Discussion of Information Uncertainty and Post‐Earnings‐Announcement‐Drift

The post-earnings-announcement drift has been the longest-standing anomaly in the finance and accounting literature. Several decades after this anomaly was first identified by Ball and Brown (1968), the strategy remains profitable. It has also withstood a variety of methodological checks. Although the anomaly violates the semi-strong form of market efficiency, Francis, LaFond, Olsson and Schipper (2007) (FLOS hereafter) argue that this need not imply investors' irrationality, as stock-return predictability rationally arises under a learning model. FLOS test the validity of rational learning models as an explanation for the post-earnings-announcement-drift by examining the empirical implications of this hypothesis. FLOS draw three implications from the learning hypothesis. Their first hypothesis is that initial reaction to earnings surprise will be more muted, the larger is the information uncertainty about underlying earnings. This is because Bayesian investors place less weight on noisier (i.e., more uncertain) earnings information and more weight on their priors, making them under-react to new information. The testable prediction from this hypothesis is that the greater the information uncertainty, the smaller will be the magnitude of initial market reaction to earnings surprises. The second hypothesis is that, if information uncertainty gives rise to the post-earnings- announcement-drift, then it must be greater in firms with higher post-earnings- announcement-drift. As firms with extreme earnings surprises are the firms with most drift, they should also be firms with the most information uncertainty. The last hypothesis is that, controlling for earnings surprises, abnormal returns associated with the post-earnings-announcement-drift are larger for firms with higher information uncertainty. This hypothesis essentially states the consequences of hypothesis 1. That is, if initial under-reaction to earnings surprises is larger for firms with greater information uncertainty, as stated in hypothesis 1, then the subsequent corrections, which cause the post-earnings-announcement-drift, should also be larger for firms with greater information uncertainty. In addition to the above three implications of the learning hypothesis, FLOS conduct two additional analyses. First, they study the relation between their findings and those of