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Combined machine learning for stock selection strategy based on dynamic weighting methods

Author

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  • Lin Cai

    (Department of Statistics, Columbia University, New York, USA)

  • Zhiyang He

    (Department of Engineering and Informatics, University of Sussex, Brighton, UK)

  • Caiya Zhang

    (Department of Statistics and Data Science, Hangzhou City University, Hangzhou, China)

Abstract

This paper proposes a novel stock selection strategy framework based on combined machine learning algorithms. Two types of weighting methods for three representative machine learning algorithms are developed to predict the returns of the stock selection strategy. One is static weighting based on model evaluation metrics, the other is dynamic weighting based on Information Coefficients (IC). Using CSI 300 index data, we empirically evaluate the strategy' s backtested performance and model predictive accuracy. The main results are as follows: (1) The strategy by combined machine learning algorithms significantly outperforms single-model approaches in backtested returns. (2) IC-based weighting (particularly IC_Mean) demonstrates greater competitiveness than evaluation-metric-based weighting in both backtested returns and predictive performance. (3) Factor screening substantially enhances the performance of combined machine learning strategies.

Suggested Citation

  • Lin Cai & Zhiyang He & Caiya Zhang, 2025. "Combined machine learning for stock selection strategy based on dynamic weighting methods," Papers 2508.18592, arXiv.org.
    • Handle: RePEc:arx:papers:2508.18592
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    File URL: http://arxiv.org/pdf/2508.18592
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