Robust Measures of Earnings Surprises

Robust Measures of Earnings Surprises

  • CHIN‐HAN CHIANG
  • WEI DAI
  • JIANQING FAN
  • HARRISON HONG
  • JUN TU

Article first published online: 19th December 2018 DOI: 10.1111/jofi.12746

Abstract


Event studies of market efficiency measure earnings surprises using the consensus error (), 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 is a nonlinear filter of individual errors that adjusts for bias. We show that is a poor parameter‐free approximation of this ideal measure. The fraction of misses on the same side (), which discards the magnitude of misses, offers a far better approximation. performs particularly well against in predicting the returns of U.S. stocks, where bias is potentially large.

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