HYBRID AI FOR STOCK MARKETS USING TRANSFORMERS AND QINN

Abstract

The rapid growth of financial markets and the increasing complexity of trading environments have necessitated the development of intelligent systems capable of accurate stock market prediction and decision-making. Traditional statistical models often fail to capture the nonlinear, dynamic, and highly volatile nature of stock price movements. To address these challenges, this project proposes a Hybrid AI Framework for Stock Market Prediction using Transformers and QuantumInspired Neural Networks (QINN). The system integrates the strengths of Transformer architectures, known for their ability to model long-range dependencies in sequential data, with Quantum-Inspired Neural Networks, which enhance optimization and pattern recognition capabilities through principles derived from quantum computing. In the proposed approach, historical stock market data, including price, volume, and technical indicators, are preprocessed and fed into a Transformer-based model to capture temporal dependencies and market trends. The extracted features are then passed to the QINN module, which performs advanced nonlinear mapping and optimization to improve prediction accuracy. This hybrid architecture enables the system to effectively model both short-term fluctuations and long-term patterns in stock data. Additionally, the system incorporates attention mechanisms to focus on relevant features and reduce noise in financial datasets. Experimental results demonstrate that the hybrid model outperforms traditional machine learning and standalone deep learning approaches in terms of prediction accuracy, robustness, and adaptability to market volatility. The system can be applied to various financial tasks such as stock price prediction, portfolio optimization, and algorithmic trading. Furthermore, the integration of quantum-inspired techniques provides a novel direction for enhancing AI performance in financial analytics. Overall, this research contributes to the development of next-generation intelligent trading systems that are more accurate, efficient, and capable of handling complex financial data