Deep Learning and Optimization Approaches in An Optimized Equivariant Split Attention Quantum Neural Network Based Recommendation System for Stock Market Prediction: A Review

Abstract

Financial markets are complex, nonlinear, and highly dynamic systems, making accurate stock market prediction a challenging task. Traditional statistical and machine learning models often fail to capture the intricate temporal dependencies and high-dimensional interactions present in financial data. This review explores advanced approaches integrating deep learning, quantum computing, equivariant neural networks, and split attention mechanisms for stock market prediction and recommendation systems. Quantum neural networks leverage principles such as superposition and entanglement to enhance feature representation and computational efficiency, while equivariant architectures improve generalization by preserving structural relationships in data. Split attention mechanisms further enhance model performance by capturing both local and global temporal dependencies. The study also examines optimization strategies, including gradient-based methods and evolutionary algorithms, for efficient training of hybrid quantum-classical models. Additionally, the integration of recommendation systems enables personalized investment decision-making by combining market data with user preferences. Evaluations on benchmark financial datasets demonstrate improved prediction accuracy, scalability, and robustness compared to conventional models. Overall, the proposed framework offers a promising direction for developing intelligent, efficient, and next-generation financial forecasting systems.