Multi-objective portfolio optimization for stock return prediction using machine learning

This paper presents a novel approach that integrates stock return prediction with the mean–variance (MV) model to enhance the performance of the original model. Firstly, stock returns are predicted using machine learning algorithms, including Robust Linear Regression (OLS-H), Random Forest (RF), and Long Short-Term Memory Networks (LSTM), to select a pre-screened stock pool composed of stocks with high predicted returns. Secondly, a linear weighting method combines the predictions above with the MV model, constructing the Mean-Variance-Forecast Error (MVF) model and determining the investment proportions for the pre-selected stocks. Finally, empirical research is conducted using the components of the CSI 300 Index as sample data. The results indicate that the RF + MVF model outperforms other models and the CSI 300 Index in return and risk metrics. At the same time, a sensitivity analysis of relevant parameters further confirms that considering return uncertainty is beneficial for improving the out-of-sample performance of the MV model.