Hierarchical Graph Learning for Stock Market Prediction via a Domain-Aware Graph Pooling Operator

The utility of Graph Neural Networks (GNN) for the paradigm of forecasting short-term stock price movements is investigated. In particular, a finance-specific graph pooling operation, referred to as StockPool, is introduced to efficiently coarsen the stock graph. This is achieved by employing domain knowledge to cluster stocks, depending on some task-specific characteristics (e.g. industries, sub-industries, etc.). Unlike fully end-to-end learnable graph pooling strategies (e.g. differentiable pooling, MinCUT pooling, etc.), such a deterministic pooling operator is considerably more computationally efficient and thus scalable to larger stock graphs. Experimentations on the S&P500 stock index demonstrate that the StockPool operator outperforms existing graph pooling strategies on the prediction of price movements. Finally, different graph pooling methods are utilized to create a set of highly uncorrelated GNN models; these are used to construct a graph ensemble model with an improved performance.