Chapter 4

Conquistadors in Tweed Jackets

GOLD WAS ALWAYS on the minds of the Spanish conquistadors of the 1500s. Those freebooting, merciless adventurers heard rumors of an entire city of gold in Peru, ruled by a king who bathed in a golden lake and wore pure gold dust like perfume. They searched and searched for El Dorado and talked of their vision to others. But the city was never found.

Fast-forward some five centuries. Faith and the sword have once more gone in against even more overwhelming odds than those that faced their predecessors centuries ago. The reigning conquistadors are not traditional warriors. To look at them, you’d have thought they were, well, professors, dressed in the armor of academia, often tweed jackets with leather patches covering their elbows. But don’t let their conservative attire fool you. They were armed with the most powerful weaponry the investment world had seen to that time—high-level mathematics and statistics, programmed into advanced computers—and their findings were overwhelming. A handful of theorists working at one major university or another across the land (and soon around the globe) came up with a powerful new theory that changed the course not only of Wall Street but of all investment thinking. They had hearts of steel, and their secular and academic faith was no less fervent than the religious zeal of the original conquistadors, while their weaponry terrified all who were foolish enough to challenge it.

I know many disciples of these theorists who would be happy to show you the way to a new El Dorado. They have a mathematical map—and a visionary theory that explains everything about how investors make decisions and markets work. Though they are far too modest to promise cities of gold, they are fervent about owning the key that unlocks the door to the secrets of markets.

Their arguments call to mind Winston Churchill’s description of Russia: “a riddle wrapped in a mystery inside an enigma.”1 But to appreciate just how flawed those arguments are, it’s important that we first investigate just how they managed to convince so many people of their veracity, because they are indeed both scientific and convincing.

A Revolutionary New Financial Hypothesis

Did the new conquistadors of mathematical analysis actually sweep away the decaying financial culture and replace it with a scientific one, or did something go seriously wrong? Rather than a new Age of Enlightenment, did they bring in a New Dark Age in its place? The historians’ Dark Ages, as we know, refer to a period of cultural and economic deterioration. That disruption and decline in Western Europe came after the fall of Rome and lasted to the early part of the Middle Ages. Compared with the highly developed cultures and civilizations of Greece and Rome and the Renaissance era that followed, this period contributed little to the enlightenment of man. Edward Gibbon, in The History of the Decline and Fall of the Roman Empire, from his eighteenth-century perspective, expressed his contempt for the “rubbish of the Dark Ages.”2

I wonder what Mr. Gibbon would think of our day. How, in this time of enormous technological, medical, scientific, and cultural advancement, he might ask, could our thinking powers have gone into such decline? It is certainly not universal, encompassing all parts of our cultural and technological development. Quite the contrary; it is localized in one area of the social sciences: economics.

In the past sixty-five years, the study of economics escaped the cozy confines of the ivory tower and became highly influential. The economic and financial theories economists espoused are now powerful enough to affect the well-being of hundreds of millions of people globally, and they have encouraged us to take a big step back from lessons we learned in the first half of the twentieth century.

A Brief History Lesson

The new conquistadors’ bible, the efficient-market hypothesis, or EMH, as it’s generally referred to, is the most influential financial theory in generations. In recent decades it leaped out of academia and became the farthest-reaching and most widely followed theory in the world of global finance. Critics protest that its assumptions and much of its research have never been proved. Others go further and state that its premises, as well as thousands of supposedly highly sophisticated mathematical papers supporting it, are rebutted by findings in many sectors of social science—and in the marketplace itself. Still, EMH flourishes, followed by enormous numbers of investors on their own or through the managers of their mutual funds and investment advisers.

How did EMH—and its two close-knit brethren, the capital asset pricing model (CAPM) and modern portfolio theory (MPT)*12—become so powerful in the investment world?*13 How can the teachings influence you, and how have they shaped markets in our time? Just as psychological errors induced by Affect and other cognitive heuristics can be avoided only by gaining an understanding of them, a study of EMH and its shortcomings is essential to protect investors from the harmful fallout it can cause.

The Beginnings of a Powerful New Hypothesis

The revolution began peacefully enough. Louis Bachelier, an outstanding French mathematics student, examined the fluctuations of commodity prices at the turn of the twentieth century in his doctoral dissertation.3 He concluded that commodity price movement appeared random, that is, without any predictable pattern. Recent price data were of no help in predicting future price fluctuations. His findings were the first contributions to what would become known as the “random walk hypothesis.”

Bachelier’s work lay dormant for decades until it was rediscovered in 1960. During the 1960s, other researchers started to study stock price movements. One early study showed that randomly chosen series of numbers, when plotted closely together, looked like charts of stock price fluctuations over time.4 Another study found that stock price movements were remarkably similar to the random movements of minute solid particles, termed “Brownian motion” in physics, after the Scottish botanist Robert Brown, who first observed the phenomenon in 1827.5

In the first half of the 1960s, evidence of the random fluctuations of stock prices mounted. Virtually all of the statistical evidence, which was now considerable, buttressed the hypothesis that successive price movements were independent of past price movements.*14

Essentially, the random walk hypothesis of stock price behavior states that the history of stock price fluctuations and trading volume does not contain any information that will allow the investor to do any better than a buy-and-hold strategy.6 In short, the odds are strong that you won’t beat the averages. The market has “no memory.” As with a friend well into his cups whom you are walking to your car, any of his steps will give you no clue of which way he’ll lurch next.

Not surprisingly, the theory was not accepted by cheering crowds of those practicing technical analysis, one of the two methods commonly used to determine stock and market values. They make their living, after all, by forecasting stock price movements. Technical analysis defines a fairly wide range of techniques, but these are all based on the premise that past information on prices and trading volume gives the sophisticated “expert” a clear picture of what lurks ahead. Unlike fundamental analysis, which will be tossed into the arena next, technical analysis attempts to forecast changes in stock prices solely by studying market data, rather than by looking at a company’s earnings, finances, and prospects. (The latter is what a fundamental analyst does.)

The last thing these grizzled veterans needed to hear from a passel of young, clean-shaven, academic computer geeks was that their methods didn’t work. If the academics were correct, it meant that technical analysis ought to be abandoned.

Obviously, a major war of ideas was about to break out. The technicians threw in their most sophisticated techniques, ranging from advanced charting formations to support and resistance levels. Most technicians work from dozens of separate patterns of prices and methods, relying on their judgment to use the proper combination for each case. They weren’t going down without giving it their best shot, and with the advent of more and more powerful computers, they could produce graphs and charts, data histograms and retrospective analysis as never before.

The Professors Kept A-coming

The academics, though, shot back with their own impressive firepower. Basically two techniques were employed. The first was evidence that showed that stock price movements were random.*15 A number of detailed studies were made in the early 1960s that, updating the prior research, demonstrated that stock movements were random, and the proof of a “trend” so vital to the technician could not be found. Such tests were performed by Arnold Moore in 1964,7 Clive Granger and Oskar Morgenstern in 1963,8 and Eugene Fama in 1965.9 Fama, for example, in his doctoral dissertation, analyzed the prices of the thirty stocks in the Dow Jones Industrial Average at time intervals varying between one day and two weeks for more than five years. His results firmly supported the random walk hypothesis.

If stock prices are random, no matter what price and volume information you have or how strong a chart may look, the chart is meaningless as a predictive instrument, because the next price move is entirely independent of the preceding one. If a stock has moved up seven days in a row, that has no influence on what it will do on the eighth day. It can trade up or down or be unchanged, just as a coin coming up heads many times in a row has a fifty-fifty chance of coming up heads again on the next toss.

In extensive testing employing rigorous statistical procedures, only relatively minor departures from randomness in price movements were found from day to day, week to week, and month to month.10 The central thesis of the technician that markets and stocks display major identifiable trends that may be used to predict future movement stands refuted.

The second argument the technicians used was more difficult to handle. “True,” they could say, “randomness might be proved from day to day or for a number of successive weeks or even months, but aren’t the measurements unfair? The tests have measured only total price data and indicated randomness. Could there not be useful direction in price changes within the time periods studied, such as hour to hour, that the daily or weekly studies did not pick up? Or trends that could be seen only by using selective data such as price-volume statistics together with Kondratieff waves?”*16 In effect, the technicians were inviting the academic researchers to test the systems used in technical analysis rather than price movements as a whole, which they proceeded to do—with devastating results. Some of the first tests, for example, were on different “filter” systems, techniques technicians believe indicate a stock is reversing a trend. If a stock was going down, the filter might show the stock was bottoming and a buy order should be put in or the reverse. But the tests showed that after deducting commissions, filters do not lead to higher returns.11 An investor is as well off with a buy-and-hold strategy or, in layman’s terms, simply buying and holding a portfolio. Relative-strength methods, which buy stocks that are performing better for a time, were also tested and provided no better results.12 The popular Dow Theory in its turn was subjected to scientific scrutiny. Peaks, valleys, support, and resistance levels, although important to technicians, were all shown to have no predictive value. Price action was random after both “sell” and “buy” signals were given.

The computer proved fickle. Though helping the chartist when in his hands, it was also turned against him. In one such test, a computer program analyzed 548 stocks trading on the New York Stock Exchange over a five-year period, scanning the information to identify any one of thirty-two of the most commonly followed patterns, including “head and shoulders” and “triple tops and bottoms.” It was programmed to act on its findings as a chartist would. It would, for example, buy on an upside breakout after a triple top, a strong technical indicator that the stock would go higher; or it would sell after the market had plunged through the support level of a triple bottom, indicating that the stock would drop lower. The computer measured its results, based on these signals, against the performance of the general market. No correlation was found between the buy and sell signals and subsequent price movements. Once again, our old friend the buy-and-hold strategy would have worked just as well.

Price-volume systems met with the same fate. Although this is an important technical tool, the size of neither price nor volume changes appears to have a bearing on the magnitude or direction of the future price; stocks going down on heavy trading may reverse themselves and go up in the next period, as may stocks currently going up on large volume.13

All the tests indicated that mechanical rules do not result in returns any better than the simple buy-and-hold strategy.14 The evidence accumulated is voluminous and strongly supports the random walk hypothesis. With a very minor caveat, some tests have shown a degree of dependence (nonrandom price movements), indicating that a number of marginally profitable trading rules and small filters appear to work consistently. The problem is that the numerous transactions involved in such systems generate substantial commissions, which absorb the expected profit.15

In numerous tests, then, no evidence to date has been able to refute the random walk hypothesis. Technicians nonetheless claim that their methods work, and if you look at their examples they certainly appear to. But, as we have seen, their success is actually only chance in accordance with the laws of averages. Also, of course, their methods work much better with hindsight. Technicians, being human, forget their “misses” and remember their “hits.” If they were wrong, the cause wasn’t the basic technique but its misapplication or the fact that another application or supplementary information was required. Technicians have also claimed that some technical systems work, supported by computer evidence of a correlation over certain periods of time. Undoubtedly this is true, but when a portion of these results was tested more thoroughly, using different time periods and more extensive price information, the correlation disappeared, again showing that the results of the systems were based simply on chance.

When all is said and done, it is impossible to absolutely prove that the random walk hypothesis never works, for that would mean testing not only all the hundreds of systems but also the hundreds of thousands of possible combinations, with the final decision depending on the technician’s own interpretation. An enormous number of tests would be required to do this. Technicians can, quite rightly, say that not all systems have been examined and that in any case their decisions were not based on any one method but were the outcome of judgment and experience. Still, from the substantial evidence accumulated, no system of technical analysis has as yet been found that can put a dent in the random walk hypothesis.

Even though the academic findings have been strongly refuted, chartists and other technicians continue to flourish. They disregard the findings—if they are aware of them—because “their” system is different and hope their clients also ignore the research. Occasionally they let off steam at their antagonists, but usually their protests are without factual support.

When they are not otherwise warning investors to beware, the academics appear to regard technicians with a detached amusement that some may reserve for witch doctors or primitive soothsayers in bygone cultures (I won’t include astrologers today). This tough and dedicated cult of financial forecasters has been taking its lumps for many years, not only from academics but also from the proponents of fundamental analysis. Some unkind fundamental analysts I have known would go so far as to propose a new experiment to the academics: a survey would be taken of shiny suits, frayed collars, and sundry holes in the attire of a sample of technicians, to be measured against a control sample of other Wall Streeters. They believe the findings would show technicians to be far the worse for wear and tear, since most tend to follow their own pronouncements.

Even so, many fundamental analysts are part-time dabblers in the technical mystique. Although fundamental analysis is dominant on Wall Street, most members of this group at one time or another take a peek “to see what the charts tell them,” probably more often in periods of crisis but also as a final affirmation of a decision to buy. We find that in spite of the accumulation of evidence for more than five decades on the unproductiveness of technical analysis, it continues to be widely practiced by investors.

The Academic Blitzkrieg Rolls On

Unfortunately for most money managers and analysts, the academics did not rest on their laurels after this one rather clear-cut victory. Beginning in the mid-1960s, a much more ambitious operation was launched when the researchers asked whether fundamental analysis, the gospel of the large majority of Wall Street professionals, is of any use in obtaining above-average returns in the market.

The fundamentalist school believes that the value of a company can be determined through a rigorous analysis of its sales, earnings, dividend prospects, financial strength, and competitive position, and other related measures. Fundamental analysts have been trained in its many complex nuances and applications, in both undergraduate and graduate schools,*17 and have expanded their knowledge through daily application in their work. Such analysis is used by the great preponderance of mutual funds, bank trust departments, pension funds, and investment advisers, as well as most brokers.

Yet despite their impeccable credentials, the money managers’ record has been anything but awe-inspiring over the years. One of the first studies of money managers’ results was undertaken by the SEC, which measured the performance of investment companies from the late 1920s to the mid-1930s. The report stated, “It can then be concluded with considerable assurance, that the entire group of management investment companies proper (closed end funds) failed to perform better than an index of leading common stocks, and probably performed worse than the index over the 1927–1935 period.”16 Alfred Cowles, an American economist and businessman who founded the Cowles Commission for Research in Economics Research and its journal, Econometrica, analyzed the performance of investment professionals in 1933 and concluded that stock market money managers do not beat the market. The study was updated in 1944, with the same results.17

Numerous scholarly studies of the lackluster performance of money managers were published in the 1960s and 1970s. One of the most exhaustive and devastating studies was by Irwin Friend, Marshall Blume, and Jean Crockett of the Wharton School in 1970.18 The report was widely read and discussed by both academics and professional investors. One hundred thirty-six funds produced an average return of 10.6 percent annually between January 1960 and June 30, 1968. During the same period the shares on the New York Stock Exchange produced an average return of 12.4 percent annually.

If fundamentalists were perplexed by why their results weren’t better, the academics certainly were not. Starting in the mid-1960s, they shifted their research firepower from technical to fundamental analysis. Extensive studies have been made of mutual funds and other professional performance results, the great majority of which subscribe to security analysis. The professors demonstrated once again that the funds and, for that matter, other large accounts of money managers did not outperform the market.19

A powerful cast of financial academics led the charge of EMH against the then-conventional wisdom, including the Nobel laureates Marshall Blume, Merton Miller, William Sharpe, and Myron Scholes, as well as distinguished academics such as Professors Eugene Fama and Richard Roll.

Academic analysis proved as unsparing of the fundamental practitioner’s sensibilities as of the technician’s. Other prevailing beliefs were treated as harshly. No link was found between the widely held belief that higher portfolio turnover led to better performance. Rapid turnover does not improve results but seems to damage them slightly. No relationship was found between performance and sales charges, although mutual funds with higher sales charges often claimed they provided better results.20 In sum, the reports firmly concluded, mutual funds do not outperform the market.

The results hardly comforted fund managers, who like to represent to clients that they provide superior performance. Mutual fund managers not only underperformed the market but, if we adjust for risk as the academics defined it, often fared worse. At the time, the money managers appeared to be routed as badly as the Inca armies who fled from Francisco Pizarro and his terrifying cannon and cavalry.*18 But, as we’ll see, it’s one thing to overthrow the ideology of the native rulers with computer “horses,” and another to rule over the theoretical world.

The Efficient-Market Legions Surge Further

As we’ve seen, the academic investigators proposed a revolutionary new hypothesis that we briefly examined earlier, the efficient-market hypothesis (EMH), which holds that competition between sophisticated, knowledgeable investors keeps stock prices where they should be. This happens because all facts that determine stock prices are analyzed by large numbers of intelligent and rational investors. New information, such as a change in a company’s earnings outlook or a dividend cut, is quickly digested and immediately reflected in the stock price. Like it or not, competition by so many market participants, all seeking hidden values, makes stock prices reflect the best estimates of their real worth. Prices may not always be right, but they are unbiased, so if they are wrong, they are just as likely to be too high as too low.

Since meaningful information enters the marketplace unpredictably, prices react in a random manner. This is the real reason that charting and technical analysis do not work. Nobody knows what new data will enter the market, whether they will be positive or negative, or whether they will affect the market as a whole or only a single company.

A key premise of the efficient-market hypothesis is that the market reacts almost instantaneously (and correctly) to new information, so investors cannot benefit. To prove this contention, researchers conducted a number of studies that they claimed validated the thesis.

One important study explored the market’s understanding of stock splits. In effect, when a stock is split, there is no free lunch—the shareholder still has the same proportionate ownership as before. If naive traders run up the price, said the academics, knowledgeable investors will sell until it is back in line, and market efficiency will be proved. And, said the researchers, this is indeed the case. Tests have confirmed that stock prices after a split was distributed maintained about the same long-term relationship to market movements as before the split.21

Another study measuring the earnings of 261 large corporations between 1946 and 1966 concluded that all but 10 to 15 percent of the data in the earnings reports were anticipated by the reporting month, indicating the market’s awareness of information.22 Other tests came up with similar results, demonstrating, the professors said, that the market quickly adjusts to inputs.

Did these tests actually prove what they claim and cinch the case that markets react quickly to new information? Remember these and other EMH cornerstones. We’ll see shortly whether these tests, along with many others, will boomerang back on the researchers who tossed them so confidently at investors.

The Capital Asset Pricing Model: EMH Victory or Trojan Horse?

With the stock market declared efficient, the theorists could tell investors they should expect only a fair return—that is, a return commensurate with the exact risk of purchasing a particular stock. The capital asset pricing model (CAPM), a younger brother of EMH, comes into play here. Risk (as CAPM defines it) is volatility. The greater the volatility of the security or portfolio, measured against the market, the greater the risk.

The most common quantitative measure of volatility is designated as beta.*19 It can be the volatility of a mutual fund, a portfolio, or a common stock. To calculate it, a mutual fund, a portfolio, or a stock must be measured against a benchmark, normally a stock index. Stock portfolios of larger-capitalization mutual funds or other similar portfolios are often measured against the S&P 500, which is assigned a beta of 1. If the mutual fund has a higher beta, the academics say, it is more risky than the index. Conversely, a lower beta is considered less risky. Over time, risk and return must always be in line, say the theorists. Securities or portfolios with greater risk should provide larger returns; those with less risk, lower returns. Thus the money manager whose portfolio outperforms the market by 3 percent a year might have a much higher beta for his portfolio. Since the academics assume that there is a direct correlation between risk (volatility) and return, after adjusting for volatility, the manager who outperforms the market by 3 percent might actually be doing worse than a manager who outperforms by 1 percent. The academics call this measurement risk-adjusted return.

The efficient-market hypothesis, elegant in its simplicity, is intuitively appealing because it explains the single most obvious mystery about investing: how can tens of thousands of intelligent, hardworking professional stock pickers be endlessly outwitted by the market and embarrassed by their selections?

EMH has much wider implications than the random walk hypothesis,*20 which said only that investors would not benefit from technical analysis. If correct, the new argument tears the heart out of fundamental research. No amount of fundamental analysis, including the exhaustive high-priced studies done by major Wall Street brokerage houses, will give investors an edge. If enough buyers and sellers correctly evaluate new information, under- and overvalued stocks will be rare indeed.23

The implications are sweeping. If you’re in the market, the theory tells you to buy and hold rather than trade a lot. Trading increases the commissions you pay without increasing your return. The theory also tells you to assume that investors who have outperformed the market in the past were just plain lucky and that you have no reason to believe they will continue to do so.24

The semistrong form of EMH contends that no mutual fund, million-dollar-plus money manager, or individual investor, no matter how sophisticated, can beat the market using public information. This is the most widely accepted form of EMH today.

If the “weak form” of EMH, the random walk hypothesis, made some dramatic claims, the “strong form” doubled down. The stronger form claims that no information, including that known by corporate insiders or by specialists trading the company’s stock (who have confidential material about unexecuted orders on their books), can help you outperform the market. In the few studies done to date, some evidence has surfaced that both insiders25 and specialists26 display an ability to beat the market. However, the strong form of EMH is generally considered too extreme and is not widely accepted.

Further Academic Backing of EMH

Professor Eugene Fama,*21 the leading advocate of EMH (Fortune magazine once referred to him as the Solomon of stocks), reviewed the literature and development of EMH in December 199127 and again in 1998.28 Fama’s reports were thorough, covering hundreds of papers published since his last major review twenty years earlier. The papers strongly supported the semistrong form of EMH.

Despite the tens of thousands of academic papers written about EMH in the last forty-five years, relatively little new research supporting the efficient-market hypothesis has been produced, with two exceptions. Some new studies show daily and weekly predictability in price movements from past movements. But after transaction costs, there is nothing much left to put in your wallet.

A second area of new support for efficient markets, according to Fama, comes from event studies, the study of specific events and how they affect a stock or the market. In the past twenty years, hundreds of such studies have been undertaken. Fama concludes that “on average stock prices adjust quickly to information about investment decisions, dividend changes, changes in capital structure, and corporate-control transactions.”29 He also refers to another large body of research from event studies showing the opposite conclusion: rather than adjusting rapidly to new information, prices adjust slowly and thus inefficiently. Nevertheless, he concludes his review article with this statement: “The cleanest evidence of market-efficiency comes from event studies, especially from event studies on daily returns.”30

Fama adds in his 1998 paper that market efficiency still survives. He spends considerable time discussing anomalies that challenge the efficacy of EMH, stating that they are chance results. But to do so he must be dismissive of major work, ironically by both other scholars and himself, that has stood for decades. The good professor and a large number of other EMH adherents even tend to deny that anomalies actually exist. We will go into these anomalies in some detail in the chapters ahead.

Since the 1960s, EMH researchers have shown a strong inclination to dismiss anomalies by criticizing the methodology of other investigators or on other grounds, as we’ll see in chapter 6.*22*23

The Power of an Idea

Whether EMH, CAPM, and MPT do a good job of describing markets or are pure blather, they have fired the imagination not only of academia but also of Wall Street. Prior to these theories, investment managers and mutual funds were measured on the rates of return their portfolios generated, usually compared with the S&P 500 or the Dow Jones, with no adjustments made for risk. The development of the CAPM resulted in academics’ and consultants’ putting risk measurements into the formula to determine how well a portfolio performed. If a portfolio earned the market return with higher risk, it was deemed, on a risk-adjusted basis, to have underperformed the market; and if it earned the market return with lower risk, it was deemed to have outperformed the market.

Risk measurement has grown into a multibillion-dollar industry and influences the decisions of countless investors, either directly or through their pension funds. If you buy a mutual fund from Morgan Stanley, Charles Schwab, or virtually any other brokerage firm, as millions do, you might take your cue from a Charles Schwab–recommended list. To rank funds, Schwab and most other mass marketers calculate risk as well as performance. Similar risk measurements are used by consultants who recommend money managers to large pension funds and the large brokerage houses, the latter of which in turn recommend money managers for millions of smaller customers. On the theory that you cannot beat the market over time, more than a trillion dollars has also gone into various forms of index funds.

In the space of twenty pages or so, I have documented the academic dismantlement of the two most important market theories of our day—technical and fundamental analysis—and their replacement, at least intellectually, by a third. The new theory has swept through the universities and then progressively through the financial press, among individual and corporate investors, and among professionals themselves. On the assumption that it is impossible to outdo the market, many professionals have radically altered their techniques and their concept of risk—a fitting tribute to the power of an idea conceived less than five decades ago.

The theory so pervades professional investing and academia that Michael Jensen, one of the important contributors to its development, stated some years back, “It’s dangerously close to the point where no graduate student would dare send off a paper criticizing the hypothesis.”31 At the same time, it is sad, for in accepting the new way, the money manager acknowledges that his or her prime raison d’être—to earn superior returns for the client—is beyond reach.

The spread of the new faith was not unlike the conquest of the vast Inca empire by Pizarro and his 180 conquistadors. Like the conquistadors, the scholars used both faith and the sword to annihilate the pagans’ beliefs in the old marketplace. If the true faith was not accepted, why, then there was the sword—the unleashing of volleys of awesome statistics disproving everything the professionals believed. What amazes in retrospect is that the leaders of the new faith subdued millions of investors with a smaller troop than the original conquistadors.

An Achilles’ Heel

But it’s not time to wave the white flag just yet. The golden age of efficient markets may not be destined to last. If we scrutinize the theory more closely after the chilling market events of more recent times, the elegant hypothesis seems to have more than a few major hitches. The professors assumed that investors were as emotionless and as efficient as the computers they used to generate their theory. They completely omitted any psychology, including the compelling work we looked at in the previous chapters that seems to fuel many of the major investor errors we repeatedly make, from their calculations. This by itself could be a fatal blow to the hypothesis, but, as we shall see in the next two chapters, there are other, even more serious flaws.

Although the efficient-market hypothesis seems to unravel some of the investment knots we have seen, such as why professional investors as a group do not outperform the market, it fails to untie many others. How, for instance, could investors en masse underperform the market for decades? How could the bulk of professional opinion prove so consistently and dramatically wrong at crucial market turning points? Or, if investors are so unfailingly rational, how could euphoria and panic prevail as often as we saw in the past chapters? More specifically, if the market is so efficient, how could the 1996–2002 and 2003–2009 bubble and crashes, two of the most severe in economic history, occur within only a few years of each other, particularly when legions of investment professionals were not only trained in, but carefully followed, efficient-market teachings and invested trillions of dollars according to this contemporary bible?

The truth is that the work of outstanding academics, including several dozen Nobel laureates whose research is the bedrock supporting EMH and MPT, has caused heretofore unheard-of market damage in numerous bubbles during the past twenty-five years.

These revolutionary new ideas that sprouted from financial ivory towers around the world won’t give you the odds to beat the market or for that matter even keep you afloat. Many of the basic teachings of EMH have now been conclusively refuted by advanced forms of the same statistical analysis that devastated the investment heathen. Yet contemporary investment practice is built on the belief in efficient markets. Large numbers of investors, though they believe the theory to be bankrupt, don’t know where else to turn.

The efficient-market hypothesis is the natural extension of the last two hundred years of economic theory. At last a place was found for economists to take a stand that would once and for all establish that people behaved in the rational manner economists have assumed for centuries. Their laboratories are, of course, the stock markets and other financial markets. Demonstrating that people actually behave rationally in these markets would be almost akin to discovering the holy books of economics.

It is not surprising, then, that the original “evidence” by Fama, Blume, Jensen, Scholes, and others, that markets were efficient, was enthusiastically greeted by economists, perhaps nowhere more than at the University of Chicago, one of the renowned bastions of laissez-faire. Equally logical was that Chicago became the intellectual heart of this dynamic new research.

Market economists threw down the gauntlet. As one academic noted, “You can see why the idea [of perfectly knowledgeable investors in the stock market] is intriguing. Where else can the economist find the ideal of the perfect market? Here is a place to take a stand if there is such a place.”32 Take a stand they did, although, as we’ll see, it is beginning to look more and more like that last stand by General Custer.

So deep was their conviction that these theories would usher in the golden age of markets, if not economics, that they were convinced the statistics would bear them out. But as we’ll see next, they do not.

Still, to avoid ensnarement by EMH, you must understand its teachings well. There are no government-required labels to warn you away from the EMH-CAPM-MPT trap; lacking knowledge, you can easily be caught up in it. A flawed investment theory is not market-neutral; it can be destructive to the capital of average and sophisticated investors alike. To find the road to successful investment, we must stay well clear of the theory. That will take something more than looking up imploringly to the heavens.

Where, then, do the real odds of market success lie? Are there any real odds at all? We will examine these questions shortly, but first let’s look at why EMH has failed and how its failure affects you as an investor.