Stock Return Prediction on the LQ45 Market Index in the Indonesia Stock Exchange Using a Machine Learning Algorithm Based on Technical Indicators

Stock return prediction in emerging markets remains difficult due to the gap between theoretical efficiency and empirical irregularities. This study assesses the statistical and economic performance of Linear Regression, Ridge Regression, Random Forest, and XGBoost in forecasting 5-day and 21-day returns for six LQ45 stocks (2016–2025). Momentum, volatility, trend, and volume indicators are used as predictors, while model performance is evaluated using MAE, RMSE, R2, and backtested trading metrics that include transaction costs. All models yield near-zero or negative R2, directional accuracy of 49–54%, and AUC around 0.50–0.53, indicating weak signals overshadowed by noise. XGBoost offers the lowest statistical errors, but Ridge Regression achieves slightly better risk-adjusted outcomes (Sharpe 0.1232), although every strategy underperforms Buy & Hold. SHAP results show volatility and volume features as most influential, but with minimal absolute impact. Overall, the LQ45 market exhibits semi-efficiency: patterns exist but fail to translate into profitable trading once real-world frictions are considered, underscoring the gap between statistical predictability and economic viability in algorithmic trading. This research was conducted in order to support the achievement of various goals through SDG 8 (Decent Work and Economic Growth).