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Robust Measures of Earnings Surprises
Author(s) -
CHIANG CHINHAN,
DAI WEI,
FAN JIANQING,
HONG HARRISON,
TU JUN
Publication year - 2019
Publication title -
the journal of finance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 18.151
H-Index - 299
eISSN - 1540-6261
pISSN - 0022-1082
DOI - 10.1111/jofi.12746
Subject(s) - earnings , econometrics , measure (data warehouse) , ideal (ethics) , economics , event (particle physics) , filter (signal processing) , mathematics , computer science , physics , accounting , data mining , philosophy , epistemology , quantum mechanics , computer vision
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.