《投资学原理 Investments》课程教学资源（阅读资料）efficient capital markets ii（fama,1991）

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THE JOURNAL OF FINANCE. VOL, XLVI NO 5. DECEMBER 1991 Efficient Capital Markets: II EUGENE F FAMA SEQUELS ARE RARELY AS good as the originals, so I approach this review of the market efficiency literature with trepidation. The task is thornier than it was 20 years ago, when work on efficiency was rather new. The literature is now so large that a full review is impossible, and is not attempted here Instead, I discuss the work that I find most interesting, and I offer my views on what we have learned from the research on market efficiency I. The Theme I take the market efficiency hypothesis to be the simple statement that security prices fully reflect all available information. a precondition for this strong version of the hypothesis is that information and trading costs, the costs of getting prices to reflect information, are always 0(Grossman and Stiglitz (1980)).A weaker and economically more sensible version of th efficiency hypothesis says that prices reflect information to the point where he marginal benefits of acting on information(the profits to be made) do not exceed the marg inal costs(Jensen(1978)) Since there are surely positive information and trading costs, the extreme version of the market efficiency hypothesis is false. Its advantage however, is that it is a clean benchmark that allows me to sidestep the messy problem of deciding what are reasonable information and trading costs. I can focus instead on the more interesting task of laying out the evidence on the adjustment of prices to various kinds of information. Each reader is then free to judge the scenarios where market efficiency is a good approximation(that is, deviations from the extreme version of the efficiency hypothesis are within information and trading costs) and those where some other model is a better simplifying view of the world mbiguity about information and trading costs is not, however, the main obstacle to inferences about market efficiency. The joint-hypothesis problem is more serious. Thus, market efficiency per se is not testable. It must be Graduate School of Business, University of Chicago. The comments of Fischer Black, David Booth, Michael Bradley, Michael Brennan, Stephen Buser, John Campbell, Nai-fu Chen, John Cochrane, George Constantinides, Wayne Ferson, Kenneth French, Campbell Harvey, Richard ppolito, Michael Jensen, Gautam Kaul, Josef Lakonishok, Bill McDonald, Robert Merton, Mark Mitchell. Sam peltz Jay Ritter Harry Re oll hwert, H. Nejat Seyhun, Jay Shanken, Robert Shiller, Andrei Shleifer, R Stulz, Richard Thaler, Robert Vishny, and Jerold Warner are gratefully acknowledged. This research is supported by the National Science Foundation 1575

1576 The Journal of finance tested jointly with some model of equilibrium, an asset-pricing model point, the theme of the 1970 review(Fama(1970b)), says that we can st whether information is properly reflected in prices in the context pricing model that defines the meaning of"properly. As a result, when we find anomalous evidence on the behavior of returns, the way it should be plit between market inefficiency or a bad model of market equilibrium is Does the fact that market efficiency must be tested jointly with an equilib rium-pricing model make empirical research on efficiency uninteresting? Does the joint-hypothesis problem make empirical work on asset-pricing models uninteresting? These are, after all, symmetric questions, with the same answer. My answer is an unequivocal no. The empirical literature efficiency and asset-pricing models passes the acid test of scientific useful ness. It has changed our views about the behavior of returns, across securi ties and through time. Indeed, academics largely agree on the facts that emerge from the tests, even when they disagree about their implications for efficiency. The empirical work on market efficiency and asset pricing models has also changed the views and practices of market professionals As these summary judgements imply, my view, and the theme of this paper, is that the market efficiency literature should it improves our ability to describe the time- series and cross-section behav. ior of security returns. It is a disappointing fact that, because of the joint hypothesis problem, precise inferences about the degree of market efficiency are likely to remain impossible. Nevertheless, judged on how it has improve our understanding of the behavior of security returns, the past research or market efficiency is among the most successful in empirical economics, with good prospects to remain so in the future Il. The main Areas of Research The 1970 review divides work on market efficiency into three categories (1)weak-form tests(How well do past returns predict future returns? ),(2) semi-strong-form tests(How quickly do security prices reflect public informa tion announcements?), and (3) strong-form tests (Do any investors have private information that is not fully reflected in market prices? )At the risk of damning a good thing, i change the categories in this paper. Instead of weak-form tests, which are only concerned with the forecast ower of past returns, the first category now covers the more general area of tests for return predictability, which also includes the burgeoning work on forecasting returns with variables like dividend yields and interest rates Since market efficiency and equilibrium-pricing issues are inseparable, the discussion of predictability also considers the cross-sectional predictability of returns, that is, tests of asset-pricing models and the anomalies (like the size effect)discovered in the tests. Finally, the evidence that there are seasonals in returns (like the January effect), and the claim that security prices are too

1577 volatile are also considered, but only briefly, under the rubric of return predictability For the second and third categories, i propose changes in title, not cover age. Instead of semi-strong-form tests of the adjustment of prices to public announcements, I use the now common title, event studies. Instead of strong. form tests of whether specific investors have information not in market prices, I suggest the more descriptive title, tests for private information Return predictability is considered first, and in the most detail. The detail reflects my interest and the fact that the implications of the evidence on the predictability of returns through time are the most controversial. In brief, the new work says that returns are predictable from past returns, dividend yields, and various term-structure variables. The new tests thus reject the old market efficiency-constant expected returns model that seemed to do well in the early work. This means, however, that the new results run head-on into che joint-hypothesis problem: Does return predictability reflect rational vari ation through time in expected returns, irrational deviations of price from fundamental value, or some combination of the two? We should also acknowl edge that the apparent predictability of returns may be spurious, the result of data-dredging and chance sample-specific condition The evidence discussed below, that the variation through time in expected returns is common to corporate bonds and stocks and is related in plausible ways to business conditions, leans me toward the conclusion that it is real and rational. Rationality is not established by the existing tests, however, and the joint-hypothesis problem likely means that it cannot be established Still, even if we disagree on the market efficiency implications of the new results on return predictability, I think we can agree that the tests enrich our knowledge of the behavior of returns, across securities and through time Event studies are discussed next, but briefly. Detailed reviews of event studies are already available, and the implications of this research for market efficiency are less controversial. Event studies have, however, been a growth industry during the last 20 years. Moreover I argue that, because they come closest to allowing a break between market efficiency and equilib rium-pricing issues, event studies give the most direct evidence on efficiency And the evidence is mostly supportive Finally, tests for private information are reviewed. The new results clarify earlier evidence that corporate insiders have private information that is not fully reflected in prices. The new evidence on whether professional inves ment managers(mutual fund and pension fund) have private information is, however, murky, clouded by the joint-hypothesis problem III. Return Predictability: Time-Varying Expected Returns through time. Unlike the pre- 1970 work, which focused on forecasting re turns from past returns, recent tests also consider the forecast power of

1578 The Journal of Finance variables like dividend yields(D/P), earnings/price ratios(E/P), and term structure variables. Moreover, the early work concentrated on the pre dictability of daily, weekly, and monthly returns, but the recent tests also examine the predictability of returns for longer horizons Among the more striking new results are estimates that the predictable component of returns is a small part of the variance of daily, weekly, and monthly returns, but it grows to as much as 40% of the variance of 2- to 10-year returns. These results have spurred a continuing debate on whether the predictability of long-horizon returns is the result of irrational bubbles in prices or large rational swings in expected returns I first consider the research on predicting returns from past returns Next comes the evidence that other variables(D/P,E/P, and term-structure variables) forecast returns. The final step is to discuss the implications of this work for market efficiency A. Past returns A. 1. Short-Horizon returns In the pre- 1970 literature, the common equilibrium-pricing model in tests of stock market efficiency is the hypothesis that expected returns are con stant through time. Market efficiency then implies that returns are unpre dictable from past returns or other past variables and the best forecast of a return is its historical mean The early tests often find suggestive evidence that daily, week monthly returns are predictable from past returns. For example,Fama (1965)finds that the first-order autocorrelations of daily returns are positive for 23 of the 30 Dow Jones Industrials and more than 2 standard errors from 0 for 1l of the 30. Fishers(1966)results suggest that the autocorrelations of monthly returns on diversified portfolios are positive and larger than those for individual stocks. The evidence for predictability in the early work often lacks statistical power, however, and the portion of the variance of returns explained by variation in expected returns is so small (less than 1% for individual stocks) that the hypothesis of market efficiency and constant expected returns is typically accepted as a good working model In recent work, daily data on NYSE and AMEX stocks back to 1962 [fr the Center for Research in Security Prices(CRSP)] makes it possible to estimate precisely the autocorrelation in daily and weekly returns. For example, Lo and MacKinlay (1988)find that weekly returns on portfolios of NYSE stocks grouped according to size(stock price times shares outstanding) show reliable positive autocorrelation. The autocorrelation is stronger for portfolios of small stocks. This suggests, however, that the results are due in part to the nonsynchronous trading effect (Fisher 1966). Fisher emphasizes that spurious positive autocorrelation in portfolio returns, induced by non synchronous closing trades for securities in the portfolio, is likely to be more important for portfolios tilted toward small stocks To mitigate the nonsychronous trading problem, Conrad and Kaul ( 1988)

Efficient Capital Markets: II 1579 examine the autocorrelation of Wednesday-to- Wednesday returns for size grouped portfolios of stocks that trade on both Wednesdays. Like Lo and MacKinlay (1988), they find that weekly returns are positively autocorre lated, and more so for portfolios of small stocks. The first-order autocorrela tion of weekly returns on the portfolio of the largest decile of NYSE stocks for 1962-1985 is only 09. For the portfolios that include the smallest 40% of NYSE stocks, however, first-order autocorrelations of weekly returns are around. 3, and the autocorrelations of weekly returns are reliably positive out to 4 lags The results of Lo and MacKinlay(1988)and Conrad and Kaul ( 1988)show that, because of the variance reduction obtained from diversification, portfo ios produce stronger indications of time variation in weekly expected returns than individual stocks. Their results also suggest that returns are more predictable for small-stock portfolios. The evidence is, however, clouded by the fact that the predictability of portfolio returns is in part due to nonsyn chronous trading effects that, especially for small stocks, are not completely mitigated by using stocks that trade on successive Wednesdays An eye-opener among recent studies of short-horizon returns is French and Roll (1986). They establish an intriguing fact Stock prices are more variable when the market is open. On an hourly basis, the variance of price changes is 72 times higher during trading hours than during weekend nontrading hours. Likewise, the hourly variance during trading hours is 13 times the overnight nontrading hourly variance during the trading week One of the explanations that French and roll test is a market inefficiency hypothesis popular among academics; specifically, the higher variance of price changes during trading hours is partly transistory, the result of noise trading by uniformed investors( e. g, Black(1986)). Under this hypothesis pricing errors due to noise trading are eventually reversed and this induces negative autocorrelation in daily returns. French and Roll find that the first-order autocorrelations of daily returns on the individual stocks of larger (the top three quintiles of)NYSE firms are positive. Otherwise, the autocor relations of daily returns on individual stocks are indeed negative, to 13 lags Although reliably negative on a statistical basis, however the autocorrela tions are on average close to 0. Few are below -.01 One possibility is that the transitory price variation induced by noise trading only dissipates over longer horizons. To test this hypothesis, French and Roll examine the ratios of variances of N-period returns on individual stocks to the variance of daily returns, for N from 2 days to 6 months. If there is no transitory price variation induced by noise trading(specifically, if price changes are i i.d. ) the N-period variance should grow like N, and the variance ratios(standardized by N)should be close to 1. On the other hand with transitory price variation, the N-period variance should grow less than in proportion to N, and the variance ratios should be less than 1 For horizons(n beyond a week, the variance ratios are more than 2 standard errors below 1, except for the largest quintile of NYSE stocks. But the fractions of daily return variances due to transitory price variation are

1580 The Journal of finance apparently small French and roll estimate that for the average NYSe stock, he upper bound on the transitory portion of the daily variance is 11.7% spurious negative autocorrelation of daily returns due to bid-ask effects(Roll(1984)), the estimate of the transitory portion drops to 4. 1%. The smallest quintile of NYSE stocks produces the largest estimate of the transitory portion of price variation, an upper bound of 26.9%.After correction for bid-ask effects, however, the estimate drops to 4.7%-hardly a number on which to conclude that noise trading results in substantial market inefficiency. French and Roll(1986, p. 23)conclude, "pricing errors. have a trivial effect on the difference between trading and non-trading variances We conclude that this difference is caused by differences in the flow of information during trading and non-trading hours In short, with the crsp daily data back to 1962, recent research is able to show confidently that daily and weekly returns are predictable from past returns. The work thus rejects the old market efficiency-constant expected returns model on a statistical basis. The new results, however tend to confirm the conclusion of the early work that, at least for individual stocks variation in daily and weekly expected returns is a small part of the variance of returns. The more striking, but less powerful, recent evidence on the predictability of returns from past returns comes from long-horizon returns A.2. Long-Horizon Returns The early literature does not interpret the autocorrelation in daily and weekly returns as important evidence against the joint hypothesis of market efficiency and constant expected returns. The argument is that, even when the autocorrelations deviate reliably from 0(as they do in the recent tests) chey are close to 0 and thus economically insignificant The view that autocorrelations of short-horizon returns close to 0 imply economic insignificance is challenged by Shiller(1984)and Summers(1986) They present simple models in which stock prices take large slowly decaying swings away from fundamental values (fads, or irrational bubbles), but short-horizon returns have little autocorrelation. In the Shiller -Summers model, the market is highly inefficient, but in a way that is missed in tests on short-horizon returns value. Suppose daily prices are a first-order autoregression(ARl) with slope less than but close to 1. All variation in the price then results from long mean-reverting swings away from the constant fundamental value. Over short horizons, however, an ARl slope close to 1 means that the price looks like a random walk and returns have little autocorrelation. Thus in tests on short-horizon returns, all price changes seem to be permanent when funda mental value is in fact constant and all deviations of price from fundamental value are temporary In his comment on Summers(1986), Stambaugh(1986)points out that although the Shiller-Summers model can explain autocorrelations of short

Efficient Capital Markets: II 1581 horizon returns that are close to 0, the long swings away from fundamental value proposed in the model imply that long-horizon returns have strong negative autocorrelation. (In the example above, where the price is a station ary ARl, the autocorrelations of long-horizon returns approach.5. ) Intu itively, since the swings away from fundamental value are temporary, over ong horizons they tend to be reversed. Another implication of the negative of returns should grow less than in proportion to the return horicon variance autocorrelation induced by temporary price movements is that the The Shiller-Summers challenge spawned a series of papers on the pre dictability of long-horizon returns from past returns. The evidence at first seemed striking, but the tests turn out to be largely fruitless. Thus, Fama and French(1988a)find that the autocorrelations of returns on diversified portfolios of NYSE stocks for the 1926-1985 period have the pattern pre dicted by the Shiller-Summers model. The autocorrelations are close to 0 at short horizons, but they become strongly negative around -0.25 to-0.4, for 3- to 5-year returns. Even with 60 years of data, however, the tests on long-horizon returns imply small sample sizes and low power. More telling Then Fama and French delete the 1926-1940 period from the tests, the evidence of strong negative autocorrelation in 3-to 5-year returns disappears Similarly, Poterba and Summers(1988)find that, for N from 2 to 8 years the variance of N-year returns on diversified portfolios grows much less than in proportion to N. This is consistent with the hypothesis that there is negative autocorrelation in returns induced by temporary price swings. Even with 115 years (1871-1985) of data, however, the variance tests for long horizon returns provide weak statistical evidence against the hypothesis that returns have no autocorrelation and prices are random walks Finally, Fama and French (1988a)emphasize that temporary swings in stock prices do not necessarily imply the irrational bubbles of the shille Summers model Suppose(1)rational pricing implies an expected return that is highly autocorrelated but mean-reverting, and(2) shocks to expected returns are uncorrelated with shocks to expected dividends. In this situation expected-return shocks have no permanent effect on expected dividends discount rates, or prices. a positive shock to expected returns generates a price decline (a discount rate effect) that is eventually erased by the tem porarily higher expected returns. In short, a ubiquitous problem in time-series tests of market efficiency, with no clear solution, is that irrational bubbles n stock prices are indistinguishable from rational time-varying expected A. 3. The contrarians DeBondt and Thaler(1985, 1987)mount an aggressive empirical attack on market efficiency, directed at unmasking irrational bubbles. They find that the NYSE stocks identified as the most extreme losers over a 3- to 5-year ing years, expecially in January of the following years. Conversely, the stocks identified as extreme winners tend to have weak returns relative to

1582 The Journal of finance the market in subsequent years. They attribute these results to market overreaction to extreme bad or good news about fi rms Chan(1988)and Ball and Kothari (1989)argue that the winner-loser results are due to failure to risk-adjust returns (DeBondt and Thaler(1987) disagree )Zarowin( 1989)finds no evidence for the DeBondt-Thaler hypothe sis that the winner-loser results are due to overreaction to extreme changes in earnings. He argues that the winner-loser effect is related to the size effect of Banz(1981); that is, small stocks, often losers, have higher expected returns than large stocks. Another explanation, consistent with an efficient market, is that there is a risk factor associated with the relative economic performance of firms (a distressed-firm effect) that is compensated in a rational equilibrium-pricing model(Chan and Chen(1991) We may never be able to say which explanation of the return behavior of extreme winners and losers is correct, but the results of DeBondt and Thaler and their critics are nevertheless interesting. ( See also Jagedeesh(1990) Lehmann(1990), and Lo and MacKinlay(1990), who find reversal behavior in the weekly and monthly returns of extreme winners and losers. Lehmann's weekly reversals seem to lack economic significance. When he accounts for spurious reversals due to bouncing between bid and ask prices, trading costs of 0. 2% per turnaround transaction suffice to make the profits from his reversal trading rules close to O. It is also worth noting that the short-term reversal evidence of Jegadeesh, Lehmann, and Lo and MacKinlay may to some extent be due to CRSP data errors, which would tend to show up price reversals. B. Other Forecasting variables The univariate tests on long-horizon returns of Fama and French(1988a) and Poterba and Summers(1988)are a statistical power failure. Still, they provide suggestive material to spur the search for more powerful tests of the hypothesis that slowly decaying irrational bubbles, or rational time-varying expected returns, are important in the long-term variation of prices There is a simple way to see the power problem. An autocorrelation is the slope in a regression of the current return on a past return. Since variation through time in expected returns is only part of the variation in returns tests based on autocorrelations lack power because past realized returns are noisy measures of expected returns. Power in tests for return predictability can be enhanced if one can identify forecasting variables that are less noisy proxies for expected returns that past returns B. 1. The Evidence There is no lack of old evidence that short-horizon returns are predictable from other variables. a puzzle of the 1970s was to explain why monthl stock returns are negatively related to expected inflation(Bodie(1976) Nelson(1976), Jaffe and Mandelker(1976), Fama(1981)and the level of short-term interest rates (Fama and Schwert(1977)). Like the autocorrela tion tests, however, the early work on forecasts of short-horizon returns from

Efficient Capital Markets: II 1583 expected inflation and interest rates suggests that the implied variation in xpected returns is a small part of the variance of returns-less than 3% for monthly returns. The recent tests suggest, however, that for long-horizon returns, predictable variation is a larger part of return variances Thus, following evidence(Rozeff (1984), Shiller(1984) that dividend yields (/P) forecast short-horizon stock returns, Fama and French(1988b)use D/P NYSE stocks for horizons from 1 month to 5 years. As shted portfolios of explains small fractions of monthly and quarterly return variances. frac tions of variance explained grow with the return horizon, however, and are E/Ratios, especially when past earnings(E)are averaged over 10-30 years, have reliable forecast power that also increases with the return horizon Unlike the long-horizon autocorrelations in Fama and French (1988a),the long-horizon forecast power of D /P and E/P is reliable for periods after 1940 Fama and French(1988b) argue that dividend yields track highly autocor related variation in expected stock returns that becomes a larger fraction of return variation for longer return horizons. The increasing fraction of the variance of long-horizon returns explained by D/P is thus due in large part to the slow mean reversion of expected returns. Examining the forecast power of variables like D/P and E /P over a range of return horizons nevertheless res striking perspective on the implications of slow-moving expected turns for the variation of returns B 2. Market Efficiency The predictability of stock returns from dividend yields (or E/P) is not in itself evidence for or against market efficiency. In an efficient market, the forecast power of D/P says that prices are high relative to dividends when discount rates and expected returns are low, and vice versa. On the other hand, in a world of irrational bubbles, low D /P signals irrationally high stock prices that will move predictably back toward fundamental values. To judge whether the forecast power of dividend yields is the result of rational variation in expected returns or irrational bubbles, other information must be used. As always, even with such information, the issue is ambiguou For example, Fama and French (1988b) show that low dividend yields imply low expected returns, but their regressions rarely forecast negative returns for the value. and equally weighted portfolios of NYSE stocks. In their data, return forecasts more than 2 standard errors below 0 are never observed. and more than 50%o of the forecasts are more than 2 standard errors above 0. Thus there is no evidence that low D/P signals bursting bubbles, that is, negative expected stock returns. a bubbles fan can argue, however, that because the unconditional means of stock returns are high, a bursting bubble may well imply low but not negative expected returns. Conversely, if here were evidence of negative expected returns, an efficient-markets type could argue that asset-pricing models do not say that rational expected returns are always positive

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