Heterogeneous trading behaviors of individual investors: A deep clustering approach

•Categorized trading behaviors of individual investors using deep learning.•Trading behaviors such as buy/sell orders and deposit/withdrawals are considered.•Used transactions data of ∼300,000 retail investors in Korea from 2016 to 2020.•Found notable differences in realized and unrealized profits d...

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Bibliographic Details
Published in:Finance research letters 2024-07, Vol.65, p.105481, Article 105481
Main Authors: Hwang, Yoontae, Park, Junpyo, Kim, Jang Ho, Lee, Yongjae, Fabozzi, Frank J.
Format: Article
Language:English
Subjects:
Clustering
Deep learning
Individual investor
Machine learning
Trading behavior
Transactions data
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Summary:•Categorized trading behaviors of individual investors using deep learning.•Trading behaviors such as buy/sell orders and deposit/withdrawals are considered.•Used transactions data of ∼300,000 retail investors in Korea from 2016 to 2020.•Found notable differences in realized and unrealized profits depending on trading behaviors. 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.
ISSN:1544-6123
DOI:10.1016/j.frl.2024.105481