LQ45 Stock Portfolio Optimization by Considering Return Predictions Using the Support Vector Regression (SVR) Method

Abstract

Investors require stock portfolio optimization to achieve desired outcomes. This process involves prediction to obtain optimal weights in the expected portfolio. Historically, portfolio optimization has primarily considered risk and expected value. However, a new approach has integrated return prediction into traditional time series models, enhancing the performance of portfolio optimization models. Machine learning has proven effective in predicting stock market trends, with many researchers applying these models in portfolio formation with satisfactory results. This study employs Support Vector Regression (SVR) method to integrate return prediction in portfolio formation, utilizing stock data from the LQ45 index. Test results indicate that return prediction using SVR method yields a Root Mean Square Error (RMSE) of 0.34973. Portfolios considering return prediction demonstrate superior performance compared to the LQ45 index, as evidenced by portfolio return averages, standard deviation, and Sharpe ratio.