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

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  • Han, Muye
  • Hao, Zhicheng
  • Zhao, Yukun

Abstract

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.

Suggested Citation

  • Han, Muye & Hao, Zhicheng & Zhao, Yukun, 2024. "Stock price crash risk prediction based on high-low frequency dual-layer graph attention network," International Review of Economics & Finance, Elsevier, vol. 96(PB).
    • Handle: RePEc:eee:reveco:v:96:y:2024:i:pb:s1059056024006002
    DOI: 10.1016/j.iref.2024.103608
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