Stock Modeling Using BP Neural Networks

Following the gradual establishment of chaos theory and fractal theory in stock markets, neural networks have been increasingly utilized to predict fluctuations in securities markets. This study aims to develop a stock price prediction methodology based on BP neural networks to enhance prediction accuracy and computational efficiency, providing new practical tools for both individual and institutional investors.

The stock market represents a complex system where stock prices are influenced by multiple factors including company fundamentals, macroeconomic indicators, and policy changes. BP neural networks, as a type of artificial neural network, can predict unknown data outputs through learning from training datasets. In our implementation, we construct a three-layer network architecture (input-hidden-output) using sigmoid activation functions, where the backpropagation algorithm minimizes prediction errors through iterative weight adjustments using gradient descent optimization.

To validate the model's effectiveness, we conduct testing with actual historical stock data and perform comparative analysis with other prediction methods. Experimental results demonstrate that the BP neural network-based approach significantly improves both prediction accuracy and computational speed. The model training process involves normalized input data preprocessing, dynamic learning rate adjustment, and early stopping mechanisms to prevent overfitting. For practical implementation, key functions include data normalization, network weight initialization, forward propagation calculations, and backward error propagation with weight updates. This methodology proves to be highly practical for investors seeking data-driven decision support tools.