Learning to be overprecise - Christoph Merkle; Philipp Schreiber (Journal of Business Economics, 2024)
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.
Keywords: D03; D83; D84; G17; G41; 650; Overconfidence; Overprecision; Miscalibration; Replication; Bayesian learning; Financial forecasting