Integrated Learning Comprehensive Evaluation of Stock Market Prediction

Abstract

Ensemble learning methods have gained significant attention in the realm of stock - market prediction due to their ability to combine multiple models for enhanced accuracy and robustness. In this study, we conduct a comprehensive evaluation of various ensemble learning techniques, including bagging, boosting, and stacking, applied to the task of predicting stock - market movements. Our evaluation encompasses a diverse set of financial markets and time periods, considering both traditional machine learning algorithms and deep learning architectures as base models. We systematically compare the performance of ensemble methods against individual models and benchmark strategies, utilizing a range of evaluation metrics such as accuracy, precision, recall, and F1 - score. Additionally, we investigate the impact of ensemble size, diversity of base models, and ensemble composition on predictive performance. Our findings provide valuable insights into the effectiveness and practical considerations of ensemble learning for stock - market prediction, offering guidance for researchers and practitioners in the field of financial forecasting.