Developing a Prediction-Based Stock Returns and Portfolio Optimization Model

The purpose of this study is to develop a prediction-based stock returns and portfolio optimization model using a combined decision tree and regression model. The empirical evidence is based on the analysis on ۱۱۲ unique firms listed on the Tehran Stock Exchange from ۲۰۰۹ to ۲۰۱۹. Regression analyses, as well as six decision tree techniques including CHAID, ID۳, CRIUSE, M۵, CART, and M۵ are used to determine the most effective variables for predicting stock returns. The results show that the six decision tree methods perform better than the regression model in selecting the optimal portfolio. Further analysis reveals that the CART model outperforms the other five decision tree models when compared using Akaike and Schwartz Bayesian. This finding is confirmed by comparing the actual returns of the selected portfolio across all six models in ۲۰۱۹. The findings indicate that the predicted returns on portfolio based on the CART model are not significantly different than the actual returns for ۲۰۱۹, suggesting that the selected model appropriately predicts the returns on the portfolio

کلیدواژه ها:

Stock Return Prediction ، Portfolio Selection ، Decision tree