Robust Measures of Earnings Surprises

Abstract

Event studies of market efficiency measure earnings surprises using the consensus error (CE), given as actual earnings minus the average professional forecast. If a subset of forecasts can be biased, the ideal but difficult to estimate parameter-dependent alternative toCEis a nonlinear filter of individual errors that adjusts for bias. We show thatCEis a poor parameter-free approximation of this ideal measure. The fraction of misses on the same side (FOM), which discards the magnitude of misses, offers a far better approximation.FOMperforms particularly well againstCEin predicting the returns of U.S. stocks, where bias is potentially large.