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.