A Novel Hybrid HO-CAL Framework for Enhanced Stock Index Prediction

The accurate prediction of stock indexes plays a critical role in supporting investment decisions and managing financial risks. This study proposed a novel hybrid deep learning model that integrated the strengths of Convolutional Neural Networks (CNN), the Attention mechanism, and Long Short-Term Memory (LSTM) networks to enhance the modelling of temporal patterns in financial time series. To further improve the prediction performance, the Hippopotamus Optimization (HO) algorithm was incorporated to fine-tune the networks parameters. This is the first application of the CNN-Attention-LSTM (CAL) architecture to stock index prediction. Ablation experiments revealed that the proposed CAL significantly outperformed traditional CNN, LSTM, and CNN-LSTM models, highlighting the effectiveness of the Attention-based architecture. Comparative analyses also demonstrated that the HO-optimized CAL (HO-CAL) model achieved superior predictive accuracy across multiple markets, confirming both the robustness of the hybrid model and the optimization algorithm. These findings underscore the potential of combining deep learning architectures with metaheuristic optimization to improve the prediction accuracy in financial markets, offering valuable insights for real-world investment strategies.