With grouping, a sample is sorted by an observable variable and the mean values of the dependent variable in the extreme-ranked groups are compared. We show that test power is maximized when the two extreme groups each contain 27% of the sample, a much larger percentage than that typically used in the literature. This result is not sensitive to the distribution of the dependent variable. We also show that regression is unambiguously more powerful than grouping, even when the independent variable is measured with error.

This paper demonstrates that the evidence supporting the hypothesis that post-earnings announcement drift (PEAD) is caused by investors’ failure to incorporate the implications of current earnings for future earnings is (also) consistent with researchers’ over-differencing an already stationary time-series. Specifically, we show the evidence is driven by a subset of firms where over-differencing of quarterly earnings in estimating earnings surprises is most likely to have occurred. Given the persistence of the PEAD over time, our alternative explanation suggests that the prior research investigating the causes for the PEAD overestimates investors’ naivete.

Predictability of future returns using ex ante information (e.g., analyst forecasts) violates market efficiency. We show that predictability can be due to non-random data deletion, especially in skewed distributions of long-horizon security returns. Passive deletion arises because some firms do not survive the post-event long horizon. Active deletion arises when extreme observations are truncated by the researcher. Simulations demonstrate that data deletion induces a negative relation between future returns and ex ante information variables. Analysis of actual data suggests a 30–50% bias in the estimated relations. We recommend specific robustness checks when testing return predictability using ex ante information.