Optimization and Improvement of Recurrent Neural Networks in Stock Price Prediction

Abstract

Recurrent Neural Networks (RNNs) are a significant branch of deep learning, particularly suitable for handling data with temporal dependencies. In the financial domain, stock price prediction is a highly focused problem. Traditional stock price prediction methods, often based on linear models, fail to fully capture the nonlinear dynamics of stock prices. RNNs, with their memory capabilities, can capture the long-term dependencies of stock prices, hence offering great application potential in stock price prediction. However, RNNs also face challenges when processing stock price data, as stock prices are influenced by numerous factors and are highly complex and uncertain. Additionally, stock price data often contains noise and outliers, impacting the model’s predictive performance. To address these issues, incremental improvements in prediction accuracy and model generalization capabilities are achieved through optimizing the model structure and improving data preprocessing methods. As technology advances and algorithms continue to innovate, the application of RNNs in stock price prediction will become more widespread and in-depth.