Hybrid Genetic Algorithm and XGBoost Model for Optimizing Sharia Compliant Stock Portfolios in the Indonesian Capital Market - Gellysa Urva; Welly Desriyati (Politeknik Negeri Cilacap, 2026)
This study proposes a hybrid portfolio optimization model that integrates Genetic Algorithm (GA) and Extreme Gradient Boosting (XGBoost) to optimize Sharia-compliant stock portfolios in the Indonesian capital market. The model leverages the predictive capability of XGBoost and the adaptive optimization mechanism of GA to achieve an optimal balance between return and risk under Sharia investment principles. The dataset consists of historical daily stock price data from six Sharia-compliant stocks (ADRO, INDF, KLBF, TLKM, UNVR, and ICBP) during 2023. XGBoost was employed to predict stock returns using selected technical indicators, and its performance was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The predicted returns were then used as inputs for portfolio optimization using GA. The results show that the hybrid model produces an optimized portfolio with KLBF (22.7%) and ICBP (21.3%) receiving the highest allocations, indicating favorable risk–return characteristics. The model achieved RMSE values ranging from 0.0081 to 0.0142 and MAPE values between 4.24% and 6.11%, demonstrating high predictive accuracy. Furthermore, the hybrid portfolio outperformed benchmark strategies, achieving an expected return of 6.42% with a Sharpe Ratio of 0.30. These findings confirm that the integration of GA and XGBoost enhances both prediction reliability and portfolio optimization performance. This study contributes to the development of artificial intelligence-based portfolio optimization in Islamic finance and provides a practical framework for ethical and data-driven investment decision-making.