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A machine learning approach for comparing the largest firm effect

Author

Listed:
  • Kim, Jang Ho
  • Han, Jiwoon
  • Kang, Taehyeon
  • Fabozzi, Frank J.

Abstract

Market capitalization of firms provides valuable information for analyzing stock markets and the size factor is widely used in factor-based investing. Some markets, such as the Korean market, are especially interesting in this respect because they contain extremely large public firms. This study analyzes the effect of the largest firm on factor investing through machine learning models that are effective for variable selection. We demonstrate how machine learning can be used for identifying important factors. Our comparison between US and Korean markets shows the significance of separating the largest firm in analyzing how factors impact performance in the Korean market.

Suggested Citation

  • Kim, Jang Ho & Han, Jiwoon & Kang, Taehyeon & Fabozzi, Frank J., 2023. "A machine learning approach for comparing the largest firm effect," Emerging Markets Review, Elsevier, vol. 54(C).
    • Handle: RePEc:eee:ememar:v:54:y:2023:i:c:s1566014122001121
    DOI: 10.1016/j.ememar.2022.100995
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    References listed on IDEAS

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    More about this item

    Keywords

    Market capitalization; Variable selection; Lasso regression; Random forest;
    All these keywords.

    JEL classification:

    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools

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