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The Inefficiency of the Mean Analyst Forecast as a Summary Forecast of Earnings

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  • Oliver Kim
  • Steve C. Lim
  • Kenneth W. Shaw

Abstract

We show analytically that mean analyst forecasts inefficiently aggregate information by assigning too much weight to analysts’ common information relative to their private information when used as a summary forecast measure of forthcoming earnings. A more precise summary forecast of earnings than the current mean forecast is the current mean forecast plus a positive multiple of the change in the mean forecast.

Suggested Citation

  • Oliver Kim & Steve C. Lim & Kenneth W. Shaw, 2001. "The Inefficiency of the Mean Analyst Forecast as a Summary Forecast of Earnings," Journal of Accounting Research, Wiley Blackwell, vol. 39(2), pages 329-335, September.
    • Handle: RePEc:bla:joares:v:39:y:2001:i:2:p:329-335
    DOI: 10.1111/1475-679X.00015
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    Cited by:

    1. Trabelsi, Emna, 2016. "Central bank transparency and the consensus forecast: What does The Economist poll of forecasters tell us?," Research in International Business and Finance, Elsevier, vol. 38(C), pages 338-359.
    2. Paul Hubert, 2014. "Disentangling qualitative and quantitative central bank influence," Working Papers hal-01098464, HAL.
    3. Phillip E. Pfeifer & Yael Grushka-Cockayne & Kenneth C. Lichtendahl, 2014. "The Promise of Prediction Contests," The American Statistician, Taylor & Francis Journals, vol. 68(4), pages 264-270, November.
    4. David A. Mascio & Frank J. Fabozzi & J. Kenton Zumwalt, 2021. "Market timing using combined forecasts and machine learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 1-16, January.
    5. Paul Hubert, 2010. "Monetary policy, imperfect information and the expectations channel [Politique monétaire,information imparfaite et canal des anticipations]," SciencePo Working papers Main tel-04095385, HAL.
    6. Kenneth Wallis, 2011. "Combining forecasts - forty years later," Applied Financial Economics, Taylor & Francis Journals, vol. 21(1-2), pages 33-41.
    7. Satopää, Ville A., 2021. "Improving the wisdom of crowds with analysis of variance of predictions of related outcomes," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1728-1747.
    8. Asa B. Palley & Jack B. Soll, 2019. "Extracting the Wisdom of Crowds When Information Is Shared," Management Science, INFORMS, vol. 67(5), pages 2291-2309, May.
    9. Yanwei Jia & Jussi Keppo & Ville Satopää, 2023. "Herding in Probabilistic Forecasts," Management Science, INFORMS, vol. 69(5), pages 2713-2732, May.
    10. Oesinghaus, Andreas, 2024. "Analysts’ extrapolative expectations in the cross-section," Journal of Economics and Business, Elsevier, vol. 130(C).
    11. Reslow, André, 2019. "Inefficient Use of Competitors'Forecasts?," Working Paper Series 380, Sveriges Riksbank (Central Bank of Sweden).
    12. repec:hal:wpaper:hal-01121434 is not listed on IDEAS
    13. Beardsley, Erik L. & Robinson, John R. & Wong, Paul A., 2021. "What's my target? Individual analyst forecasts and last-chance earnings management," Journal of Accounting and Economics, Elsevier, vol. 72(1).
    14. Kenneth C. Lichtendahl & Yael Grushka-Cockayne & Phillip E. Pfeifer, 2013. "The Wisdom of Competitive Crowds," Operations Research, INFORMS, vol. 61(6), pages 1383-1398, December.
    15. Keppo, Jussi & Satopää, Ville A., 2024. "Bayesian herd detection for dynamic data," International Journal of Forecasting, Elsevier, vol. 40(1), pages 285-301.
    16. Lin, Hai & Tao, Xinyuan & Wu, Chunchi, 2022. "Forecasting earnings with combination of analyst forecasts," Journal of Empirical Finance, Elsevier, vol. 68(C), pages 133-159.
    17. Marinovic, Iván & Ottaviani, Marco & Sorensen, Peter, 2013. "Forecasters’ Objectives and Strategies," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 690-720, Elsevier.

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