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- W2126256173 abstract "Overconfidence is often regarded as one of the most prevalent judgment biases. Several studies show that can lead to suboptimal decisions of investors, managers, or politicians. Recent research, however, questions whether should be regarded as a bias and shows 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 by extensively analyzing interval estimates for knowledge questions, real financial time series, and for artificially generated charts. We thereby suggest a new method to measure in interval estimates which is based on the implied probability mass behind a stated prediction interval. We document patterns which are difficult to reconcile with rationality of agents and which cannot be explained by differences in knowledge as differences in knowledge do not exist in our task. Furthermore, we show that measures are reliable in the sense that there exist stable individual differences in the degree of in interval estimates, thereby testing an important assumption of behavioral economics and behavioral finance models: stable individual differences in the degree of across people. 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, by using different miscalibration metrics, and for tasks which avoid common weaknesses like a non-representative selection of trick questions." @default.
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- W2126256173 date "2012-01-01" @default.
- W2126256173 modified "2023-09-26" @default.
- W2126256173 title "True Overconfidence in Interval Estimates: Evidence Based on a New Measure of Miscalibration" @default.
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- W2126256173 doi "https://doi.org/10.2139/ssrn.712583" @default.
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