Stock Type Prediction Model Based on Hierarchical Graph Neural Network

TL;DR

This paper presents a Hierarchical Graph Neural Network model that effectively captures multi-level relationships and temporal information in stock data to improve stock type prediction.

Contribution

The paper introduces a novel HGNN framework that integrates stock relationships and hierarchical attributes for enhanced stock prediction accuracy.

Findings

The model achieves superior prediction performance compared to baseline methods.

Effective modeling of stock relationships improves prediction accuracy.

Hierarchical and temporal features enhance the understanding of stock market dynamics.

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.