Robust Measures of Earnings Surprises

Chin Han Chiang, Wei Dai, Jianqing Fan, Harrison Hong, Jun Tu

Research output: Contribution to journalArticle

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)943-983
Number of pages41
JournalJournal of Finance
Volume74
Issue number2
DOIs
StatePublished - Apr 2019

All Science Journal Classification (ASJC) codes

  • Accounting
  • Finance
  • Economics and Econometrics

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    Chiang, C. H., Dai, W., Fan, J., Hong, H., & Tu, J. (2019). Robust Measures of Earnings Surprises. Journal of Finance, 74(2), 943-983. https://doi.org/10.1111/jofi.12746