Optimizing Random Forest Hyperparameters for Enhanced Stock Price Prediction

Random Forests are widely used in financial forecasting because they capture complex patterns and mitigate noise. However, their predictive performance heavily depends on the careful selection of hyperparameters. This study explores the impact of hyperparameter tuning on Random Forest models for stock price prediction. We enhance model accuracy and robustness by systematically adjusting key parameters-such as the number of trees, maximum depth, and minimum sample split. Our findings reveal that well-optimized hyperparameters significantly improve predictive performance while reducing overfitting, resulting in more reliable insights for investors. This research contributes to financial forecasting methodologies by providing empirical evidence of the crucial role of hyperparameter tuning in refining Random Forest models.