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Stock Type Prediction Model Based on Hierarchical Graph Neural Network

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Listed:
  • Jianhua Yao
  • Yuxin Dong
  • Jiajing Wang
  • Bingxing Wang
  • Hongye Zheng
  • Honglin Qin

Abstract

This paper introduces a novel approach to stock data analysis by employing a Hierarchical Graph Neural Network (HGNN) model that captures multi-level information and relational structures in the stock market. The HGNN model integrates stock relationship data and hierarchical attributes to predict stock types effectively. The paper discusses the construction of a stock industry relationship graph and the extraction of temporal information from historical price sequences. It also highlights the design of a graph convolution operation and a temporal attention aggregator to model the macro market state. The integration of these features results in a comprehensive stock prediction model that addresses the challenges of utilizing stock relationship data and modeling hierarchical attributes in the stock market.

Suggested Citation

  • Jianhua Yao & Yuxin Dong & Jiajing Wang & Bingxing Wang & Hongye Zheng & Honglin Qin, 2024. "Stock Type Prediction Model Based on Hierarchical Graph Neural Network," Papers 2412.06862, arXiv.org.
    • Handle: RePEc:arx:papers:2412.06862
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    File URL: http://arxiv.org/pdf/2412.06862
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