Predicted Mean Semi-Variance Portfolio Optimization Based on Machine Learning
In this paper,the predicted returns rates are forecast by Random Forest,XG-Boost,and SVR.The mean,semi-variance and semi-covariance of the portfolio model cal-culated by using the average predicted returns rates.Considering the threshold constraints,borrowing constraints and transaction costs,the mean semi-variance portfolio model is proposed.The model is solved by the pivoting algorithms.Finally,this paper uses the constituent stocks of CSI 100 index as a sample for empirical analysis.The results show that SVR+M-SV model has a higher level of efficient frontier compared to RF+M-SV and XGBoost+M-SV model in the in-sample test;In the out-of-sample test,the three machine learning hybrid algorithms+M-SV model significantly outperforms the M-SV and 1/N mod-els in terms of return,Sharpe ratio,and Sortino ratio.
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