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How to measure earnings surprises: Based on revised market reaction

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  • Qin Pan
  • Kai Huang

Abstract

We investigate the robustness of earnings surprise measures in the context of a revised market reaction. While existing literature suggests that financial anomalies may distort cumulative abnormal returns (CAR) during annual announcements, our research proves that a revised market reaction offers a more accurate reflection of investor reactions to earnings correction. Specifically, we introduce an innovative adjustment to CAR using stock price jumps, and prove that the fraction of misses on the same side (FOM) provides a superior measure of earnings surprises. Furthermore, we find that investor trading patterns align with FOM, and the post-earnings announcement drift (PEAD) strategy based on FOM outperforms that based on analysts’ forecast error.

Suggested Citation

  • Qin Pan & Kai Huang, 2023. "How to measure earnings surprises: Based on revised market reaction," PLOS ONE, Public Library of Science, vol. 18(12), pages 1-24, December.
    • Handle: RePEc:plo:pone00:0296394
    DOI: 10.1371/journal.pone.0296394
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    References listed on IDEAS

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    1. Yu, Deshui & Huang, Difang & Chen, Li, 2023. "Stock return predictability and cyclical movements in valuation ratios," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 36-53.
    2. Bernard, Victor L. & Thomas, Jacob K., 1990. "Evidence that stock prices do not fully reflect the implications of current earnings for future earnings," Journal of Accounting and Economics, Elsevier, vol. 13(4), pages 305-340, December.
    3. Kothari, S. P., 2001. "Capital markets research in accounting," Journal of Accounting and Economics, Elsevier, vol. 31(1-3), pages 105-231, September.
    4. Yu, Deshui & Huang, Difang, 2023. "Cross-sectional uncertainty and expected stock returns," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 321-340.
    5. Gu, Zhaoyang & Wu, Joanna Shuang, 2003. "Earnings skewness and analyst forecast bias," Journal of Accounting and Economics, Elsevier, vol. 35(1), pages 5-29, April.
    6. Joseph Engelberg & R. David Mclean & Jeffrey Pontiff, 2018. "Anomalies and News," Journal of Finance, American Finance Association, vol. 73(5), pages 1971-2001, October.
    7. Qi Chen & Wei Jiang, 2006. "Analysts' Weighting of Private and Public Information," The Review of Financial Studies, Society for Financial Studies, vol. 19(1), pages 319-355.
    8. Brown, Lawrence D. & Hagerman, Robert L. & Griffin, Paul A. & Zmijewski, Mark E., 1987. "Security analyst superiority relative to univariate time-series models in forecasting quarterly earnings," Journal of Accounting and Economics, Elsevier, vol. 9(1), pages 61-87, April.
    9. Kevin Koh & Dawn A. Matsumoto & Shivaram Rajgopal, 2008. "Meeting or Beating Analyst Expectations in the Post†Scandals World: Changes in Stock Market Rewards and Managerial Actions," Contemporary Accounting Research, John Wiley & Sons, vol. 25(4), pages 1067-1098, December.
    10. repec:bla:jfinan:v:59:y:2004:i:2:p:755-793 is not listed on IDEAS
    11. Ball, R & Brown, P, 1968. "Empirical Evaluation Of Accounting Income Numbers," Journal of Accounting Research, Wiley Blackwell, vol. 6(2), pages 159-178.
    12. Anna Scherbina, 2008. "Suppressed Negative Information and Future Underperformance," Review of Finance, European Finance Association, vol. 12(3), pages 533-565.
    13. Yu, Deshui & Huang, Difang & Chen, Li & Li, Luyang, 2023. "Forecasting dividend growth: The role of adjusted earnings yield," Economic Modelling, Elsevier, vol. 120(C).
    Full references (including those not matched with items on IDEAS)

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