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How Does Overconfidence Affect Asset Pricing, Volatility, and Volume?

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Advances in Computational Social Science

Part of the book series: Agent-Based Social Systems ((ABSS,volume 11))

Abstract

Overconfidence is one of the most important characteristics of traders. In the past decade, theoretical approaches have paid much attention to this topic and obtained significant results. However, they heavily rely on specific assumptions regarding the characteristics of traders as well as the market environments. Most importantly, they only consider the market with a few types of traders. None of them is built upon a truly heterogeneous-agent framework . This paper develops an agent-based financial market . Each trader adopts a genetic programming learning method to form his expectations regarding the future. The overconfidence level of each trader is modeled as the degree of underestimation of the conditional variance. Based on this framework, we examine how traders’ overconfidence affects the market by analyzing the results regarding market volatility, price distortion, and trading volume.

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Notes

  1. 1.

    The benefits of agent-based modeling in economics and finance are described in [23, 36] in depth.

  2. 2.

    Simon [33] points out that the definition of rationality should consider prediction and the formation of expectations under uncertainty if the assumptions of perfect foresight are discarded.

  3. 3.

    Research such as that of [10, 39–42] adopts this approach.

  4. 4.

    For example, K = 1,12,52,250 represent the number of trading periods measured by the units of a year, month, week, and day, respectively.

  5. 5.

    With this kind of functional form, the traders are still able to take a chance on the martingale hypothesis when f i, t  = 0. The form employed in [8] shares the same merit. Although we agree that it is reasonable to also allow the traders to separately form their expectations about prices and dividends, a multi-objective GP design that aims to accommodate this bipartite learning may be, at the current stage, too complicated to establish an intuitive causal link between price dynamics and individual expectations. Even the models which assume more classic frameworks, like those proposed by Brock and Hommes [5] or the SF-ASM, still somehow simplify the formations of traders’ expectations.

  6. 6.

    For more details about applying the GP to the evolution of function formation, the readers are recommended to refer to Appendix A in [42].

  7. 7.

    A similar design was used in [3, 24].

  8. 8.

    Technically speaking, we may adjust the value of the parameter to achieve the same effects of the parameter shown in Eq. (8.6); however, λ and γ have their own implications. λ describes a trader’s risk preference, while γ measures the level of a trader’s overconfidence, which is not related to his risk preference.

  9. 9.

    Refer to [3], pp. 40–41.

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Acknowledgements

The authors are grateful for the useful comments received from two anonymous referees. The research support from NSC Grant no. 98-2410-H-155-021 is also gratefully acknowledged.

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Authors and Affiliations

  1. Department of Information Management, Yuan Ze University, Chungli, Taoyuan, 320, Taiwan

    Chia-Hsuan Yeh

  2. Department of Computational Social Science, Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, 22030, USA

    Chun-Yi Yang

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  1. Chia-Hsuan Yeh
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Correspondence to Chia-Hsuan Yeh .

Editor information

Editors and Affiliations

  1. Department of Economics, National Chengchi University, Taipei, Taiwan

    Shu-Heng Chen

  2. Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, Yokohama, Kanagawa, Japan

    Takao Terano

  3. Department of Political Science and Economics, Waseda University, Tokyo, Japan

    Ryuichi Yamamoto

  4. Department of Economics, Tunghai University, Taichung, Taiwan

    Chung-Ching Tai

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Yeh, CH., Yang, CY. (2014). How Does Overconfidence Affect Asset Pricing, Volatility, and Volume?. In: Chen, SH., Terano, T., Yamamoto, R., Tai, CC. (eds) Advances in Computational Social Science. Agent-Based Social Systems, vol 11. Springer, Tokyo. https://doi.org/10.1007/978-4-431-54847-8_8

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