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.
@article{arxiv.2412.06862,
title = {Stock Type Prediction Model Based on Hierarchical Graph Neural Network},
author = {Jianhua Yao and Yuxin Dong and Jiajing Wang and Bingxing Wang and Hongye Zheng and Honglin Qin},
journal= {arXiv preprint arXiv:2412.06862},
year = {2024}
}