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Analysis of herding behavior in individual investor portfolios using machine learning algorithms

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  • Mavruk, Taylan

Abstract

This paper examines the determinants of herding at both stock and individual investor levels and studies the portfolio performance of herd vs. non-herd portfolios using machine learning algorithms. The disposition effect and the attention effect seem to explain herding behavior at the stock level. At the individual investor level, the cumulative number of buys and portfolio values reduce the prediction of herding behavior, while high values of portfolio return lead to a small increase in herding. Individuals who herd do not outperform either market or non-herd portfolios, suggesting that herding is a behavioral bias. Thus, such behavior seems to destabilize stock markets, creating temporary discrepancies in stock prices followed by reversals back to fundamentals. The most predictive factor in the performance tests of individual portfolios is the market risk premium and using equally-weighted factors rather than value-weighted factors seem to provide more consistent results in the portfolio performance analyses.

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  • Mavruk, Taylan, 2022. "Analysis of herding behavior in individual investor portfolios using machine learning algorithms," Research in International Business and Finance, Elsevier, vol. 62(C).
    • Handle: RePEc:eee:riibaf:v:62:y:2022:i:c:s0275531922001283
    DOI: 10.1016/j.ribaf.2022.101740
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    1. Guiqiang Shi & Dehua Shen & Zhaobo Zhu, 2024. "Herding towards carbon neutrality: The role of investor attention," Post-Print hal-04348526, HAL.

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    More about this item

    Keywords

    Herding; Portfolio performance; Machine learning algorithms;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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