FinSIR: Financial SIR-GCN for Market-Aware Stock Recommendation
Abstract
Existing works on stock price prediction have largely treated stocks in a market independently of one another. Nevertheless, recent advances in graph neural networks (GNNs) have enabled the efficient processing of diverse stock relations. This paper introduces the Financial SIR-GCN (FinSIR) for market-aware stock price prediction and recommendation. By modeling stock markets as spatio-temporal graphs, FinSIR addresses the key architectural limitation of existing graph-based models. Notably, the proposed model integrates the soft-isomorphic relational graph convolution network (SIR-GCN) with the "sandwich" structure employed in GNN for time series analysis (GNN4TS) to jointly process the two key dimensions of stock market graphs and to contextualize hidden states with both spatial and temporal stock relations. Backtesting results on the New York Stock Exchange (NYSE) and the National Association of Securities Dealers Automatic Quotation System (NASDAQ) reveal FinSIR consistently achieving up to 65% and 36% larger cumulative investment returns, respectively, compared to baseline models. Additionally, an ablation study further highlights the contribution of each FinSIR module in providing better investment recommendations. Overall, the paper incorporates recent advances in GNN and GNN4TS to provide a new perspective on graph-based solutions for improved stock price prediction and recommendation.
Authors: Brian Godwin Lim, Jiahong Liu, Hans Jarett J. Ong, Jan Adrian Chan, Renzo Roel P. Tan, Irwin King, Kazushi Ikeda