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Learning to be overprecise

Author

Listed:
  • Christoph Merkle

    (Aarhus University BSS
    Danish Finance Institute (DFI))

  • Philipp Schreiber

    (Esslingen University)

Abstract

We replicate and extend two studies on the dynamics of overconfidence among financial professionals. Using 20 years of data from the ZEW Financial Market Survey with over 40,000 individual forecasts of confidence intervals, we document that participants are overprecise during the entire time period with no evidence of learning on the aggregate. We confirm that professionals update in a Bayesian manner after hits and misses by contracting or expanding their confidence intervals, respectively. However, this updating is insufficient to reach proper calibration. We cannot confirm other predictions of a Bayesian model. An explanation based on self-attribution bias fits the data better.

Suggested Citation

  • Christoph Merkle & Philipp Schreiber, 2025. "Learning to be overprecise," Journal of Business Economics, Springer, vol. 95(2), pages 467-497, April.
    • Handle: RePEc:spr:jbecon:v:95:y:2025:i:2:d:10.1007_s11573-024-01203-w
    DOI: 10.1007/s11573-024-01203-w
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    More about this item

    Keywords

    Overconfidence; Overprecision; Miscalibration; Replication; Bayesian learning; Financial forecasting;
    All these keywords.

    JEL classification:

    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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