Overconfidence, Investor Sentiment, and Evolution

Wang, F.Albert

doi:10.1006/jfin.2001.0311

Cited by 125 publications

(65 citation statements)

References 30 publications

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“…15 The third dataset consists of the answers to an online questionnaire that was designed to elicit several measures of overconfidence 13 There are other overconfidence models that address questions like the dynamics of overconfidence, the survival of overconfident investors in markets, and the cross-section of expected returns. Examples are Daniel et al [1998Daniel et al [ , 2001, Hirshleifer and Luo [2001], Gervais and Odean [2001], and Wang [2001]. 14 See Glaser [2003Glaser [ , 2005 for descriptive statistics and further details.…”

Section: Datasetsmentioning

confidence: 99%

Overconfidence and trading volume

Glaser

Weber

2007

Geneva Risk Insur Rev

559

267

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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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Section: Datasetsmentioning

confidence: 99%

Overconfidence and trading volume

Glaser

Weber

2007

Geneva Risk Insur Rev

559

267

View full text Add to dashboard Cite

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“…A study conducted on the Warsaw Stock Exchange -analysts were asked to forecast the WIG index for the six following months -confirmed that analysts are overconfident as to their competence, because only one third of forecasts were accurate (Zaleśkiewicz, 2011). Several papers have developed theoretical models based on the observation that investors are overconfident (Benos, 1998;Caballe, Sakovics, 2003;Daniel, Hirshleifer, Subrahmanyam, 1998;Gervais, Odean, 2001;Hong, Scheinkman, Xiong, 2006;Kyle, Wang, 1997;Odean, 1998;Peng, Xiong, 2006;Scheinkman, Xiong, 2003;Wang, 2001). Generally, these models assume investors suffer from the miscalibration type of overconfidence.…”

Section: Cognitive Biasesmentioning

confidence: 99%

Heuristic. Psychological Aspects of Decision-Making on Capital Market

Mikołajek-Gocejna

2017

Zeszyty Naukowe Uniwersytetu Szczecińskiego Finanse Rynki Finansowe Ubezpieczenia

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Heuristics are efficient cognitive processes that ignore part of information. In classical view, heuristic decisions save effort, but imply greater errors then "rational" decisions, defined by logic and statistical model. It clearly emerges that in real life people do not always make rational decisions based on established preferences and complete information. In many ways their behavior thus contradicts the homo oeconomicus model. Much of the behaviour observed is caused through people trying to cope with the complexity of the world around them by approximating, because collating and evaluating all the factors of relevance to a decision overtaxes their mental processing capacity. These psychologically driven inadequacies also occur with investment decisions. Heuristics are simply a more effective way of evaluating choices in the rich and changing decision making environment investors must face. The aim of this paper is to review the impact of heuristics bias decision-making on capital market.

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“…In …nance, papers include Barber and Odean (2001), Bernardo and Welch (2001), Chuang and Lee (2006), Daniel, Hirshleifer and Subrahmanyam (2001), Kyle and Wang (1997), Malmendier and Tate (2005), Peng and Xiong (2006), and Wang (2001). See Benoît and Dubra (2008) for a discussion of some of the literature.…”

Section: Introductionmentioning

confidence: 99%