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Event studies of market efficiency measure an earnings surprise with the consensus error (CE), defined as earnings minus the average of professional forecasts. 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 for this ideal measure. The fraction of misses on the same side FOM, by discarding the magnitude of misses, offers a far better approximation. FOM performs particularly well against CE in predicting the returns of US stocks, where bias is potentially large, than that of international stocks.