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Overconfidence and debiasing in the financial industry

Author

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  • Markku Kaustia
  • Milla Perttula

Abstract

Purpose - The purpose of this paper is to measure overconfidence amongst finance professionals in domain relevant knowledge, and test for the impact of different debiasing methods. Design/methodology/approach - The approach used was survey field experiments with varying debiasing attempts. Findings - The subjects were overconfident in terms of probability calibration, better‐than‐average beliefs, and unfounded confidence. Debiasing attempts yielded mixed results. Explicit written warnings reduced better‐than‐average‐type of overconfidence. There was a further strong effect from attending lectures on investor psychology covering relevant examples. In contrast, there was only limited success in reducing miscalibration in probability assessments. Research limitations/implications - Different types of overconfidence are distinct and respond differentially to debiasing. Future research on debiasing professional judgment should concentrate on testing in‐depth/personally engaging methods. Practical implications - It is important for bankers to acknowledge the dangers of overconfidence. Correct confidence interval calibration is needed in order to have a sense of the risks involved in different asset allocation policies and trading strategies. Bankers should also be able to help their clients avoid overconfidence. Social implications - Debiasing overconfidence in the finance industry likely carries public benefits. The results imply that this task is not easy, but not impossible either. The authors think further investment in this endeavor is justified. Originality/value - Documenting an important judgment bias among finance professionals and estimating the effects of debiasing.

Suggested Citation

  • Markku Kaustia & Milla Perttula, 2012. "Overconfidence and debiasing in the financial industry," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 4(1), pages 46-62, July.
    • Handle: RePEc:eme:rbfpps:v:4:y:2012:i:1:p:46-62
    DOI: 10.1108/19405971211261100
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    Citations

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    Cited by:

    1. Lorenz Graf-Vlachy, 2019. "Like student like manager? Using student subjects in managerial debiasing research," Review of Managerial Science, Springer, vol. 13(2), pages 347-376, April.
    2. Ritika & Nawal Kishor, 2020. "Development and validation of behavioral biases scale: a SEM approach," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 14(2), pages 237-259, November.
    3. Huber, Christoph & Huber, Jürgen & Hueber, Laura, 2019. "The effect of experts’ and laypeople’s forecasts on others’ stock market forecasts," Journal of Banking & Finance, Elsevier, vol. 109(C).
    4. Amitai Etzioni, 2014. "Humble Decision-Making Theory," Public Management Review, Taylor & Francis Journals, vol. 16(5), pages 611-619, June.
    5. Hermansson, Cecilia & Song, Han-Suck, 2016. "Financial advisory services meetings and their impact on saving behavior – A difference-in-difference analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 30(C), pages 131-139.
    6. Hueber, Laura & Schwaiger, Rene, 2022. "Debiasing through experience sampling: The case of myopic loss aversion," Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 87-138.
    7. Rodgers, Waymond & Hudson, Robert & Economou, Fotini, 2023. "Modelling credit and investment decisions based on AI algorithmic behavioral pathways," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    8. Pruijssers, Jorien Louise & Singer, Gallia & Singer, Zvi & Tsang, Desmond, 2023. "Social influence pressures and the risk preferences of aspiring financial market professionals," Journal of Accounting Education, Elsevier, vol. 62(C).
    9. Johannes Binswanger & Anja Garbely & Manuel Oechslin, 2023. "Investor beliefs about transformative innovations under uncertainty," Economica, London School of Economics and Political Science, vol. 90(360), pages 1119-1144, October.
    10. Syed Aliya Zahera & Rohit Bansal, 2018. "Do investors exhibit behavioral biases in investment decision making? A systematic review," Qualitative Research in Financial Markets, Emerald Group Publishing Limited, vol. 10(2), pages 210-251, May.
    11. Laura Hueber & Rene Schwaiger, 2021. "Debiasing Through Experience Sampling: The Case of Myopic Loss Aversion," Working Papers 2021-01, Faculty of Economics and Statistics, Universität Innsbruck.

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