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Do Investors Overrely on Old Elements of the Earnings Time Series?

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  • Robert J. Bloomfield
  • Robert Libby
  • Mark W. Nelson

Abstract

This paper reports an experiment demonstrating that MBA students overrely on old earnings performance when predicting future earnings performance in a laboratory setting. In the experiment, MBA students relied too heavily on old annual ROE information to predict future annual ROE. The experiment shows how a common cognitive error (overreliance on unreliable information) interacts with the structure of the earnings time series to create particular patterns of prediction errors. The results also suggest directions for research on two well†known anomalies, long†run overreactions (De Bondt and Thaler 1985, 1987) and post†earnings†announcement drift (Bernard and Thomas 1990).

Suggested Citation

  • Robert J. Bloomfield & Robert Libby & Mark W. Nelson, 2003. "Do Investors Overrely on Old Elements of the Earnings Time Series?," Contemporary Accounting Research, John Wiley & Sons, vol. 20(1), pages 1-31, March.
    • Handle: RePEc:wly:coacre:v:20:y:2003:i:1:p:1-31
    DOI: 10.1506/N8T8-9QR7-YUCX-91X2
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    Cited by:

    1. Stefan Schiller, 2017. "The Quest for Rationality: Chief Financial Officers’ and Accounting Master’s Students’ Perception of Economic Rationality," SAGE Open, , vol. 7(2), pages 21582440177, April.
    2. Sami Keskek & James N. Myers & Linda A. Myers, 2020. "Investors' Misweighting of Firm‐Level Information and the Market's Expectations of Earnings," Contemporary Accounting Research, John Wiley & Sons, vol. 37(3), pages 1828-1853, September.
    3. Abdul Hamid Habbe, 2017. "Estimation Error of Earnings Information: A Test of Representativeness and Anchoring-adjustment Heuristic," International Journal of Economics and Financial Issues, Econjournals, vol. 7(1), pages 224-233.
    4. Frieder, Laura, 2008. "Investor and price response to patterns in earnings surprises," Journal of Financial Markets, Elsevier, vol. 11(3), pages 259-283, August.
    5. Rimona Palas & Amos Baranes, 2017. "The Prediction of Earnings Movement Using Mandated XBRL data ? Industry Analysis," Proceedings of Economics and Finance Conferences 4507381, International Institute of Social and Economic Sciences.
    6. Miranda-Lopez, Jose E. & Nichols, Linda M., 2012. "The use of earnings and cash flows in investment decisions in the U.S. and Mexico: Experimental evidence," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 21(2), pages 198-208.
    7. Fink, Josef & Palan, Stefan & Theissen, Erik, 2020. "Earnings autocorrelation and the post-earnings-announcement drift: Experimental evidence," CFR Working Papers 20-10, University of Cologne, Centre for Financial Research (CFR).
    8. repec:grz:wpsses:2020-04 is not listed on IDEAS
    9. Sri Sundari & Mediaty & Abdul Hamid Habbe & Harryanto, 2018. "Heuristic of Representativeness and Anchoring-Adjustment in Budgeting," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, vol. 8(4), pages 52-60, October.
    10. W. Brooke Elliott & Jessen L. Hobson & Brian J. White, 2015. "Earnings Metrics, Information Processing, and Price Efficiency in Laboratory Markets," Journal of Accounting Research, Wiley Blackwell, vol. 53(3), pages 555-592, June.
    11. Fink, Josef, 2021. "A review of the Post-Earnings-Announcement Drift," Journal of Behavioral and Experimental Finance, Elsevier, vol. 29(C).
    12. Rimona Palas & Amos Baranes, 2019. "Making investment decisions using XBRL filing data," Accounting Research Journal, Emerald Group Publishing Limited, vol. 32(4), pages 587-609, November.
    13. repec:grz:wpsses:2020-03 is not listed on IDEAS
    14. Martin, Rachel, 2019. "Examination and implications of experimental research on investor perceptions," Journal of Accounting Literature, Elsevier, vol. 43(C), pages 145-169.

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