Abstract
Post-earnings-announcement drift is the well-documented ability of earnings surprises to predict future stock returns. Despite nearly four decades of research, little has been written about the importance of how earnings surprise is actually measured. We compare the magnitude of the drift when historical time-series data are used to estimate earnings surprise with the magnitude when analyst forecasts are used. We show that the drift is significantly larger when analyst forecasts are used. Furthermore, we show that using the two models together does a better job of predicting future stock returns than using either model alone.
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