Deep learning for stock price prediction: A comparative study of stacked and weighted ensemble models

In this paper, we provide a unique ensemble-based framework that combines sophisticated deep learning architectures and novel ensemble algorithms to improve forecast accuracy and robustness. The framework includes the Stacking Ensemble Meta-Model, which aggregates predictions as meta-features that are processed by a linear regression meta-model. We also introduce an Optimized Weighted Ensemble model, which uses constrained optimization to compute optimal weights for combining predictions from LSTM, CNN, and Hybrid CNN/LSTM-Attention-based models. Our results show that our proposed ensemble meta-model framework outperforms the optimized weighted ensemble model, with improved predictive accuracy across the evaluation metrices.