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Heterogeneous trading behaviors of individual investors: A deep clustering approach

Author

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  • Hwang, Yoontae
  • Park, Junpyo
  • Kim, Jang Ho
  • Lee, Yongjae
  • Fabozzi, Frank J.

Abstract

While individual investors may have more diverse preferences and trading behavior than institutional investors due to their lack of professional education, many studies tend to lump individual investors together or classify them by socio-demographic characteristics. We conducted an empirical study using account-level trading data for over 300,000 investors in the Korean stock market from 2016 to 2020 to analyze the heterogeneity of individual investors. Our findings reveal notable disparities in profit distributions among the clusters formed based on investors' trading behavior. Therefore, this study emphasizes the importance of exploring the heterogeneity of individual investors to understand their behavior better.

Suggested Citation

  • Hwang, Yoontae & Park, Junpyo & Kim, Jang Ho & Lee, Yongjae & Fabozzi, Frank J., 2024. "Heterogeneous trading behaviors of individual investors: A deep clustering approach," Finance Research Letters, Elsevier, vol. 65(C).
    • Handle: RePEc:eee:finlet:v:65:y:2024:i:c:s1544612324005117
    DOI: 10.1016/j.frl.2024.105481
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    Keywords

    Individual investor; Trading behavior; Transactions data; Clustering; Machine learning; Deep learning;
    All these keywords.

    JEL classification:

    • G50 - Financial Economics - - Household Finance - - - General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • G40 - Financial Economics - - Behavioral Finance - - - General

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