Hypergraph Neural Networks to Predict Stock Movements By Exploring Higher-order Relationships

Predicting stock price movements can be framed as a classification task, where the goal is to anticipate whether a stock will increase, decrease, or remain stable. Most existing approaches rely solely on the movement patterns of individual stocks or stock pairs, overlooking the more complex, higher-order connections that exist among groups of stocks. In practice, stocks are often interrelated in higher orders, for example, by belonging to the same industry sector or being jointly held within the same investment fund. To address this, we compare 4 hypergraph neural network-based approaches to make spatio-temporal predictions for stock movement prediction, which explicitly leverages these higher-order dependencies. We use two heterogeneous hypergraphs, where one hypergraph represents sector-based associations and the other one represents fund-holding relationships among stocks. In general, we found the hierarchical hypergraph attention mechanism and temporal attention to be effective in achieving better performance. A hierarchical hypergraph attention mechanism models these relationships by weighting the contributions of stock nodes, hyperedges, and even the hypergraphs themselves. Temporal attention captures time-dependent dynamics of both stock and sector sequences, effectively accounting for the influence of past states. Experiments on real-world datasets demonstrate that the methods specializing in hypergraph integration achieve superior performance compared to existing methods, both in terms of predictive accuracy and profitability.