PSO-BP Neural Network Model in Stock Prediction

Stock prediction is crucial for financial product pricing, optimal asset allocation, speculative strategy research and risk management, etc., and there are many experts and scholars devoted to stock price prediction analysis. However, due to the volatility and non-linear nature of stock prices, it brings great difficulties to stock prediction, which makes some machine learning methods applied to stock prediction.This study introduces a Particle Swarm Optimization-Back Propagation (PSO-BP) neural network model designed to enhance stock price prediction. The PSO algorithm optimizes the BP neural network by fine-tuning the number of hidden layer nodes, thereby improving prediction accuracy and robustness. Experimental results reveal that the PSO-BP model not only reduces prediction time, averaging between 0.5 to 0.8 seconds, but also demonstrates a more efficient calculation speed compared to the traditional BP algorithm. The PSO-BP model performs better with increased sample data, showing minimal increases in calculation time, thus indicating its robust performance. The findings suggest that the PSO-BP model provides a reliable and efficient method for stock price prediction, with notable improvements in both speed and accuracy compared to existing methods.