Contrarian Investment Strategies Read online

  Here’s what they had to read:

  Tom W. is of high intelligence, although lacking in true creativity. He has a need for order and clarity and for neat and tidy systems in which every detail finds its appropriate place. His writing is dull and rather mechanical, occasionally enlivened by somewhat corny puns and flashes of imagination of the sci-fi type. He has a strong drive for competence. He seems to have little feeling and little sympathy for other people, and does not enjoy interacting with others. Self-centered, he nevertheless has a deep moral sense.

  Tom W. is currently a graduate student. Please rank the following nine fields of graduate specialization in order of the likelihood that Tom W. is now a student in that field. Let rank one be the most probable choice:

  Business Administration

  Computer Sciences

  Engineering

  Humanities and Education

  Law

  Library Science

  Medicine

  Physical and Life Sciences

  Social Science and Social Work

  Given the lack of substantive content, the graduate students should have ignored the analysis entirely and made the only remaining logical choices—by the percentage of graduate students in each field: in other words, the base rate. That information had also been provided to them. It was assumed by the experimenters that the graduate students would realize that those were the real data. They had been taught that for a specific situation, the more unreliable the available sketch, the more one should rely on previously established information—that, to use the language of cognitive psychology, the “case rate” (the profile of subject Tom W.) should not have been conflated with the “base rate” (the known percentage of graduate students enrolled in each field). Did the experiment’s group members realize that the base rate was the only accurate data they had? They did not!

  The student subjects relied entirely upon the irrelevant profile, deciding that computer sciences and engineering were the two most likely fields Tom W. would enter, even if those fields had relatively few enrollees.

  But we shouldn’t be too hard on those grad students. Investors in the stock market make similar mistakes all the time, as we’ve seen, being repeatedly impressed by the case rate, even though the substantiating data for it are usually flimsy at best. Too many buyers of red-hot technology stocks in the dot-com bubble failed to consider that the average price decline in very similar stocks in each previous technology bubble collapse had been about 80 percent—repeat, 80 percent. That was the base rate they should have been taking into account.

  Let’s make this into another Psychological Guideline:

  PSYCHOLOGICAL GUIDELINE 5: The greater the complexity and uncertainty in the market, the less emphasis you should place on the case rate, no matter how spectacular near-term returns are, and the more on the base rate.29

  Regression to the Mean

  The previous cognitive biases, stemming from representativeness, buttress one of the most important and consistent sources of investment error. As intuitive statisticians, we do not comprehend the principle of regression to the mean. This statistical phenomenon was noted more than a hundred years ago by Sir Francis Galton, a pioneer in eugenics, and is important in avoiding this major market error. Studying the height of men, Galton found that the tallest men usually had shorter sons, while the shortest men usually had taller sons. Since many tall men come from families of average height, they are likely to have children shorter than they are; similarly, short men are likely to have children taller than they are. In both cases, the height of the children was less extreme than the height of the fathers. In other words, the heights of the children regressed to the mean height for the population as a whole.

  The study of this phenomenon gave rise to the term “regression.” The effects of regression are all around us. In our experience, most outstanding fathers have somewhat disappointing sons or daughters, brilliant wives have duller husbands, people who seem to be ill adjusted often improve, and those considered extraordinarily fortunate eventually have a run of bad luck.30

  Take the reaction we have to a baseball player’s batting average. Although a player may be hitting .300 for the season, his batting will be uneven. He will not get three hits in every ten times at bat. Sometimes he will bat .500 or more, well above his average (or mean); other times he will be lucky to hit .125. Within 162 regular Major League scheduled games, whether the batter hits .125 or .500 in any dozen or so games makes little difference to the average. But rather than realizing that the player’s performance over a week or a month can deviate widely from his season’s average, we tend to focus only on the immediate past record. The player is said to be in a “hitting streak” or a “slump.” Fans, sportscasters, and, unfortunately, the players themselves place too much emphasis on brief periods and forget the long-term average, to which the players will be likely to regress.

  Regression occurs in many instances where it is not expected and yet is bound to happen. Israeli Air Force flight instructors were chagrined after they praised a student for a successful execution of a complex maneuver, because it was normally followed by a poorer one the next time. Conversely, when they criticized a bad maneuver, a better one usually followed. What they did not understand was that at the level of training of these student pilots, there was no more consistency in their maneuvers than in the daily batting figures of baseball players. Bad exercises would be followed by well-executed ones and good exercises by bad ones. Their flying regressed to the mean. Good landings were followed by poor ones; skillful gunnery was followed by missing the target and good formation flying by ragged patterns. Correlating the maneuver quality to their remarks, the instructors erroneously concluded that criticism was helpful to learning and praise detrimental, a conclusion universally rejected by learning theory researchers.31

  How does this work in the stock market? According to the classic work on stock returns of Roger Ibbotson and Rex Sinquefield, then at the University of Chicago,32 stocks have returned 9.9 percent annually on average (price appreciation and dividends) over the eighty-five years to 2010, against a return of about 5.5 percent for bonds. An earlier study by the Cowles Commission for Research in Economics showed much the same return for stocks going back to the 1880s.

  As Figure 3-3 shows, however, the return has been anything but consistent—not unlike the number of hits a .300 career hitter will get in individual games over a few weeks. There have been long periods when stocks have returned more than the 9.9 percent mean. And within each of those periods, there have been times when stocks performed sensationally, rising sometimes 40 percent or more in a year. At other times, they have seemed to free-fall. Stocks, then, although they have a consistent average, also have “streaks” and “slumps.”

  For investors, the long-term rate of return of common stocks, like the long-term batting average of a ballplayer, is the important thing to remember. However, as intuitive statisticians, we find this very hard to do. Market history provides a continuous example of our adherence to the belief that deviations from the norm are, in fact, the new norm.

  Investors of 1927 and 1928 or 1995–1999 thought that returns of 25 to 35 percent were in order from that time on, although they diverged far from the mean. In 1930, 1931, 1973, 1974, and 2007–2008, they believed that huge losses were inevitable, although they, too, deviated sharply from the long-term mean, as Figure 3-3 clearly shows. Investors of mid-1982, observing the insipid performance of the Dow Jones Industrial Average (which was lower at the time than in 1965), believed stocks were no longer a viable investment instrument; BusinessWeek ran a cover story shortly before the great bull market began in July 1982 entitled “The Death of Equities.”33 By 1987, the Dow had nearly quadrupled. And of course, by the late 1990s Wall Street gurus believed that major bear markets were a thing of the past because the Fed had finally mastered the business cycle. Those optimistic thoughts lasted for a shorter period than the average Reno marriage.

  The same scenarios h
ave been enacted at every major market peak and trough. Studies of investment advisers’ buying and selling indicate that most experts are closely tied, if not pilloried, to the current market’s movement. The prevalent belief is always that extreme returns—whether positive or negative—will persist. The far more likely probability is that they are the outliers on a chart plotting returns and that succeeding prices will regress toward the mean, as Figure 3-3 indicates.

  We can lose sight of the relevance of long-term returns by detailed study of a specific trend and intense involvement in it.34 Even those who are aware of long-term standards cannot always see them clearly because of preoccupation with short-term conditions. The long-term return of the market might be viewed like the average height of men. Just as it is unlikely that abnormally tall men will beget even taller men, it is unlikely that abnormally high returns will follow already high returns for long, if at all.

  But because experts in the stock market are no more aware of the principle of regression than anyone else is, each sharp price deviation from past norms is explained by a new, spurious theory.

  The Role of TMI

  Another reason we can so easily lose sight of the longer-term truths we should be factoring into our investing choices is the flood of information we must contend with. TMI, the abbreviation for “too much information,” became common slang in the early 2000s. Though TMI is often used as a jocular interjection, applied to cases when you’ve been forced to know more about some person or something inconsequential than you really care to, there’s a serious side to TMI: the problem of investor information overload. It’s been brought on by the torrent of data available to us today, the processing power of modern computers, and the ubiquitous communication tools we have at our fingertips, and it’s no laughing matter.

  TMI is anything but new. I’m reminded of the soothsayers’ dire warnings to Londoners many months before February 1, 1524. Those expert prognosticators warned, on the basis of massive astrological evidence, that on that day the Thames, that most tranquil of rivers, would suddenly rise hundreds of feet, drowning all who remained in the city. That sent a goodly portion of the city population fleeing weeks before the appointed day, although they soon realized that their prescientific experts were a bit off the mark. The Thames, needless to say, flowed peacefully on within its banks.

  The returning population was enraged; many wanted to throw the pack of soothsayers into the river in sacks. The soothsayers, aware of what might happen, huddled together and told the enraged populace that the stars were never wrong, indeed that the flood predicted would take place. But they had made the slightest of errors in their very complex calculations. The flood would occur on February 1, 1624, not February 1, 1524. The good people of London could go home—at least for a while.

  What exactly happens when we’re confronted with TMI or, to put it in more formal terms, “information overload”?

  The Biases Caused by Information Overload

  In 1959, the polymath Herbert Simon, winner of the Nobel Prize in Economics, was one of the first academics to rigorously look at information overload. In the simplest formulation, what Simon established was that not only does more information not necessarily bring about better decisions; it can lead to poorer ones. This is because humans cannot process large amounts of information effectively. In Simon’s words, “Every human organism lives in an environment that generates millions of bits of new information each second, but the bottleneck of the perceptual apparatus certainly does not admit more than 1,000 bits per second, and possibly much less. We react consciously to only a minute portion of the information that is thrown at us.”35

  And we are biased in the way we do this. Professor Simon notes that this filtering process is not a passive activity “but an active process involving attention to a very small part of the whole and exclusion, from the outset, of almost all that is not within the scope of attention.”36 The key to the bias, he observed, is that when people are bombarded with information they see only the part they are interested in and screen out the rest. More recent research has backed up this finding, including work by Professors Baba Shiv and Alexander Fedorikhin in 1999,37 as well as Professor Nelson Cowan in a research paper in 2000.38

  In the context of the stock market (and other trading markets), who suffers most from information overload? It’s hard to give the nod to either the professional or the individual investor. The sheer volume of information is nearly unfathomable. Consider just the large number of securities analysts, sometimes twenty or more covering a single company, who must put out research reports and updates, not to mention all the information they must process on related companies, the industry, the market, and other important economic and financial data. Information overload? More like information breakdown. These are people working every day, faced with hundreds, if not thousands, of analytical factors that conventional investment theory insists must be considered, from competition and profit margins across a wide range of product lines to the likelihood that industry and company earnings will rise or fall because of new developments.

  This vast, sometimes staggering amount of market information, much of which is complex and contradictory, is nearly impossible to analyze effectively. As we saw in chapter 2, this is an important reason money managers show such poor results over time and only a very small number of analysts and money managers outperform the market. Of course, under these conditions, you can bet that Affect and heuristics are a predictable fallback. (If you very carefully read between the carefully hedged, well-crafted lines of a lot of investment recommendation reports, you may see Affect or a cognitive heuristic peeking out.) This despite the elaborate decision-making process undertaken by highly educated men and women supported by large corporate resources.

  I’m afraid we’re all just going to have to live with market TMI and information overload, but, as we’ll see in Part IV, there are methods that can help us neutralize their negative effects. Let’s now look at a few other ways that our brains go wrong in dealing with other heuristic errors.

  Doing Heuristic Math

  There is yet another powerful heuristic bias stemming from representativeness. This is the intuitive belief that psychological inputs and outputs should be closely correlated. Companies with strong sales growth (the inputs) should be accompanied by rising earnings and profit margins over time (the outputs). We believe that consistent inputs supply greater predictability than inconsistent ones do. Tests, for example, show that people are far more confident that a student is likely to have a B average in the future if he has two Bs rather than an A and a C, although the belief is not statistically valid.39 To translate this into stock market terms, investors have more confidence in a company that has 10 percent earnings growth that rises consistently year after year than they do in one that has 15 percent growth over the same period but is more volatile, i.e., 18 percent in year 1, 3 percent in year 2, 15 percent in year 3, and so on.40

  Another direct application of this finding is the manner in which investors equate a good stock with a rising price and a poor stock with a falling one. One of the most common questions analysts, money managers, and brokers are asked is “If the stock is so good, why doesn’t it go up?” or “If contrarian strategies are so successful, why aren’t they working now?” The answer, of course, is that the value (the input) is often not recognized in the price (the output) for quite some time. Contrarian stocks have outperformed the market for many decades, but that doesn’t mean they can’t underperform for one year or even a few years—remember the lessons we learned about regression to the mean a couple of sections back. Yet investors too often demand immediate, though incorrect, feedback and can make serious mistakes as a consequence.

  Another interesting aspect of this phenomenon is that investors mistakenly tend to place high confidence in extreme inputs or outputs. As we have seen, Internet stocks in the late 1990s were believed to have sensational prospects (the input), confirmed by prices that moved up astronomically (the outp
ut). Companies’ strong fundamentals in each bubble went hand in hand with sharply rising prices, such as prices for HMO stocks in the mid-1990s or the computer software and medical technology stocks of 1968 and 1973. Extreme correlations between rapidly growing dot-com companies and hockey stick–like stock charts look great, and people are willing to accept them as reliable auguries, but as generations of investors have learned the hard way, they don’t last.

  The same thinking is applied to each crash and panic. Analysts and money managers pull back the earnings estimates and outlooks (the inputs) as prices (the outputs) drop. Graham and Dodd, astute market clinicians that they were, saw the input-output relationship clearly. They wrote that “an inevitable rule of the market is that the prevalent theory of common stock valuations has developed in rather close conjunction with the change in the level of prices.”41

  As we’ve seen, demanding immediate success invariably leads to playing the fads or fashions that are currently performing well rather than investing on a solid fundamental basis. An investment course, once plotted, should be given time to work. The immediate investor matching of inputs and outputs serves to repeatedly thwart this goal. The problem is not as simple as it may appear; studies have shown that businessmen and other investors abhor uncertainty.42 To most people in the marketplace, quick psychological input-output matching is an expected condition of successful investing. Taking advantage of this constantly repeated heuristical error by investors is also a critical part of the strategies to be proposed in Part IV. The consistency of this behavior leads us to our next Psychological Guideline.