Development of intelligent algorithms for predicting stock market trends using historical data analysis

Posted: Sep 30, 2021

• Abigail Rodriguez
• Aiden Roberts
• Aiden Rodriguez

Abstract

The prediction of stock market trends represents one of the most challenging problems in financial analytics, characterized by high volatility, non-stationary behavior, and complex interdependencies among multiple factors. Traditional approaches to stock prediction have predominantly relied on technical analysis, statistical models, and more recently, machine learning techniques. However, these methods often struggle to capture the underlying chaotic dynamics and emergent patterns in financial markets. This research introduces a fundamentally new approach that transcends conventional methodologies by integrating principles from quantum computing with deep learning architectures. Financial markets exhibit properties that bear remarkable similarity to quantum systems, including superposition of states, entanglement-like correlations between assets, and measurement-induced perturbations. These characteristics suggest that quantum-inspired representations may offer superior modeling capabilities for market behavior. Our work builds upon this insight to develop the Quantum-Enhanced Temporal Convolutional Network (QETCN), which represents a significant departure from existing prediction frameworks. The novelty of our approach lies in three key contributions: first, the development of quantum amplitude encoding for financial time series data, which transforms price movements into quantum state representations; second, the integration of these quantum representations with temporal convolutional networks to capture multi-scale temporal dependencies; and third, the introduction of a quantum-inspired attention mechanism that dynamically weights the importance of different market regimes.