TD-HCN: A trend-driven hypergraph convolutional network for stock return prediction

Abstract

Stock data analysis has become one of the most challenging tasks in time series data analysis due to its dynamism, complexity, and nonlinearity. Recently, relational graphs have become popular for describing certain important relationships in data, particularly by mapping indirect and direct relationships between stocks into non-Euclidean spaces. Existing graph-based methods mainly capture simple pairwise and static relationships between stocks, so they cannot effectively identify higher-order relationships and characterize the dynamic trends of stock relationships. This limitation restricts the performance of stock return prediction models. A variety of stock data types reveal complex relationships among stocks, such as stock prices, industry links, and wiki relationships. This paper proposes a novel Trend-Driven Hypergraph Convolutional Network (TD-HCN) that integrates these data types in order to predict stock rankings through a cooperative learning method of local dynamic and global static relationships across temporal dimensions. To be concrete, we employ a Prior-constrained Relational Learning (PCRL) model that leverages explicit prior knowledge to guide the discovery of latent high-order relationships among stocks. In order to comprehensively capture and utilize dynamic trends in relationships among stocks, a Disentanglement Representation Learning (DRL) mechanism is developed to enhance the key trend features through the disentanglement operation and dual attention module. Extensive experiments on NASDAQ and NYSE datasets show that TD-HCN consistently outperforms the state-of-the-art methods by a considerable margin in terms of returns. It is also effective and robust in learning the dynamic relationships among stocks and capturing key changes in trends within those relationships.