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Robust Measures of Earnings Surprises

Author

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

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.

Suggested Citation

  • Chin‐Han Chiang & Wei Dai & Jianqing Fan & Harrison Hong & Jun Tu, 2019. "Robust Measures of Earnings Surprises," Journal of Finance, American Finance Association, vol. 74(2), pages 943-983, April.
    • Handle: RePEc:bla:jfinan:v:74:y:2019:i:2:p:943-983
    DOI: 10.1111/jofi.12746
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    Cited by:

    1. Koppenberg, Maximilian & Hirsch, Stefan & Finger, Robert, 2023. "Effects of the debate on glyphosate's carcinogenic risk on pesticide producers' share prices," Ecological Economics, Elsevier, vol. 212(C).
    2. Ni, Xiaoran & Peng, Qiyuan & Yin, Sirui & Zhang, Ting, 2020. "Attention! Distracted institutional investors and stock price crash," Journal of Corporate Finance, Elsevier, vol. 64(C).
    3. Cahill, Daniel & Ho, Choy Yeing (Chloe) & Yang, Joey W., 2022. "The COVID-19 pandemic: How important is face-to-face interaction for information dissemination?," Global Finance Journal, Elsevier, vol. 54(C).
    4. John Ammer & John Rogers & Gang Wang & Yang Yu, 2020. "Monetary Policy Expectations, Fund Managers, and Fund Returns: Evidence from China," International Finance Discussion Papers 1285, Board of Governors of the Federal Reserve System (U.S.).
    5. Andrew C. Call & Max Hewitt & Jessica Watkins & Teri Lombardi Yohn, 2021. "Analysts’ annual earnings forecasts and changes to the I/B/E/S database," Review of Accounting Studies, Springer, vol. 26(1), pages 1-36, March.
    6. Chandan Sharma & Archana Singh & Rajan Yadav, 2023. "Impact of Competition in Credit Rating Industry: Evidence From India," SAGE Open, , vol. 13(1), pages 21582440221, January.
    7. Han, Chulwoo & Kang, Jangkoo & Kim, Sun Yung, 2022. "Betting against analyst target price," Journal of Financial Markets, Elsevier, vol. 59(PB).
    8. Zdeněk Zmeškal & Dana Dluhošová & Karolina Lisztwanová & Antonín Pončík & Iveta Ratmanová, 2023. "Distribution Prediction of Decomposed Relative EVA Measure with Levy-Driven Mean-Reversion Processes: The Case of an Automotive Sector of a Small Open Economy," Forecasting, MDPI, vol. 5(2), pages 1-19, May.
    9. John Ammer & John Rogers & Gang Wang & Yang Yu, 2023. "Chinese Asset Managers’ Monetary Policy Forecasts and Fund Performance," Management Science, INFORMS, vol. 69(1), pages 598-616, January.
    10. Merkley, Kenneth & Michaely, Roni & Pacelli, Joseph, 2020. "Cultural diversity on Wall Street: Evidence from consensus earnings forecasts," Journal of Accounting and Economics, Elsevier, vol. 70(1).

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