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The Fama–French Five-Factor Model with Hurst Exponents Compared with Machine Learning Methods

Author

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  • Yicun Li

    (Business School, Research Center of Digital Transformation and Social Responsibility Management, Hangzhou City University (HZCU), Hangzhou 310015, China
    Hangzhou Yiyuan Technology Co., Ltd., Hangzhou 310015, China)

  • Yuanyang Teng

    (School of Management, Zhejiang University, Hangzhou 310027, China)

Abstract

Scholars and investors have been interested in factor models for a long time. This paper builds models using the monthly data of the A-share market. We construct a seven-factor model by adding the Hurst exponent factor and the momentum factor to a Fama–French five-factor model and find that there is a 7% improvement in the average R–squared. Then, we compare five machine learning algorithms with ordinary least squares (OLS) in one representative stock and all A-Share stocks. We find that regularization algorithms, such as lasso and ridge, have worse performance than OLS. SVM and random forests have a good improvement in fitting power, while the neural network is not always better than OLS, depending on the data, frequency, period, etc.

Suggested Citation

  • Yicun Li & Yuanyang Teng, 2023. "The Fama–French Five-Factor Model with Hurst Exponents Compared with Machine Learning Methods," Mathematics, MDPI, vol. 11(13), pages 1-19, July.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2988-:d:1186815
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    References listed on IDEAS

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