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Predicting Chinese stock market using XGBoost multi-objective optimization with optimal weighting.

Jichen Liu1

  • 1School of International Trade and Economics, University of International Business and Economics, Beijing, China.

Peerj. Computer Science
|March 14, 2024
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A new machine learning model, Optimal Weights Extreme Gradient Boosting (OW-XGBoost), balances investment portfolio risks and returns. This AI approach enhances financial quantitative research and investment strategies.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Financial Quantitative Research

Background:

  • Artificial intelligence (AI) is a growing research area with significant potential in finance.
  • Machine learning models offer valuable tools for economic and financial analysis.

Purpose of the Study:

  • To propose and evaluate the Optimal Weights Extreme Gradient Boosting (OW-XGBoost) model.
  • To balance investment portfolio returns and risks using a novel multi-objective optimization approach.

Main Methods:

  • Developed the OW-XGBoost model, fusing labels with optimal weights for multi-objective optimization.
  • Applied the model to China A-share data (October 2022 - April 2023).
  • Conducted robustness tests across various market conditions and stock selections.

Main Results:

  • OW-XGBoost demonstrated superior performance in risk control and return generation compared to baseline XGBoost models (YL-XGBoost, MLC-XGBoost).
  • The model showed overall better performance than existing methods.
  • Robustness tests confirmed consistent performance across different market conditions, stock pools, and training durations.

Conclusions:

  • The OW-XGBoost model offers an effective approach for balancing investment portfolio risk and return.
  • The study provides new avenues for integrating AI and machine learning in financial quantitative research.
  • The model shows optimal performance in moderately volatile markets with high-value stocks and monthly training data.