Stock Type Prediction Model Based on Hierarchical Graph Neural Network
This work addresses stock market analysis for investors and analysts, but it appears incremental as it builds on existing graph neural network techniques.
This paper tackles stock type prediction by developing a Hierarchical Graph Neural Network (HGNN) model that integrates stock relationship data and hierarchical attributes, resulting in an effective prediction model for stock types.
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.