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Factor Investment or Feature Selection Analysis?

Author

Listed:
  • Jifang Mai

    (School of Economics, Jinan University, Guangzhou 510632, China
    School of Mathematics and Statistics, Lingnan Normal University, Zhanjiang 524033, China)

  • Shaohua Zhang

    (School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China)

  • Haiqing Zhao

    (School of Mathematics and Statistics, Lingnan Normal University, Zhanjiang 524033, China)

  • Lijun Pan

    (School of Mathematics and Statistics, Lingnan Normal University, Zhanjiang 524033, China)

Abstract

This study has made significant findings in A-share market data processing and portfolio management. Firstly, by adopting the Lasso method and CPCA framework, we effectively addressed the problem of multicollinearity among feature indicators, with the Lasso method demonstrating superior performance in handling this issue, thus providing a new method for financial data processing. Secondly, Deep Feedforward Neural Networks (DFN) exhibited exceptional performance in portfolio management, significantly outperforming other evaluated machine learning methods, and achieving high levels of out-of-sample performance and Sharpe ratios. Additionally, we consistently identified price changes, earnings per share, net assets per share, and excess returns as key factors influencing predictive signals. Finally, this study combined the Lasso method with DFN, providing a new perspective and methodological support for asset pricing measurement in the financial field.

Suggested Citation

  • Jifang Mai & Shaohua Zhang & Haiqing Zhao & Lijun Pan, 2024. "Factor Investment or Feature Selection Analysis?," Mathematics, MDPI, vol. 13(1), pages 1-34, December.
    • Handle: RePEc:gam:jmathe:v:13:y:2024:i:1:p:9-:d:1551677
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