Overconfidence and Trading Volume

Abstract

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 (number of trades, turnover). Approximately 3000 online broker investors were asked to answer an internet questionnaire which was designed to measure various facets of overconfidence (miscalibration, the better than average effect, illusion of control, unrealistic optimism). 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 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. The results hold even when we control for several other determinants of trading volume in a cross-sectional regression analysis. 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. We argue that our findings present a psychological foundation for the 'differences of opinion' explanation of high levels of trading volume. In addition, our way of empirically evaluating behavioural 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 drive economic behaviour.

Keywords: Overconfidence, differences of opinion, trading volume, individual investors, investor behaviour, correlation of economic and psychological variables, combination of psychometric measures of judgement biases and field data

JEL Classification: D80, G10