Exploiting Earnings Persistence to Better Measure Market Expectations in Detecting Post-Earnings Announcement Drift

Abstract

ABSTRACT We construct an alternate proxy for earnings expectations that does not depend on analyst following and exploits differential earnings persistence across earnings levels. We examine the usefulness of our proxy in the context of post-earnings announcement drift (PEAD) where the random walk model is still widely used in the literature. We show that a refined trading strategy using our alternative proxy results in statistically significant and economically large increases in PEAD relative to the random walk model. Furthermore, using a subsample where analysts' forecasts are available, we show that the PEAD associated with our alternate proxy is incremental to the PEAD associated with analyst forecast errors. Our earnings expectation proxy can be used in other settings where extreme earnings or earnings surprise portfolios are the focus of the analysis and researchers want to include firms without analyst following.