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Multifactor Stock Selection Strategy Based on Machine Learning: Evidence from China

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  • Jieying Gao
  • Huan Guo
  • Xin Xu

Abstract

Machine learning methods have been used in multifactor stock strategy for years. This paper uses three machine learning methods and linear regression method to find the most appropriate approach. First, a framework is established and 10 style factors and 30 industry factors are chosen. Second, four methods are used to forecast portfolio returns and compared by predicting returns, successful rate, and Sharpe ratio. Finally, this paper draws conclusion. The main findings are as follows: the support vector regression has the most stable successful rate for predicting, while ridge regression and linear regression have the most unstable successful rate with more extreme cases; algorithm of support vector regression fitting higher‐degree polynomials in Chinese A‐share market is optimized, compared with the traditional linear regression both in terms of stock return and retracement control; the results of support vector regression significantly outperforming the CSI 500 index prove further.

Suggested Citation

  • Jieying Gao & Huan Guo & Xin Xu, 2022. "Multifactor Stock Selection Strategy Based on Machine Learning: Evidence from China," Complexity, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:7447229
    DOI: 10.1155/2022/7447229
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    References listed on IDEAS

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    1. Wanbo Lu & Tingting Qiu & Wenhui Shi & Xiaojun Sun, 2022. "International Gold Price Forecast Based on CEEMDAN and Support Vector Regression with Grey Wolf Algorithm," Complexity, John Wiley & Sons, vol. 2022(1).

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