Forecaster Overconfidence and Market Survey Performance

Richard Deaves, Jin Lei and Michael Schröder

Journal of Behavioral Finance, 2019, vol. 20, issue 2, 173-194

Abstract: The authors document using the ZEW panel of German stock market forecasters that weak forecasters tend to be overconfident in the sense that they provide extreme forecasts and their confidence intervals are less likely to contain eventual realizations. They further show that moderate filters based on forecast accuracy of past performance over short rolling windows, which delicately balance ignoring relevant information and noise reduction, are somewhat successful in improving predictability. While poor performance can be due to various factors, a filter based on forecaster overconfidence, a prior tendency to have high forecast standard deviations, also improves the performance of market survey forecasts.

Date: 2019
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DOI: 10.1080/15427560.2018.1505727

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