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How (Over) Confident Are Financial Analysts?

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  • Ning Du
  • David V. Budescu

Abstract

Extensive research has been devoted to the quality of analysts' earnings forecasts. The common finding is that analysts' forecasts are not very accurate. Prior studies have tended to focus on the mean of forecasts and measure accuracy using various summaries of forecast errors. The present study sheds new light on the accuracy of analysts' forecasts, by measuring how well calibrated these forecasts are. The authors follow the tradition of calibration studies in psychological literature and measure the degree of calibration by the hit rate. They analyze a year's worth of data from the Institutional Brokers Estimate System database, which includes over 200,000 annual earnings forecasts made by over 6,000 analysts for over 5,000 companies. By using different ways to convert analysts' point estimates of earnings into a range of values, the authors establish the bounds that are necessary to determine the hit rates, and examine to what extent the actual earnings announced by the companies are bracketed by these intervals. These hit rates provide a more complete picture of the accuracy of the forecasts.

Suggested Citation

  • Ning Du & David V. Budescu, 2018. "How (Over) Confident Are Financial Analysts?," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 19(3), pages 308-318, July.
    • Handle: RePEc:taf:hbhfxx:v:19:y:2018:i:3:p:308-318
    DOI: 10.1080/15427560.2018.1405004
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    Cited by:

    1. Etienne Theising & Dominik Wied & Daniel Ziggel, 2023. "Reference class selection in similarity‐based forecasting of corporate sales growth," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1069-1085, August.
    2. Emmanuel Mamatzakis & Anna Bagntasarian, 2021. "The nexus between CEO incentives and analysts' earnings forecasts," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 6205-6248, October.
    3. Königstorfer, Florian & Thalmann, Stefan, 2020. "Applications of Artificial Intelligence in commercial banks – A research agenda for behavioral finance," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    4. Zhang, Mingshan, 2022. "Warren Buffett Anomaly," Finance Research Letters, Elsevier, vol. 46(PB).

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