用分层图神经网络预测股票类型，捕捉行业关系与时间动态。
Stock Type Prediction Model Based on Hierarchical Graph Neural Network
- 构建股票行业关系图，分层建模行业与个股关系
- 融合时间注意力机制，捕捉市场状态演变规律
- 适合量化投资、金融分析领域研究者参考
本文提出一种基于分层图神经网络（HGNN）的股票数据分析方法，通过构建股票行业关系图并提取历史价格序列中的时序信息，有效捕捉股票市场的多层级结构与关联关系。模型设计了图卷积操作与时间注意力聚合器，用于建模宏观市场状态。该方法整合了股票间关系数据与分层属性，显著提升了股票类型预测性能，解决了利用股票关系数据及建模层次特征的挑战。
原文摘要 · Abstract (English)
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.
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