Neural Network-Based CSI300 Stock Prediction: Feature Importance and Attention Mechanism Analysis

In this study, neural networks are utilized to develop a stock price prediction model based on the constituent stocks of the China Securities Index 300 (CSI300). This research investigates various prediction methods and models through experiments, comparing their advantages, limitations, and applicability to improve the accuracy and efficiency of stock price forecasting. Furthermore, we analyze the characteristics of CSI300 constituent stocks and explore the relationships among key variables and influential factors, enhancing our understanding of stock market behavior. Additionally, we explore the impact of the attention mechanism on stock price prediction, assess its contribution to enhancing predictive performance, and propose an optimized CSI300 stock prediction model based on neural networks.