Factor Investment or Feature Selection Analysis?

Jifang Mai, Shaohua Zhang (), Haiqing Zhao and Lijun Pan
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Jifang Mai: School of Economics, Jinan University, Guangzhou 510632, 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

Mathematics, 2024, vol. 13, issue 1, 1-34

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.

Keywords: machine learning; multicollinearity; CPCA framework; lasso method (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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