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Overconfidence and trading volume


Theoretical models predict that overconfident investors will trade more than rational investors. We directly test this hypothesis by correlating individual overconfidence scores with several measures of trading volume of individual investors. Approximately 3,000 online broker investors were asked to answer an internet questionnaire which was designed to measure various facets of overconfidence (miscalibration, volatility estimates, better than average effect). The measures of trading volume were calculated by the trades of 215 individual investors who answered the questionnaire. We find that investors who think that they are above average in terms of investment skills or past performance (but who did not have above average performance in the past) trade more. Measures of miscalibration are, contrary to theory, unrelated to measures of trading volume. This result is striking as theoretical models that incorporate overconfident investors mainly motivate this assumption by the calibration literature and model overconfidence as underestimation of the variance of signals. In connection with other recent findings, we conclude that the usual way of motivating and modeling overconfidence which is mainly based on the calibration literature has to be treated with caution. Moreover, our way of empirically evaluating behavioral finance models—the correlation of economic and psychological variables and the combination of psychometric measures of judgment biases (such as overconfidence scores) and field data'seems to be a promising way to better understand which psychological phenomena actually drive economic behavior.

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This paper was the keynote address at the Barcelona EGRIE meeting.

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Glaser, M., Weber, M. Overconfidence and trading volume. Geneva Risk Insur Rev 32, 1–36 (2007).

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  • Overconfidence
  • Differences of opinion
  • Trading volume
  • Individual investors
  • Investor behavior
  • Correlation of economic and psychological variables
  • Combination of psychometric measures of judgment biases and field data

JEL Classifications

  • D8
  • G1