Quantum-Inspired Swarm Optimization Algorithm for High-Frequency Stock Trend Forecasting

High-frequency stock trend forecasting is a critical component of financial decision-making that requires accurate and rapid prediction capabilities. Traditional optimization and prediction models often fall short in capturing the non-linear, volatile, and time-sensitive nature of high-frequency financial data. Existing swarm-based and deep learning techniques suffer from slow convergence, local optima entrapment, and lack of adaptability to dynamic market conditions. To overcome these limitations, this paper proposes a novel Quantum-Inspired Swarm Forecasting Algorithm (QISFA) that integrates principles of quantum mechanics with particle swarm optimization (PSO) and deep learning to enhance forecasting performance. QISFA utilizes quantum-inspired behavior to improve population diversity and exploration capability while coupling with Long Short-Term Memory (LSTM)-based neural networks for capturing temporal dependencies in stock prices. The proposed method is tested on real high-frequency stock datasets and benchmarked against standard models. Experimental results demonstrate that QISFA significantly outperforms traditional PSO, LSTM, and hybrid models in terms of prediction accuracy, convergence speed, and robustness under varying market conditions. This approach presents a promising advancement for traders and analysts seeking intelligent tools for real-time market prediction.

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