Stock price crash risk prediction based on high-low frequency dual-layer graph attention network

The phenomenon of a stock price crash involves a rapid, significant decrease in stock prices, severely impacting the market, investors, and the economy. This study introduces the BiGAT-GRU model, which combines Graph Attention Networks (GAT) and Gated Recurrent Units (GRU) to predict stock price crash risk by analyzing multi-scale investor sentiment propagation using data from Baidu search index and public opinion texts. The model demonstrates superior performance in predicting crash risk, providing valuable insights for policymakers and investors.