In this paper, we examine the predictability of the cross-section of bank stock returns by taking advantage of the unique set of industry characteristics that prevail in the financial services sector. We examine predictability in the cross-section of bank stock returns using information contained in individual bank fundamental variables such as income from derivative usage, previous loan commitments, loan-loss reserves, earnings, and leverage. We find that variables related to non-interest income, loan-loss reserves, earnings, leverage, and standby letters of credit are all univariately important in forecasting the cross-section of bank stock returns. Surprisingly, neither book-to-market nor firm size is important in our sample. We examine whether this cross-sectional predictability is due to increased risk, or another explanation, such as investor under or overreaction. Our results suggest that this predictability is not due to increased risk, but rather is consistent with investor underreaction to changes in banks’ fundamental variables. Furthermore, out-of-sample testing demonstrates this underreaction appears to be exploitable using simple cross-sectional trading strategies.

Abstract The fragility of the CAPM has led to a resurgence of research that frequently uses trading strategies based on sorting procedures to uncover relations between firm characteristics (such as “value” or “glamour”) and equity returns. We examine the propensity of these strategies to generate statistically and economically significant profits due to our familiarity with the data. Under plausible assumptions, data snooping can account for up to 50 percent of the in‐sample relations between firm characteristics and returns uncovered using single (one‐way) sorts. The biases can be much larger if we simultaneously condition returns on two (or more) characteristics.