Contrarian Investment Strategies Page 18

  [I]n mergers and tender offers, the average increase in stock prices of target firms in the three days around the announcement is more than 15%. Since the average daily return on stocks is only about .04% (10% per year divided by 250 trading days), different ways of measuring expected returns have little effect on the inference that target shares have large abnormal returns in the days around merger and tender announcements.46

  Again this appears to be a wee break with reality. Merger and tender offers are almost always made at a higher price, sometimes significantly higher than the price before the offer.

  That stock prices go up 15 percent on average over the three days around the announcement of an IPO certainly is not proof that markets are efficient. Again, as seen earlier in this chapter, highly regarded EMH theorists make a major mistake in assuming that just because stocks respond to new information, they are responding correctly to it. Often the bids for both mergers and tender offers are raised as company management demands and frequently receives higher prices (particularly with hostile takeovers). The stocks also often trade at a discount to the proposed offering price for an extended period of time.

  The 15 percent that stocks appreciate around the date of the initial announcement of an offer is half of the approximately 30 percent total increase that shareholders received on average, for periods ranging from a few weeks to a couple of months before or after the offer was consummated, according to other studies.47 Even allowing for the occasional offer that is dropped, the first tender price appears far too low. The market again seems to be incorrect in its initial pricing of tender offers and mergers.

  No evidence is provided that the initial reaction to the news is the correct one, as prices far too frequently move up from the trading levels following the announcements. This premise spawned a generation of risk arbitrageurs who have made enormous returns on their capital. The study is somewhat unsophisticated in its knowledge of mergers and acquisitions, and from the evidence available it appears that markets are inefficient rather than efficient when measuring initial offerings relative to the final takeover price.

  To use a chess analogy, it’s like concluding that if I move a chess piece after a move by the current world chess champion, the fact that I pushed the piece at all puts me on his level of play. Once again the theory: any price movement is the correct price movement. Nonsensical, yes; a nice daydream, yes; but also the essence of the weak “proofs” of EMH we are looking at.

  Other Evidence Against Efficiency

  Our case further strengthens with other evidence that efficiency is quite a bit rarer than the theorists admit. The evidence that markets do not adjust quickly to new information keeps mounting. Roni Michaely, Richard Thaler (one of the pioneers of behavioral finance), and Kent Womack studied the subject in 1994.48 The three researchers measured how stocks behaved after a dividend cut or increase during the 1964–1988 period. The average stock underperformed the market by 11 percent in the year after the announcement of a dividend cut, and by 15.3 percent for the three-year period. It outperformed by 7.5 percent in the year following a dividend increase, and by 24.8 percent for the three years afterward. This study indicates again that markets do not adjust to new information quickly.

  A number of other studies have shown that the market is slow to digest new information. Several researchers have found that when a company reports an earnings surprise (that is, a figure above or below the consensus of analysts’ forecasts), prices move up when the surprise is positive and down when it is negative for the next three quarters.49 Jeffery Abarbanell and Victor Bernard, as will be noted in chapter 9, have shown that analysts don’t adjust their earnings estimates quickly after past mistakes.50 The “buy-and-hold” contrarian strategies presented in chapters 11 and 1251 demonstrated that “worst” stocks with earnings surprises continued to outperform and “best” stocks to underperform the market for periods of up to nine months. These findings that markets are slow to react fully to information, rather than reacting instantaneously, appear to shoot another arrow through EMH.

  Finally, Robert Shiller argues that if markets were efficient, when we look back at history, stock prices at a given time should be related to prices that we can say are “rational.”52 To find out if this is true, he looked back at prices that would be considered rational in light of the dividends subsequently paid. The study covers the 1871–1979 period.

  The rational index Shiller created after the fact follows a smooth, stable path, whereas the actual market index veers sharply above or below it for extended periods, displaying substantial volatility. Shiller concluded, “[S]tock price volatility over the past century appear[s] to be far too high . . . to be attributed to new information about future real dividends.”53 In short, markets over that long term did not respond accurately to information but rather moved far higher or lower than was warranted.

  A Black Hole in EMH Theory

  Recall that adherents of EMH believe that knowledgeable investors keep prices where they should be. Unfortunately, this does not appear to be true; and if it is not true, then the most important axiom of the hypothesis is gone.

  One of the key questions efficient-market adherents have not examined is how professional investors keep prices in line with values. What methods do they use to do so? It’s doubtful that this question has ever been explained by EMH believers, and is anything other than a critical theoretical assumption. Perhaps this is due to the fact that academics have little understanding of the tools and models knowledgeable investors employ. In chapter 2, we looked at some of the important fundamental methods used to train analysts and money managers, including the use of numerous stock market evaluation techniques and ratios. These methods, if followed, should prevent them from buying stocks that are enormously overpriced.

  If they do buy bubble stocks or other highly overpriced stocks, as actually occurs frequently, they are walking away from their years of experience and training and the valuation methods they have used repeatedly through their careers. Such actions would not be considered rational and should not happen. However, they do occur repeatedly during bubbles or periods of skyrocketing prices, as does selling excellent companies that have been knocked down sharply in periods of panic.

  Importantly, the errors are made, as noted, by the very professional and knowledgeable investors that EMH states keeps markets efficient. If they can’t keep prices at correct levels, how do markets stay efficient? The answer, obviously, is that they don’t. That is the real reason bubbles happen so frequently and go to prices that are often astronomical before they are dashed down in the ensuing panic.

  I know, these seem to be very peculiar rational investors. Since we are in an EMH chapter, I will only refer in passing to the psychology we learned in Part I, which can play into this otherwise unfathomable behavior.

  The Black Swans of EMH

  The history of science teaches us that, given capable, intelligent people, large errors normally do not occur in the development of a hypothesis but rather occur in the assumptions upon which the work is based. Powerful statistical techniques without realistic assumptions take on a life of their own. As bad currency drives out good, more than five decades of bad constructs in finance and economics have driven out good science, leaving few useful contributions for the enormous effort expended.

  To be fair, the concept of efficient markets has come under attack even by financial academics. Edward Saunders, Jr., using the work of Karl Popper, one of the important theorists on scientific method in the last half of the twentieth century, criticizes the scientific approach of EMH.54 Popper stated in a famous analogy that to prove the theory that all swans are white, the researchers should not concentrate their efforts on searching for more white swans. On the contrary, they should search for black swans, because finding even one would destroy the theory.55 EMH researchers have not followed Popper’s teachings. The black swans of EMH are the ever-increasing number of major anomalies outside the theory’s explanatory range. Not onl
y have EMH researchers continued to search for more white swans, but they have put together an unrelenting campaign to exterminate black swans—the anomalies that cannot exist if EMH is correct.

  A Leap of Faith

  Even if the studies claiming that markets are efficient were not problematic, there is a much more serious question about them that was raised in chapter 4. The scientific findings were far too modest to justify the researchers’ all-encompassing, revolutionary conclusions. What proof did the researchers have that the markets respond not only immediately but correctly to new information? None. They accepted market reaction to uncomplicated information as proof positive not simply of reaction but of the correct price reaction to the event. The investigators never attempted to test investors’ ability to interpret far more complex financial and economic data, such as we’ve viewed throughout the text.

  The pattern is common not only to EMH studies but to most areas of mathematical economics. The researchers are very rigorous in their statistical analysis but extremely liberal, if not specious, in their interpretation of broader issues. We saw this in their presentation of the studies that attempted to prove that markets are efficient. The work looked at obvious examples of news affecting markets. These findings, which one could call “slivers of efficiency,” led the researchers by quantum leaps to much broader conclusions. If markets can understand the impact of relatively simple news of mergers or secondary offerings, the reasoning goes, they must be equally capable of gathering and correctly interpreting complex data about companies; industries; economic, monetary, and financial conditions; and the market itself.56

  One must marvel at the boldness of these scholars to build an all-encompassing theory on such flimsy evidence. It is an enormous leap of faith from these simple findings to the conclusion that the market correctly interprets all information, no matter how complex, such as that contained in a bubble or panic, correctly and almost instantaneously. That is like saying that if my daughter, when she was six, could count to a hundred without difficulty, she should also be able to comprehend the theory of relativity—though I’m sure that if asked back then, she would have readily given me an answer, as she did to anything else, but somehow I think it would have missed the mark.

  Unfortunately, when put to the test, the canons of EMH resound like a string of stunning military defeats. None of the risk measurements that the academics credit to rational investors have stood the test of time; here, it seems, we have a financial epicycle. The risk-return paradigm must exist, or EMH will be remembered in history much like the Ptolemaic system, a theory widely popular for a long time that ultimately failed and was discarded.

  We have also examined in some depth the three key assumptions of the efficient-market hypothesis.*40 The three have been discredited by both the relative weakness of and errors in the supposed “proofs,” as well as very strong evidence that disputes the efficiency hypothesis.

  Finally, as we’ve previously established, there is the major problem with the EMH assumption that investors can interpret vast amounts of data. Findings in cognitive psychology and other psychological disciplines demonstrate that this assumption is not accurate. To use another chess analogy, although hundreds of millions of people play the game, there are only a score of grand masters and only one world champion. If people are not equally adept at interpreting the complex world of the chessboard, can they be any more equal at understanding the more complex and significantly more emotional world of markets?

  Ultimately, what chapters 5 and 6 seem to declare to EMH, CAPM, and MPT advocates is the equivalent of “Sorry, it turns out that the sun does not revolve around the earth. Try to accept it.”

  Epilogue to Part II: The Crisis of Modern Economics

  How strong is the support for EMH today? A first thought might be that it is very strong, given the thousands of articles still written by scholars in some of the world’s most prestigious financial and economic journals and its widespread use in the investment world. Yet from what we’ve seen, this revolutionary theory seems to have been built on the flimsiest of foundations—unkind critics might say a house of cards. As we saw, one of the most important pillars of the hypothesis—the theory of the rational measurement of risk—was simply a questionable assumption of financial academics, which was necessary to bind investment theory to economics. Yet for EMH to be correct, it was essential that investors measure risk in this way. The academics willed it to be true. And they continue to do so today, although it almost boggles the mind that some of the world’s finest economists have trapped themselves in such a logically indefensible position.

  The most important reason researchers failed so badly on risk measurement is the manner in which EMH and most other economic investigators conduct their research. Since World War II the social sciences have attempted to become as rigorous as the physical sciences. No discipline has put more effort into this goal than economics. Starting more than sixty years ago, economists held out high hopes that through mathematics they could make the dismal science as predictable as Albert Einstein’s theory of relativity or Johannes Kepler’s laws of planetary motion. Nobel laureate Paul Samuelson, then a young professor of economics at MIT, was the first to integrate the techniques of differential equations, which had met with such success in physics, into a structured approach that could be used to study virtually any economic problem.

  The key assumption was rationality: for a firm it meant maximizing profits; for an individual, maximizing his or her economic desires. Rational behavior is the bedrock of Samuelson’s work. This dubious platform allowed economists to merrily build the most complex mathematical models. Economics could now be converted into a precise physical science.

  A PACT WITH MEPHISTOPHELES

  It would be unfair to say that economists and efficient-market adherents are unaware of the simplicity and vulnerability of their assumptions. The premise of economic rationality is one that has perplexed economic theorists for a long time. The assumption was derived in the golden age of rationalism in the eighteenth and early nineteenth centuries.

  Absolute rationality has all but been discarded in philosophy and the social sciences. It’s commonly accepted that although people often act rationally, there are also many times when they don’t. Market and economic history strongly supports the findings of the behavioral experts.

  Why, then, do most economists persist in using an outmoded concept of human behavior as the cornerstone of their theory? Many agree that the concept of rationality, so central to EMH, is problematic. Still they strongly defend its usefulness; as one book stated decades ago, “To introduce a more realistic assumption would make economic theory very difficult.”57

  Economic theory and later financial theory have been caught on the horns of this dilemma for many decades. Should they espouse realistic assumptions, and if so, what should these be? Or should the assumptions, although acknowledged to be unrealistic, allow extensive analysis, however flawed in terms of practical value? It’s difficult to construct economic theory on numerous behavioral or other assumptions, even if they’re realistic. Rationality gives economists one simple and unwavering assumption to build upon; however, the construct is often seriously flawed.

  Paul Samuelson, as noted, was the pioneer in using highly sophisticated mathematics to solve economic problems. A new economic age had begun. The goal was to make economics as predictable as physics or other physical sciences. The findings of economic theory would be as precise as measuring the exact expansion of steel on a bridge of a given length as the temperature rose. The only solid platform upon which the higher math could be built was the bedrock of rationality. Integrating sociological or psychological theories could result in a number of possible starting points, with new ones added over time, and it would be impossible to anchor complex mathematical formulas on a changing behavioral platform. No, the most practical solution, to most economists, was to use the assumption of consistent rationality, even if it was often incorrect.

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p; As a result, the great majority of economic research gravitated in that direction, despite the warnings of some of the important economic thinkers of the past. John Maynard Keynes, for example, was trained as a mathematician but refused to build his classic theory on unrealistic assumptions. Like his teacher, the great Victorian economist Alfred Marshall, Keynes believed that economics was a branch of logic, not a pseudo–natural science. Marshall himself wrote that most economic phenomena do not lend themselves to mathematical equations and warned against the danger of falling into the trap of overemphasizing the economic elements that could be most easily quantified.

  The Samuelson revolution, with its emphasis on complex quantification parroting the physical sciences, came to totally dominate economics in the postwar period. Mathematics, which pre-Samuelson was a valuable but subordinate aid to reality-based assumptions, now rules economics. Good ideas are often ignored by economists simply because they are not written down in pages of highly complex statistical formulas or don’t employ equations using most of the letters of the Greek alphabet. The vast amount of research published in the academic journals contains minuscule additions to economic thinking but is dressed in sophisticated mathematical models. Bad ideas planted in deep math tend to endure, even when the assumptions are questionable and evidence strongly contradicts the conclusions. As Nobel laureate Paul Krugman noted, “As I see it, the economic profession went astray because economists, as a group, mistook beauty, clad in impressive-looking mathematics, for truth.”58

  Economic ideas and principles once understood by educated readers are now unfathomable to all but the most highly trained mathematical researchers. This would be well and good if economics had achieved the predictability of a physical science. But without realistic assumptions, the dismal science has been broken down rather than been rejuvenated by mathematics. Nobel laureate Joseph Stiglitz, in his 2001 Nobel Prize lecture, spoke to this point in discussing the inadequacy of preferred economic models: “[With one model] I only varied one assumption—the assumption concerning perfect information—and in ways which seemed highly plausible. . . . [As a result] we succeeded in showing not only that the standard theory was not robust. . . . Changing only the one assumption . . . had drastic consequences, [to the theory] but also indicated that an alternative robust paradigm with great explanatory power could be constructed.”59