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Wealthy individual investors and stock markets’ tail risk

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Listed:
  • He Yu
  • Rong Lu
  • Hu Yang
  • Bin Zhang

Abstract

This paper employs a unique data set to analyze the trading behavior of wealthy individual investors across Mainland China and their impact on Chinese stock markets’ tail risk. Results show that the wealthy individual investors’ trading behavior can explain Chinese stock markets’ tail risk, and the daily investment portfolios based on the network density of wealthy individual investors have significant excess returns. This paper also investigates the determinants of wealthy individual investors’ trading behavior with the social network method and the spatial econometric model, and reveals that wealthy individuals benefit from the spillover effect of their trading behavior through the investor networks. The results of this paper not only reveal micro evidence for the formation mechanism of asset prices, but also provide insight into the behavior of wealthy individual investors.

Suggested Citation

  • He Yu & Rong Lu & Hu Yang & Bin Zhang, 2024. "Wealthy individual investors and stock markets’ tail risk," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-25, May.
    • Handle: RePEc:plo:pone00:0282173
    DOI: 10.1371/journal.pone.0282173
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    References listed on IDEAS

    as
    1. Ng, Lilian & Wu, Fei, 2007. "The trading behavior of institutions and individuals in Chinese equity markets," Journal of Banking & Finance, Elsevier, vol. 31(9), pages 2695-2710, September.
    2. Li, Xindan & Geng, Ziyang & Subrahmanyam, Avanidhar & Yu, Honghai, 2017. "Do wealthy investors have an informational advantage? Evidence based on account classifications of individual investors," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 1-18.
    3. Yang, Haijun & Ge, Hengshun & Gao, Xinpeng, 2022. "An information diffusion model for momentum effect based on investor wealth," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    4. Bryan Kelly & Hao Jiang, 2014. "Editor's Choice Tail Risk and Asset Prices," The Review of Financial Studies, Society for Financial Studies, vol. 27(10), pages 2841-2871.
    5. Lei Feng & Mark Seasholes, 2005. "Do Investor Sophistication and Trading Experience Eliminate Behavioral Biases in Financial Markets?," Review of Finance, Springer, vol. 9(3), pages 305-351, September.
    6. Chen, Joseph & Hong, Harrison & Stein, Jeremy C., 2001. "Forecasting crashes: trading volume, past returns, and conditional skewness in stock prices," Journal of Financial Economics, Elsevier, vol. 61(3), pages 345-381, September.
    7. repec:bla:jfinan:v:59:y:2004:i:5:p:2013-2040 is not listed on IDEAS
    8. Lei Feng & Mark S. Seasholes, 2005. "Do Investor Sophistication and Trading Experience Eliminate Behavioral Biases in Financial Markets?," Review of Finance, European Finance Association, vol. 9(3), pages 305-351.
    9. Bui, Dien Giau & Hasan, Iftekhar & Lin, Chih-Yung & Zhai, Rui-Xiang, 2022. "Income, trading, and performance: Evidence from retail investors," Journal of Empirical Finance, Elsevier, vol. 66(C), pages 176-195.
    10. Joshua D. Coval & Tobias J. Moskowitz, 2001. "The Geography of Investment: Informed Trading and Asset Prices," Journal of Political Economy, University of Chicago Press, vol. 109(4), pages 811-841, August.
    11. Li, Fangzhou & Jiang, Yuxiang, 2022. "Institutional investor networks and crash risk: Evidence from China," Finance Research Letters, Elsevier, vol. 47(PA).
    12. Lung-fei Lee & Xiaodong Liu & Xu Lin, 2010. "Specification and estimation of social interaction models with network structures," Econometrics Journal, Royal Economic Society, vol. 13(2), pages 145-176, July.
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