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