Contrarian Investment Strategies Read online

  PSYCHOLOGICAL GUIDELINE 6: Don’t expect that the strategy you adopt will prove a quick success in the market; give it a reasonable time to work out.

  Anchoring and Hindsight Biases

  Let’s briefly look at two other systematic heuristic biases that tend to cause investment errors. They, too, are difficult to correct, since they reinforce the others. The first, known as “anchoring,”43 is another simplifying heuristic, which sets up a price for a stock that is not far removed from its current trading price as its anchor. In a complex situation, such as the marketplace, we choose some natural starting point where we think the stock is a good buy or a sale and make adjustments from there. The adjustments are typically insufficient. Thus an investor in 1997 who wanted to buy might have thought a price of $91 was too high for Cascade Communications, a leader in PC networking, and $80 was more appropriate. But Cascade Communications was grossly overvalued at $91 and dropped to $22 before recovering modestly. The heuristic process tends to drop the anchors far too close to the stock’s current price, and investors don’t seem to believe prices can go either much lower if they want to sell or much higher if they want to buy; this often leads to missed opportunities.

  The final bias is also interesting. In looking back at past mistakes, researchers have found, people believe that each error could have been seen much more clearly if only they hadn’t been wearing dark- or rose-colored glasses. The inevitability of what happened seems obvious in retrospect. Hindsight bias seriously impairs proper assessment of past errors and significantly limits what can be learned from experience.44

  Looking back at the 2007–2008 housing crash, many investors blame themselves because it seems the housing bubble was so obvious. We also knew that the number and variations of derivatives were almost mind-boggling. Some of us saw clear instances of major overleverage in subprime lenders such as NovaStar Financial, New Century Financial Corporation, and many others and acted on them early, at the beginning of 2007.

  But what most of us didn’t know until after the financial markets began to melt down was the extent of the leverage in the entire financial system, which was multiplied enormously by the increase of specialized obtuse and complex derivatives as well as the intentional enormous replication of poor-quality subprime loans by numbers of banks and investment bankers. Much of this information came out only in late 2009 and 2010, well after the financial debacle, through the actions of House and Senate committees that subpoenaed e-mails and other information from the banks, credit-rating agencies, and investment banks involved. Most investors also did not know how poor the regulation was prior to the bubble that allowed the leverage to get so out of hand, or how enormously overrated subprime mortgages were by Moody’s, Standard & Poor’s, and Fitch.*11 In early 2011, scathing bipartisan reports were released by the two key Senate commissions: the Wall Street and the Financial Crisis Commission, chaired by Carl Levin (chairman of the Senate Permanent Subcommittee on Investigations); and the Financial Crisis Inquiry Commission, chaired by Phil Angelides, investigating the debacle. The reports evidence how risky lending, poor bond assessments by regulatory and credit-rating agencies, and major conflicts of interest by some of the Street’s largest firms contributed to one of the worst financial disasters in U.S. history. The commissions wrote scathing reports about the actions of major financial players from Goldman Sachs to Washington Mutual; these reports were turned over to the Department of Justice, which is now investigating the charges, some of which may well be criminal. It all seems so obvious in hindsight, but, as the above examples show, it is nightmarishly difficult to see at the time. As a result, we think mistakes are easy to see and are confident we won’t make them again—until we do. This bias, too, is difficult to handle. Again, that walk to the library may be as good a solution as any, showing us that mistakes that now seem so obvious to us were anything but at the time they were made.

  Heuristics and Decisional Biases

  We have repeatedly seen the seductiveness of current market fashions, how prudent investors could be swept away by the lure of huge profits in mania after mania through the centuries. Now, with some knowledge of availability, representativeness, and the other decisional biases we’ve looked at, we can understand why the lure of quick profits has been so persistent and so influential on both the market population and the expert opinion of the day.

  Whatever the fashion, the experts could demonstrate that the performance of a given investment was statistically superior to other less favored ones in the immediate past and sometimes stayed that way for fairly long periods (the case rate beats the base rate). Circumstances are really different this time!

  The pattern repeats itself continually. A buyer of canal bonds in the 1830s or blue-chip stocks in 1929 could argue that though the instruments were dear, each had been a vastly superior holding in the recent past. Along with the 1929 crash and the Depression came a decade-and-a-half-long passion for government bonds at near-zero interest rates. After a whipping in the markets, investors flock to safety, no matter how little they earn. We repeated the desire for safety by dashing into Treasuries at near-zero interest rates after the crash of 2007–2008 and again in August 2011.

  Investing in good-grade common stocks came into vogue in the 1950s and 1960s, and by the end of the latter decade, the superior record of stocks through the postwar era had put investing in bonds into disrepute. Institutional Investor, a magazine exceptionally adept at catching the prevailing trends, presented a dinosaur on the cover of its February 1969 issue with the question “Can the Bond Market Survive?” The article continued, “In the long run, the public market for straight debt might become obsolete.”45

  The accumulation of stocks shot up dramatically in the early seventies just as their rates of return were beginning to decrease. Bonds immediately went on to provide better returns than stocks. Professionals tended to play the fashions of the day, whatever they were. One fund manager, at the height of the dot-com bubble, noted the skyrocketing prices of the high-tech and Internet stocks at the time and said that their performance stood out “like a beacon in the night.” We all know too well the rocks that beacon led to.

  Although market history provides convincing testimony about the ephemeral nature of very high or low returns, generation after generation of investors has been swept up by the prevalent thinking of its day. Each trend has its supporting statistics. The trends have strong affective and heuristic qualities. They are salient and easy to recall and are, of course, confirmed by rising prices. These biases, all of which interact, make it natural to project the prevailing trend well into the future. The common error each time is that although the trend may have lasted for months, sometimes for years, it is not representative and is often far removed from the performance of equities or bonds over longer periods. In hindsight, we can readily identify the errors and wonder why, if they were so obvious, we did not see them earlier.

  The major lesson I hope you take away from this chapter is that the information-processing shortcuts we use all the time, though highly efficient in day-to-day situations, systematically work against us in the marketplace. We just are not good information processors in many ways, and the effects of cognitive biases on our decision making are enormous, not only in investing, but in economics, management, and virtually every area of life.

  Even so, these findings have been almost completely disregarded by mainstream economics. The prevailing theory of the markets, the efficient-market hypothesis, rejects virtually all of the psychology we’ve just considered. Instead it posits that investors are rational information processors at almost all times. In order to see just how misleading the tenets of this hypothesis are and how damaging they can be to your investment earnings, let’s now take a close look at the hypothesis and the many reasons it should be discarded—or at the least should not govern your own investment decisions.

  Part II

  The New Dark Ages

  Chapter 4

  Conquistadors in Tweed Jacket
s

  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 academic
s, 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.