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The overconfident trading behavior of individual versus institutional investors

Author

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  • Liu, Hsiang-Hsi
  • Chuang, Wen-I
  • Huang, Jih-Jeng
  • Chen, Yu-Hao

Abstract

A double-threshold GARCH model is employed to simultaneously investigate the relative degree of overconfident trading of individual versus institutional investors and the impact of their overconfident trading on stock return volatility across high and low market return regimes. The results show that both individual and institutional investors trade more overconfidently in high market return regimes than in low, which corresponds to the finding that the return volatility is also higher in high market return regimes compared to low regimes. Conditional on the market state, market volatility, and market liquidity, it is believed that both individual and institutional investors exhibit more pronounced overconfident trading behavior when the market is up, less volatile, and more liquid across market return regimes. Finally, we obtain consistent evidence that individual investors trade with more overconfidence than institutional investors in these market conditions during high market return regimes, indicating that individual investors are more overconfident traders than institutional investors.

Suggested Citation

  • Liu, Hsiang-Hsi & Chuang, Wen-I & Huang, Jih-Jeng & Chen, Yu-Hao, 2016. "The overconfident trading behavior of individual versus institutional investors," International Review of Economics & Finance, Elsevier, vol. 45(C), pages 518-539.
    • Handle: RePEc:eee:reveco:v:45:y:2016:i:c:p:518-539
    DOI: 10.1016/j.iref.2016.07.016
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    References listed on IDEAS

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    4. Gupta, Suman & Das, Debojyoti & Hasim, Haslifah & Tiwari, Aviral Kumar, 2018. "The dynamic relationship between stock returns and trading volume revisited: A MODWT-VAR approach," Finance Research Letters, Elsevier, vol. 27(C), pages 91-98.
    5. Zhang, Mingshan, 2022. "Warren Buffett Anomaly," Finance Research Letters, Elsevier, vol. 46(PB).
    6. Jitender Kumar & Neha Prince, 2022. "Overconfidence bias in the Indian stock market in diverse market situations: an empirical study," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 3031-3047, December.
    7. Ramzi Boussaidi, 2022. "Implications of the overconfidence bias in presence of private information: Evidence from MENA stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3660-3678, July.
    8. Koziol, Christian & Proelss, Juliane, 2021. "An explanation for momentum with a rational model under symmetric information – Evidence from cross country equity markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 70(C).
    9. Ben Amor, Salma & Kooli, Maher, 2024. "Does overconfidence affect venture capital firms’ investment?," Journal of Behavioral and Experimental Finance, Elsevier, vol. 41(C).
    10. Suman Gupta & Vinay Goyal & Vinay Kumar Kalakbandi & Sankarshan Basu, 2018. "Overconfidence, trading volume and liquidity effect in Asia’s Giants: evidence from pre-, during- and post-global recession," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 45(3), pages 235-257, September.
    11. Xiong, Tao & Wang, Peng, 2023. "Institutional ownership and momentum in the Chinese A-share market," Pacific-Basin Finance Journal, Elsevier, vol. 79(C).
    12. Esra Alp Coşkun & Hakan Kahyaoglu & Chi Keung Marco Lau, 2023. "Which return regime induces overconfidence behavior? Artificial intelligence and a nonlinear approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-34, December.

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    More about this item

    Keywords

    Overconfident trading; Double-threshold GARCH model; Market regime; Market volatility; Market liquidity;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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