Building on the work of Bernard and Thomas 1990, we develop a model to infer the degree to which the information in an earnings announcement is incorporated into investors' expectations for the subsequent earnings announcement at any point in time between the two announcements. We are unable to reject the null hypothesis that investors' earnings expectations are based on a seasonal random walk and reflect none of the implications of the immediately prior earnings announcement up to 15 trading days after that announcement. By mid‐quarter, expectations are significantly more sophisticated than a seasonal random walk. Two trading days before the next earnings announcement, as much as one half of the information in the prior earnings announcement is reflected in earnings expectations. We also find that the dissemination of information, albeit predictable information, speeds the incorporation of prior earnings information into earnings expectations. Our results suggest that as information about future earnings that could have been discerned from the earlier announcements (because past earnings surprises predict future ones) is disseminated in a more transparent form, investors revise their earnings expectations to reflect this information. Thus, the investors' expectations appear to incorporate more and more of the serial correlation in earnings surprises as the quarter progresses, even though they do not consider per se the serial correlation in earnings surprises in forming their expectations.

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.

In this study of sell-side analysts’ forecasts, we explore the effects of analyst aptitude, learning-by-doing, and the internal environment of the brokerage house on forecast accuracy. Our results indicate that analysts’ aptitude and brokerage house characteristics are associated with forecast accuracy, while learning-by-doing is only associated with forecast accuracy when we do not control for analysts’ company-specific aptitude in forecasting. It is unlikely that this result is caused by measurement errors because it is robust when we use a sub-sample where we can accurately measure experience.

Accounting research frequently uses R2, for example, to measure value relevance. We show analytically that scale effects present in levels regressions increase R2, and this effect increases in the scale factor's coefficient of variation. Thus, between-sample comparisons of R2 are invalid, unless one controls for differences in the scale factor's coefficient of variation. Applying our analysis to prior research, we show that the documented increase in value relevance of accounting is attributable to increases in the coefficient of variation of the scale factor. Controlling for this effect, there has been a decline in value relevance, as measured by R2.