1991–2000 and did moderately better than the S&P 500 during 2001–2010. For the entire 30-year period, the newsletters lagged the S&P 500 by an average of 3.7 percent per annum.
Table 1.1 Average Annual Return: S&P 500 versus Average of Financial Newsletters
Source: Raw data on investment newsletter performance from the Hulbert Financial Digest.
Perhaps if the choice of newsletters were restricted to those that performed best in the recent past, this more select group would do much better than the group as whole. To examine this possibility, we focus on the returns generated by the top-decile performers in prior three-year periods. Thus, for example, the 1994 returns would be based on the average of only those newsletters that had top-decile performance for the 1991–1993 period. Table 1.2 compares the performance of these past better-performing newsletters with the S&P 500 and also includes comparison returns for the past worst-decile-return group. Choosing from among the best past performers doesn’t seem to make much difference. The past top-decile-return newsletters still lag the S&P 500. Although picking the best prior performers doesn’t seem to provide much of an edge, it does seem advisable to avoid the worst prior performers, which for the period as a whole did much worse than the average of all newsletters.
Table 1.2Average Annual Return: S&P 500 versus Average of Financial Newsletters in Top and Bottom Deciles in Prior Three-Year Periods
Source: Raw data on investment newsletter performance from the Hulbert Financial Digest.
Perhaps three years is a look-back period of insufficient length to establish superior performance. To examine this possibility, Table 1.3 duplicates the same analysis comparing the past five-year top- and bottom-decile performers with the S&P 500. The relative performance results are strikingly similar to the three-year look-back analysis. For the period as a whole, the past top-decile performers lagged the S&P 500 by 2.6 percent (versus 2.4 percent in the three-year look-back analysis), and the bottom-decile group lagged by a substantive 9.5 percent (versus 8.7 percent in the prior analysis). The conclusion is the same: Picking the best past performers doesn’t seem to provide any edge over the S&P 500, but avoiding the worst past performers appears to be a good idea.
Table 1.3Average Annual Return: S&P 500 versus Average of Financial Newsletters in Top and Bottom Deciles in Prior Five-Year Periods
Source: Raw data on investment newsletter performance from the Hulbert Financial Digest.
Some of the newsletters tracked by Hulbert did indeed add value, delivering market-beating recommendations over the long term. Picking these superior newsletters ahead of time, however, is no easy task. The complicating factor is that while some superior past performers continue to do well, others don’t. Simply selecting from the best past performers is not sufficient to identify the newsletters whose advice is likely to beat the market in a coming year.
Investment Insights
Many investors seek guidance from the advice of financial experts available through both broadcast and print media. Is this advice beneficial? In this chapter, we have examined three cases of financial expert advice, ranging from the recommendation-based record of a popular financial program host to an index based on the directional calls of 10 market experts and finally to the financial newsletter industry. Although this limited sample does not rise to the level of a persuasive proof, the results are entirely consistent with the available academic research on the subject. The general conclusion appears to be that the advice of the financial experts may sometimes trigger an immediate price move as the public responds to their recommendations (a price move that is impossible to capture), but no longer-term net benefit.
My advice to equity investors is either buy an index fund (but not after a period of extreme gains—see Chapter 3) or, if you have sufficient interest and motivation, devote the time and energy to develop your own investment or trading methodology. Neither of these approaches involves listening to the recommendations of the experts. Michael Marcus, a phenomenally successful trader, offered some sage advice on the matter: “You have to follow your own light. . . . As long as you stick to your own style, you get the good and the bad in your own approach. When you try to incorporate someone else’s style, you often wind up with the worst of both styles.”3
1Engelberg, Joseph, Caroline Sasseville, and Jared Williams, Market Madness? The Case of Mad Money (October 20, 2010). Available at SSRN: http://ssrn.com/abstract=870498.
2“Louis Rukeyser Shelves Elves Missed Market Trends Tinkering Didn’t Improve Index’s Track Record for Calling Market’s Direction (MUTUAL FUNDS),” Investor’s Business Daily, November 1, 2001. Retrieved March 29, 2011, from AccessMyLibrary: www.accessmylibrary.com/article-1G2.106006432/louis-rukeyser-shelves-elves.html.
3Jack D. Schwager, Market Wizards (New York: New York Institute of Finance, 1989).
Chapter 2
The Deficient Market Hypothesis
The most basic investment question is: Can the markets be beat? The efficient market hypothesis provides an unambiguous answer: No, unless you count those who are lucky.
The efficient market hypothesis, a theory explaining how market prices are determined and the implications of the process, has been the foundation of much of the academic research on markets and investing during the past half century. The theory underlies virtually every important aspect of investing, including risk measurement, portfolio optimization, index investing, and option pricing. The efficient market hypothesis can be summarized as follows:
Prices of traded assets already reflect all known information.
Asset prices instantly change to reflect new information.
Therefore,
Market prices are true and accurate.
It is impossible to consistently outperform the market by using any information that the market already knows.
The efficient market hypothesis comes in three basic flavors:
The Efficient Market Hypothesis and Empirical Evidence
It should be clear that if the efficient market hypothesis were true, markets would be impossible to beat except by luck. Efficient market hypothesis proponents have compiled a vast amount of evidence that markets are extremely difficult to beat. For example, there have been many studies that show that professional mutual fund managers consistently underperform benchmark stock indexes, which is the result one would expect if the efficient market hypothesis were true. Why underperform? Because if the efficient market hypothesis were true, the professionals should do no better than the proverbial monkey throwing darts at a list of stock prices or a random process, which on average should lead to an approximate index result if there were no costs involved. However, there are costs involved: commissions, transaction slippage (bid/asked differences), and investor fees. Therefore, on average, the professional managers should do somewhat worse than the indexes, which they do. The efficient market hypothesis proponents point to the empirical evidence of the conformity of investment results to that implied by the theory as evidence that the theory either is correct or provides a close approximation of reality.
There is, however, a logical flaw in empirical proofs of the efficient market hypothesis, which can be summarized as follows:
If A is true (e.g., the efficient market hypothesis is true),
and A implies B (e.g., markets are difficult to beat),
then the converse (B implies A) is also true (if markets are difficult to beat, then the efficient market hypothesis is true).
The logical flaw is that the converse of a true statement is not necessarily true. Consider the following simple example:
All polar bears are white mammals.
But clearly, not all white mammals are polar bears.
While empirical evidence can’t prove the efficient market hypothesis, it can disprove it if one can find events that contradict the theory. There is no shortage of such events. We will look at four types of empirical evidence that clearly seem to contradict the efficient market hypothesis:
The Price Is Not Always Right
A cornerstone principle underlying the efficient market hypothesis is that market prices are perfect. Viewed in the light of actual market examples, this assumption seems nothing short of preposterous. We consider only a few out of a multitude of possible illustrative examples.
Pets.com and the Dot-Com Mania
Pets.com is a reasonable poster child for the Internet bubble. As its name implies, Pets.com’s business model was selling pet supplies over the Internet. One particular problem with this model was that core products, such as pet food and cat litter, were low-margin items, as well as heavy and bulky, which made them expensive to ship. Also, these were not exactly the types of products for which there was any apparent advantage to online delivery. On the contrary, if you were out of dog food or cat litter, waiting for delivery of an online order was not a practical alternative. Given these realities, Pets.com had to price its products, including shipping, competitively. In fact, given the large shipping cost, the only way the company could sell product was to set prices at levels below its own total cost. This led to the bizarre situation in which the more product Pets.com sold, the more money it lost. Despite these rather bleak fundamental realities, Pets.com had a market capitalization in excess of $300 million following its initial public offering (IPO). The company did not survive even a full year after its IPO. Ironically, Pets.com could have lasted longer if it could just have cut sales, which were killing the company.
Pets.com was hardly alone, but is emblematic of the dot-com mania. From 1998 to early 2000, the market experienced a speculative mania in technology stocks and especially Internet stocks. During this period, there were numerous successful IPO launches for companies with negative cash flows and no reasonable near-term prospects for turning a profit. Because it was impossible to justify the valuation of these companies, or for that matter even any positive valuation, by any traditional metrics (that is, those related to earnings and assets), this era saw equity analysts invent such far-fetched metrics as the number of clicks or “eyeballs” per website with talk of a “new paradigm” in equity valuation. Many of these companies, which reached valuations of hundreds of millions or even billions of dollars, crashed and burned within one or two years of their launch. Burn is the appropriate word, as the timing of the demise of these tenuous companies was linked to their so-called burn rate—the rate at which their negative cash flow consumed cash.
Figure 2.1 shows the AMEX Internet Index during the 1998–2002 period. From late 1998 to the March 2000 peak, the index increased an incredible sevenfold in the space of 17 months. The index then surrendered the entire gain, falling 86 percent in the next 18 months. The efficient market hypothesis not only requires believing that the fundamentals improved sufficiently between October 1998 and March 2000 to justify a 600 percent increase in this short time span, but that the fundamentals then deteriorated sufficiently for prices to fall 86 percent by September 2001. A far more plausible explanation is that the giant rally in Internet stocks from late 1998 to early 2000 was unwarranted by the fundamentals, and therefore the ensuing collapse represented a return of prices to levels more consistent with prevailing fundamentals. Such an explanation, however, contradicts the efficient market hypothesis, which would require new fundamental developments to explain both the rally and the collapse phases.
Figure 2.1 AMEX Internet Index (IIX), 1998–2002
Source: moneycentral.msn.com.
A Subprime Investment1
A subprime mortgage bond combines multiple individual subprime mortgages into a security that pays investors interest income based on the proceeds from mortgage payments. These bonds typically employ a structure in which multiple tranches (or classes) are created from the same pool of mortgages. The highest-rated class, AAA, gets paid off in full first; then the next highest-rated class (AA) is paid off, and so on. The higher the class, the lower the risk, and hence the lower the interest rate the tranche receives. The so-called equity tranche, which is not rated, typically absorbs the first 3 percent of losses and is wiped out if this loss level is reached. The lower-rated tranches are the first to absorb default risk, for which they are paid a higher rate of interest. For example, a typical BBB tranche, the lowest-rated tranche, would begin to be impaired if losses due to defaulted repayments reached 3 percent, and investors would lose all their money if losses reached 7 percent. Each higher tranche would be protected in full until losses surpassed the upper threshold of the next lower tranche. The lowest-rated tranche (i.e., BBB), however, is always exposed to a significant risk of at least some impairment.
During the housing bubble of the mid-2000s, the risks associated with the BBB tranches of subprime bonds, which were high to start, increased dramatically. There was a significant deterioration in the quality of loans, as loan originators were able to pass on the risk by selling their mortgages for use in bond securitizations. The more mortgages they issued and sold off, the greater the fees they collected. Effectively, mortgage originators were freed from any concern about whether the mortgages they issued would actually be repaid. Instead, they were incentivized to issue as many mortgages as possible, which was exactly what they did. The lower they set the bar for borrowers, the more mortgages they could create. Ultimately, in fact, there was no bar at all, as subprime mortgages were being issued with the following characteristics:
No down payment.
No income, job, or asset verification (the so-called infamous NINJA loans).
Adjustable-rate mortgage (ARM) structures in which low teaser rates adjusted to much higher levels after a year or two.
There was no historical precedent for such low-quality mortgages. It is easy to see how the BBB tranche of a bond formed from these low-quality mortgages would be extremely vulnerable to a complete loss.
The story, however, does not end there. Not surprisingly, the BBB tranches were difficult to sell. Wall Street alchemists came up with a solution that magically transformed the BBB tranches into AAA. They created a new securitization called a collateralized debt obligation (CDO) that consisted entirely of the BBB tranches of many mortgage bonds.2 The CDOs also employed a tranche structure. Typically, the upper 80 percent of a CDO, consisting of 100 percent BBB tranches, was rated AAA.
Although the CDO tranche structure was similar to that employed by subprime mortgage bonds consisting of individual mortgages, there was an important difference. In a properly diversified pool of mortgages, there was at least some reason to assume there would be limited correlation in default risk among individual mortgages. Different individuals would not necessarily come under financial stress at the same time, and different geographic areas could witness divergent economic conditions. In contrast, all the individual elements of the CDOs were clones—they all represented the lowest tier of a pool of subprime mortgages. If economic conditions were sufficiently unfavorable for the BBB tranche of one mortgage bond pool to be wiped out, the odds were very high that BBB tranches in other pools would also be wiped out or at least severely impaired.3 The AAA tranche needed a 20 percent loss to begin being impaired, which sounds like a safe number, until one considers that all the holdings are highly correlated. The BBB tranches were like a group of people in close quarters contaminated by a highly contagious flu. If one person is infected, the odds that many will be infected increase dramatically. In this context, the 20 percent cushion of the AAA class sounds more like a tissue paper layer.
How could bonds consisting of only BBB tranches be rated AAA? There are three interconnected explanations.
If you are an investor, how much of an interest premium over a 10-year Treasury note would you request for investing in a AAA-rated CDO consisting entirely of BBB subprime mortgage tranches? How does ¼ of 1 percent sound? Ridiculous? Why would anyone buy a bond consisting entirely of the worst subprime assets for such a minuscule premium? Well, people did. In what universe does this pricing make sense? The efficient market hypothesis would by definition contend that these bonds consisting of BBB tranches constructed from no-verification, ARM subprime mortgages were correctly priced in paying only ¼ of 1 percent over U.S. Treasuries. Of course, the buyers of these complex securities had no idea of the inherent risk and were merely relying on the credit rating agencies. According to the efficient market hypothesis, however, knowledgeable market participants should have brought prices into line. This line of reasoning highlights another basic flaw in the efficient market hypothesis: It doesn’t allow for the actions of the ignorant masses to outweigh the actions of the well informed—at least for a while—and this is exactly what happened.
Negative Value Assets—The Palm/3Com Episode5
Although it would seem extremely difficult to justify Internet company prices at their peak in 2000 or the AAA ratings for tranches of CDOs consisting of the lowest-quality subprime mortgages, there is no formula to yield an exact correct price at any given time. (Of course, the efficient market hypothesis believers would contend that this price is the market price.) Therefore, while these examples provide compelling illustrations of apparent drastic mispricings, they fall short of the solidity of a mathematical proof of mispricing due to investor irrationality. The Palm/3Com episode provides such incontrovertible evidence of investor irrationality and prices that can be shown to be mathematically incorrect.
On March 2, 2000, 3Com sold approximately 5 percent of its holdings in Palm, most of it in an IPO. The Palm shares were issued at $38. Palm, the leading manufacturer of handheld computers at the time, was a much sought-after offering, and the shares were sharply bid up on the first day. At one point, prices more than quadrupled the IPO price, reaching a daily (and all-time) high of $165. Palm finished the first day at a closing price of $95.06.
Since 3Com retained 95 percent ownership of Palm, 3Com shareholders indirectly owned 1.5 Palm shares for each 3Com share, based on the respective number of outstanding shares in each company. Ironically, despite the buying frenzy in Palm, 3Com shares fell 21 percent on the day of the IPO, closing at 81.181. Based on the implicit embedded holding of Palm shares, 3Com shares should have closed at a price of at least $142.59 based solely on the value of the Palm shares at their closing price ($1.5 × $95.06 = $142.59). In effect, the market was valuing the stub portion of 3Com (that is, the rest of the company excluding Palm) at −$60.78! The market was therefore assigning a large negative price to all of the company’s remaining assets excluding Palm, which made absolutely no sense. At the high of the day for Palm shares, the market was implicitly assigning a negative value well in excess of $100 to the stub portion of 3Com. Adding to the illogic of this pricing, 3Com had already indicated its intention to spin off the remainder of Palm shares later that year, pending an Internal Revenue Service (IRS) ruling on the tax status, which was expected to be resolved favorably. Thus 3Com holders were likely to have their implicit ownership of Palm converted to actual shares within the same year.
The extreme disconnect between 3Com and Palm prices, despite their strong structural link, seems to be not merely wildly incongruous; it appears to border on the impossible. Why wouldn’t arbitrageurs simply buy 3Com and sell Palm short in a ratio of 1.5 Palm shares to one 3Com share? Indeed, many did, but the arbitrage activity was insufficient to close the wide value gap, because Palm shares were either impossible or very expensive to borrow (a prerequisite to shorting the shares). Although the inability to adequately borrow Palm shares can explain why arbitrage didn’t immediately close the price gap, it doesn’t eliminate the paradox. The question remains as to why any rational investors would pay $95 for one share of Palm when they could have paid $82 for 3Com, which represented 1.5 shares of Palm plus additional assets. The paradox is even more extreme when one considers the much higher prices paid by some investors earlier in the day as Palm shares traded as high as $165. There is no escaping the fact that these investors were acting irrationally.
Given the facts, it is clear that either the market was pricing Palm too high or it was pricing 3Com too low, or some combination of the two. It is a logical impossibility to argue that both Palm and 3Com were priced perfectly, or for that matter even remotely close to correctly. At least one of the two equities was hugely mispriced.
What ultimately happened? Exactly what would have reasonably been expected: Palm shares steadily lost ground relative to 3Com, and the implied value of the 3Com stub rose steadily from deeply negative to over $10 per share at the time of the distribution of Palm shares to 3Com shareholders less than four months later. Arbitrageurs who were able to short Palm and buy 3Com profited handsomely, while Palm investors who bought shares indirectly by buying 3Com fared tremendously better than investors who purchased Palm shares directly. Gaining advantage through obvious mispricings for a high-profile IPO that was prominently discussed in the financial press is something that should have been impossible if the efficient market hypothesis were correct.
So what is the explanation for the paradoxical price relationships that occurred in the Palm spin-off? Quite simply that, contrary to the efficient market hypothesis contention that prices are always correct, sometimes emotions will cause investors to behave irrationally, resulting in prices that are far removed from fundamentally justifiable levels. In the case of Palm, this was another example of investors getting caught up in the frenzy of the tech buying bubble, which peaked only about a week after the Palm IPO. Figure 2.2 shows what happened to Palm shares after the initial IPO. (Note that this chart is depicted in terms of current share prices—that is, past prices have been adjusted for stock splits and reverse splits, which equates to a 10:1 upward adjustment in the March 2000 prices.) As can be seen, in less than two years, Palm shares lost over 99 percent of what their value had been on the close of the IPO day.
Figure 2.2 Palm (Split-Adjusted), 2000–2002
Source: moneycentral.msn.com.
The fact that some mispricings, such as Palm/3Com, can be demonstrated with mathematical certainty lends credence to the view that numerous other cases of apparent mispricings are indeed price aberrations, even when such an absolute proof is not possible. There is an important difference between this point of view and the efficient market hypothesis framework. Whereas the efficient market hypothesis view of the world argues that it is futile to search for opportunities because the market price is always right, a view that investor emotions can cause prices to deviate widely from reasonable valuations implies that there are opportunities to profit from market prices being wrong (that is, routinely trading at premiums or discounts to fair value).
The Market Is Collapsing; Where Is the News?
In the world described by the efficient market hypothesis, price moves occur because the fundamentals change and prices adjust. Large price moves therefore imply some very major event.
On October 19, 1987, a day that became known as Black Monday, equity indexes witnessed an incredible plunge. The Standard & Poor’s (S&P) 500 index lost 20.5 percent, by far the largest single-day loss ever. Moreover, the actual decline was far worse. The cash S&P index, which normally is kept tightly in line with S&P futures by arbitrageurs, dramatically lagged the decline in futures on October 19, 1987, because the New York Stock Exchange (NYSE) order processing system couldn’t keep up with the avalanche of orders. These mechanical delays resulted in stale limit orders (that is, orders placed earlier in the day when index prices were higher) being executed. Thus the cash market index close on October 19, 1987, was itself stale and significantly understated the actual decline. The more liquid futures market, which did not embed such stale pricing and therefore was a far more accurate indicator of the actual decline, fell by an even more astounding 29 percent! Even Black Tuesday, the October 28, 1929, crash, failed to come close, losing a mere 12.94 percent.6 Although the 1929 Black Tuesday decline was followed the next day by an additional 10.2 percent decline, even the loss on these two days combined was still one-third smaller than the S&P futures decline on October 19, 1987. All other historic daily declines in stocks were less than one-third as large (using S&P futures as the comparison). In short, the October 19, 1987, crash towers above all other historic declines, including the infamous October 1929 crash.
