Resumo:
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.
Chiang, Chin-Han and Dai, Wei and Fan, Jianqing and Hong, Harrison G. and Tu, Jun, Robust Measures of Earnings Surprises (May 3, 2016). Journal of Finance, Forthcoming.
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