Robust Measures of Earnings Surprises

Chin‐han Chiang, Wei Dai, Jianqing Fan, Harrison Hong and Jun Tu

Journal of Finance, 2019, vol. 74, issue 2, 943-983

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 to CE is a nonlinear filter of individual errors that adjusts for bias. We show that CE is 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. FOM performs particularly well against CE in predicting the returns of U.S. stocks, where bias is potentially large.

Date: 2019
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