Equivariant Split Attention Quantum Neural Networks for Intelligent Stock Market Prediction Systems

Abstract

The rapid advancement of artificial intelligence, deep learning, and quantum computing has significantly transformed stock market prediction and financial analytics. Traditional statistical models are limited in capturing the nonlinear, stochastic, and high-dimensional nature of financial time series, leading to the adoption of advanced hybrid computational frameworks. This review presents an optimized equivariant split attention quantum neural network (OESAQNN) framework for stock market prediction and recommendation systems. The architecture integrates quantum neural networks with equivariant learning to preserve structural relationships in financial data, while split attention mechanisms enhance feature extraction by focusing on relevant temporal and contextual patterns. The study explores hybrid classical-quantum optimization techniques, including parameter-shift gradient methods and adaptive optimization algorithms, to improve convergence and computational efficiency. It also incorporates multimodal datasets such as historical prices, sentiment data, and macroeconomic indicators for robust prediction. Results demonstrate improved accuracy, scalability, and interpretability compared to conventional deep learning models.Overall, the proposed framework offers a promising direction for developing intelligent, efficient, and next-generation financial forecasting and recommendation systems for real-time decision-making.