True Overconfidence in Interval Estimates: Evidence Based on a New Measure of Miscalibration
Glaser Markus, Langer Thomas, Weber Martin
Overconfidence is often regarded as one of the most prevalent judgment biases. Several studies show that overconfidence can lead to suboptimal decisions of investors, managers, or politicians. Recent research, however, questions whether overconfidence should be regarded as a bias and show that standard "overconfidence" findings can easily be explained by different degrees of knowledge of agents plus a random error in predictions. We contribute to the current literature and ongoing research in the following ways. We test the main assumption of behavioral economics and behavioral finance models: stable individual differences in the degree of overconfidence across people. We do this by extensively analyzing interval estimates for knowledge questions, real financial time series, and for artificially generated charts. Furthermore, as a methodological contribution, we suggest a new method to measure overconfidence in interval estimates which is based on the implied probability mass behind a stated prediction interval. Our results can be summarized as follows. We document overconfidence patterns which are difficult to reconcile with rationality of agents and which cannot be explained by knowledge plus random error. Furthermore, we show that there exist stable individual differences in the degree of overconfidence in interval estimates. We do this in a „field experiment", for different levels of expertise of subjects (students on the one hand and professional traders and investment bankers on the other hand), over time, and for tasks which are ecologically valid.
Overconfidence; Judgment Biases; Individual Differences; Expert Judgment; Better Than Average Effect; Interval Estimates