Revolutions often spawn counterrevolutions and the efficient market hypothesis in finance is no exception. The intellectual dominance of the efficient-market revolution has more been challenged by economists who stress psychological and behavioral elements of stock-price determination and by econometricians who argue that stock returns are, to a considerable extent, predictable. This survey examines the attacks on the efficient-market hypothesis and the relationship between predictability and efficiency. I conclude that our stock markets are more efficient and less predictable than many recent academic papers would have us believe. 2 A generation ago, the efficient market hypothesis was widely accepted by academic financial economists; for example, see Eugene Fama's (1970) influential survey article, "Efficient Capital Markets." It was generally believed that securities markets were extremely efficient in reflecting information about individual stocks and about the stock market as a whole. The accepted view was that when information arises, the news spreads very quickly and is incorporated into the prices of securities without delay. Thus, neither technical analysis, which is the study of past stock prices in an attempt to predict future prices, nor even fundamental analysis, which is the analysis of financial information such as company earnings, asset values, etc., to help investors select "undervalued" stocks, would enable an investor to achieve returns greater than those that could be obtained by holding a randomly selected portfolio of individual stocks with comparable risk. The efficient market hypothesis is associated with the idea of a "random walk," which is a term loosely used in the finance literature to characterize a price series where all subsequent price changes represent random departures from previous prices. The logic of the random walk idea is that if the flow of information is unimpeded and information is immediately reflected in stock prices, then tomorrow's price change will reflect only tomorrow's news and will be independent of the price changes today. But news is by definition unpredictable and, thus, resulting price changes must be unpredictable and random. As a result, prices fully reflect all known information, and even uninformed investors buying a diversified portfolio at the tableau of prices given by the market will obtain a rate of return as generous as that achieved by the experts. The way I put it in my book, A Random Walk Down Wall Street, first published in 1973, a blindfolded chimpanzee throwing darts at the Wall Street Journal could select a portfolio that would do as well as the experts. Of course, the advice was not literally to throw darts but instead to throw a towel over the stock pages -that is, to buy a broadbased index fund that bought and held all the stocks in the market and that charged very low expenses. By the start of the twenty-first century, the intellectual dominance of the efficient market hypothesis had become far less universal. Many financial economists and statisticians began to believe that stock prices are at least partially predictable. A new breed of economists emphasized psychological and behavioral elements of stock-price determination, and came to believe that future stock prices are somewhat predictable on the basis of past stock price patterns as well as certain "fundamental" valuation metrics. Moreover, many of these economists were even making the far more controversial claim that these predictable patterns enable investors to earn excess risk-adjusted rates of return. This paper examines the attacks on the efficient market hypothesis and the belief that stock prices are partially predictable. While I make no attempt to present a complete survey of the purported regularities or anomalies in the stock market, I will describe the major statistical findings as well as their behavioral underpinnings, where relevant, and also examine the relationship between predictability and efficiency. I will also describe the major arguments of those who believe that markets are often irrational by analyzing the "crash of 1987," the "Internet bubble" of the fin de siecle, and other specific irrationalities often mentioned by critics of efficiency. I conclude that our stock markets 4 are far more efficient and far less predictable than some recent academic papers would have us believe. Moreover, the evidence is overwhelming that whatever anomalous behavior of stock prices may exist, it does not create a portfolio trading opportunity that enables investors to earn extraordinary risk adjusted returns. At the outset, it is important to make clear what I mean by the term "efficiency". I will use as a definition of efficient financial markets that they do not allow investors to earn above-average returns without accepting above-average risks. A well-known story tells of a finance professor and a student who come across a $100 bill lying on the ground. As the student stops to pick it up, the professor says, "Don't bother-if it were really a $100 bill, it wouldn't be there." The story well illustrates what financial economists usually mean when they say markets are efficient. Markets can be efficient in this sense even if they sometimes make errors in valuation, as was certainly true during the 1999-early 2000 internet bubble. Markets can be efficient even if many market participants are quite irrational. Markets can be efficient even if stock prices exhibit greater volatility than can apparently be explained by fundamentals such as earnings and dividends. Many of us economists who believe in efficiency do so because we view markets as amazingly successful devices for reflecting new information rapidly and, for the most part, accurately. Above all, we believe that financial markets are efficient because they don't allow investors to earn above-average risk-adjusted returns. In short, we believe that $100 bills are not lying around for the taking, either by the professional or the amateur investor. What I do not argue is that the market pricing is always perfect. After the fact, we know that markets have made egregious mistakes as I think occurred during the recent 5 Internet bubble. Nor do I deny that psychological factors influence securities prices. But I am convinced that Benjamin Graham (1965) was correct in suggesting that while the stock market in the short run may be a voting mechanism, in the long run it is a weighing mechanism. True value will win out in the end. And before the fact, there is no way in which investors can reliably exploit any anomalies or patterns that might exist. I am skeptical that any of the "predictable patterns" that have been documented in the literature were ever sufficiently robust so as to have created profitable investment opportunities and after they have been discovered and publicized, they will certainly not allow investors to earn excess returns. In this section, I review some of the patterns of possible predictability suggested by studies of the behavior of past stock prices. The original empirical work supporting the notion of randomness in stock prices looked at such measures of short-run serial correlations between successive stock-price changes. In general, this work supported the view that the stock market has no memorythe way a stock price behaved in the past is not useful in divining how it will behave in the future; for example, see the survey of articles contained in Cootner (1964) . More recent work by Lo and MacKinlay (1999) finds that short-run serial correlations are not zero and that the existence of "too many" successive moves in the same direction enable 6 them to reject the hypothesis that stock prices behave as random walks. There does seem to be some momentum in short-run stock prices. Moreover, Lo, Mamaysky and Wang (2000) also find, through the use of sophisticated nonparametric statistical techniques that can recognize patterns, some of the stock-price signals used by "technical analysts" such as "head and shoulders" formations and "double bottoms", may actually have some modest predictive power. Economists and psychologists in the field of behavioral finance find such shortrun momentum to be consistent with psychological feedback mechanisms. Individuals see a stock price rising and are drawn into the market in a kind of "bandwagon effect." For example, Shiller (2000) describes the rise in the U.S. stock market during the late 1990s as the result of psychological contagion leading to irrational exuberance. The behavioralists offered another explanation for patterns of short-run momentum -a tendency for investors to underreact to new information. If the full impact of an important news announcement is only grasped over a period of time, stock prices will exhibit the positive serial correlation found by investigators. As behavioral finance became more prominent as a branch of the study of financial markets, momentum, as opposed to randomness, seemed reasonable to many investigators. However, there are several factors that should prevent us from interpreting the empirical results reported above as an indication that markets are inefficient. First, while the stock market may not be a mathematically perfect random walk, it is important to distinguish statistical significance from economic significance. The statistical dependencies giving rise to momentum are extremely small and are not likely to permit investors to realize excess returns. Anyone who pays transactions costs is unlikely to 7 fashion a trading strategy based on the kinds of momentum found in these studies that will beat a buy-and-hold strategy. Indeed, Odean (1999) suggests that momentum investors do not realize excess returns. Quite the opposite -a sample of such investors suggests that such traders did far worse than buy-and-hold investors even during a period where there was clear statistical evidence of positive momentum. This is so because of the large transactions costs involved in attempting to exploit whatever momentum exists. Similarly, David Lesmond, Michael Schill, and Chunsheng Zhou (2001) find that the transactions costs involved in undertaking standard "relative strength" strategies are not profitable because of the trading costs involved in their execution. Second, while behavioural hypotheses about bandwagon effects and underreaction to new information may sound plausible enough, the evidence that such effects occur systematically in the stock market is often rather thin. For example, Eugene Fama (1998) surveys the considerable body of empirical work on "event studies" that seeks to determine if stock prices respond efficiently to information. The "events" include such announcements as earnings surprises, stock splits, dividend actions, mergers, new exchange listings, and initial public offerings. Fama finds that apparent underreaction to information is about as common as overreaction, and post-event continuation of abnormal returns is as frequent as post-event reversals. He also shows that many of the return "anomalies" arise only in the context of some very particular model, and that the results tend to disappear when exposed to different models for expected "normal" returns, different methods to adjust for risk, and when different statistical approaches are used to measure them. For example, a study, which gives equal-weight to post-announcement returns of many stocks, can produce different results 8 from a study that weight the stocks according to their value. Certainly, whatever momentum displayed by stock prices does not appear to offer investors a dependable way to earn abnormal returns. The key factor is whether any patterns of serial correlation are consistent over time. Momentum strategies, which refer to buying stocks that display positive serial correlation and/or positive relative strength, appeared to produce positive relative returns during some periods of the late 1990s but highly negative relative returns during 2000. It is far from clear that any stock-price patterns are useful for investors in fashioning an investment strategy that will dependably earn excess returns. Many predictable patterns seem to disappear after they are published in the finance literature. As Schwert (2001) points out, there are two possible explanations for such a pattern. One explanation may be that researchers are always sifting through mountains of financial data. Their normal tendency is to focus on results that challenge perceived wisdom, and every now and again, a combination of a certain sample and a certain technique will produce a statistically significant result that seems to challenge the efficient markets hypothesis. Alternatively, perhaps practitioners learn quickly about any true predictable pattern and exploit it to the extent that it becomes no longer profitable. My own view is that such apparent patterns were never sufficiently large or stable to guarantee consistently superior investment results and certainly such patterns will never be useful for investors after they have received considerable publicity. The so-called January effect, for example, seems to have disappeared soon after it was discovered. 9 Long-run Return Reversals In the short-run, when stock returns are measured over periods of days or weeks, the usual argument against market efficiency is that some positive serial correlation exists. But many studies have shown evidence of negative serial correlation -that is, return reversals --over longer holding periods. For example, Fama and French (1988) found that 25 to 40 percent of the variation in long holding period returns can be predicted in terms of a negative correlation with past returns. Similarly, Poterba and Summers (1988) found substantial mean reversion in stock market returns at longer horizons. Some studies have attributed this forecastability to the tendency of stock market prices to "overreact." DeBondt and Thaler (1995) , for example, argue that investors are subject to waves of optimism and pessimism that cause prices to deviate systematically from their fundamental values and later to exhibit mean reversion. They suggest that such overreaction to past events is consistent with the behavioral decision theory of Kahneman and Tversky (1982), where investors are systematically overconfident in their ability to forecast either future stock prices or future corporate earnings. These findings give some support to investment techniques that rest on a "contrarian" strategy, that is, buying the stocks, or groups of stocks, that have been out of favor for long periods of time and avoiding those stocks that have had large run-ups over the last several years. There is indeed considerable support for long-run negative serial correlation in stock returns. However, the finding of mean reversion is not uniform across studies and is quite a bit weaker in some periods than it is for other periods. Indeed, the strongest empirical results come from periods including the Great Depression -which may be a 10 time with patterns that do not generalize well. Moreover, such return reversals for the market as a whole may be quite consistent with the efficient functioning of the market since they could result, in part, from the volatility of interest rates and the tendency of interest rates to be mean reverting. Since stock returns must rise or fall to be competitive with bond returns, there is a tendency when interest rates go up for prices of both bond and stocks to go down, and as interest rates go down for prices of bonds and stocks to go up. If interest rates mean revert over time, this pattern will tend to generate return reversals, or mean reversion, in a way that is quite consistent with the efficient functioning of markets. Moreover, it may not be possible to profit from the tendency for individual stocks to exhibit patterns of return reversals. Fluck, Malkiel and Quandt (1997) simulated a strategy of buying stocks over a 13-year period during the 1980s and early 1990s that had particularly poor returns over the past three to five years. They found that stocks with very low returns over the past three to five years had higher returns in the next period, and that stocks with very high returns over the past three to five years had lower returns in the next period. Thus, they confirmed the very strong statistical evidence of return reversals. However, they also found that returns in the next period were similar for both groups, so they could not confirm that a contrarian approach would yield higher-thanaverage returns. There was a statistically strong pattern of return reversal, but not one that implied an inefficiency in the market that would enable investors to make excess returns. Seasonal and Day-of-the-Week Patterns
Empirical research suggested that market prices can be partially predicted REF .
The Efficient Market Hypothesis and Its Critics
Journal of Economic Perspectives
Economics