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

Abstract

The application of artificial intelligence (AI) technology in various fields has been a recent research hotspot. As a representative technology of AI, the specific application of machine learning models in the field of economics and finance undoubtedly holds significant research value. This article proposes Extreme Gradient Boosting Multi-Objective Optimization Model with Optimal Weights (OW-XGBoost) to comprehensively balance the returns and risks of investment portfolios. The model utilizes fusing label with optimal weights to achieve multi-objective tasks, effectively controlling the impact of various risk and return indicators on the model, thus improving the interpretability and generalization ability of the model. In the experiments, we tested the model using China A-share data from October 2022 to April 2023 and conducted a series of robustness tests. The results indicate that: (1) The OW-XGBoost outperforms the XGBoost Model with Yield as Label (YL-XGBoost), XGBoost Multi-Label Classification Model (MLC-XGBoost) in controlling risk or achieving returns. (2) OW-XGBoost performs better overall compared to baseline models. (3) The robustness tests demonstrate that the model performs well under different market conditions, stock pools, and training set durations. The model performs best in moderately fluctuating stock markets, stock pools comprising high market value stocks, and training set durations measured in months. The methodology and results of this study provide a new perspective and approach for fundamental quantitative investment and also create new possibilities and avenues for the integration of AI, machine learning, and financial quantitative research.