"There are three popular explanations for cross-sectional predictability. First, predictability could be the result of cross-sectional differences in risk, reflected in discount rates (see Fama (1991, 1998)). In this framework, cross-sectional return predictability is expected because return differences simply reflect ex-ante differences in discount rates that were used to value the stocks. There are no surprises here: what happens with returns ex-post was expected by rational investors ex-ante (e.g., Fama and French (1992, 1996)).

The second explanation comes from behavioral finance, and argues that return predictability reflects mispricing (e.g., Barberis and Thaler (2003)). For example, investors may systematically have biased expectations of cash flows, and the anomaly variables are correlated with these mistakes among the cross-section of stocks. When new information arrives, investors update their beliefs, which corrects prices and creates the return-predictability. This theory of biased expectations has been used to explain predictability resulting from price-to earnings ratios (Basu, 1977), long-term reversal (Debondt and Thaler (1985, 1987)), and the value-growth anomaly (Lakonishok, Shleifer and Vishny (1994), La Porta et al. (1997)).

A third explanation for return predictability is data mining. As Fama (1998) points out, academics have likely tested thousands of variables, so it is not surprising to find that some of them predict returns in-sample, even if in reality none of them do. Recognition of a “multiple testing bias” in all types of empirical research dates at least back to Bonferroni (1935) and is stressed more recently in the finance literature by Harvey, Lin, and Zhu (2014).

To differentiate between these three views we compare predictability on days where firm-specific information is publicly released to days where we do not observe news. Using 489,996 earnings announcements and 6,223,007 Dow Jones news items during the period 1979-2013, we find support for the idea that anomalies are the result of mispricing. We find that anomaly portfolios have higher returns on news days compared to non-news days. Comparisons of anomaly returns on and off earnings announcement days shows that anomaly returns are 7 times higher on earnings announcement days and 2 times higher on corporate news days."

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