Learning to be Overprecise

Published in Working Paper, 2023

Recommended citation: Merkle, Christoph and Philipp Schreiber. (2019). "Learning to be Overprecise." Working Paper.

We replicate and extend two studies on the dynamics of overconfidence among financial professionals. Using twenty 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.


JEL codes: D03, D83, D84, G17, G41.

Keywords: Overconfidence, Overprecision, Miscalibration, Replication, Bayesian Learning, Financial Forecasting.