Robust Measures of Earnings Surprises
研究发现，用分析师预测误差的共识误差度量盈利意外效果不佳，而忽略误差大小、只关注同侧错误比例的FOM指标能更稳健地预测美国股票回报，尤其适用于预测偏差较大的场景。
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.