A large catalog of variables with no apparent connection to risk has been shown to forecast stock returns, both in the time series and the cross-section. For instance, we see medium-term momentum and post-earnings drift in returns -- the tendency for stocks that have had unusually high past returns or good earnings news to continue to deliver relatively strong returns over the subsequent six to twelve months (and vice-versa for stocks with low past returns or bad earnings news); we also see longer-run fundamental reversion -- the tendency for "glamour" stocks with high ratios of market value to earnings, cashflows, or book value to deliver weak returns over the subsequent several years (and vice-versa for "value" stocks with low ratios of market value to fundamentals). To explain these patterns of predictability in stock returns, we advocate a particular class of heterogeneous-agent models that we call "disagreement models." Disagreement models may incorporate work on gradual information flow, limited attention, and heterogeneous priors, but all highlight the importance of differences in the beliefs of
investors. Disagreement models hold the promise of delivering a comprehensive joint account of stock prices and trading volume -- and some of the most interesting empirical patterns in the stock market are linked to volume.
Hong, Harrison, and Jeremy C. Stein.
"Disagreement and the Stock Market."
Journal of Economic Perspectives,