Graph Neural Networks in Asset and Investment Management: An Overview for Practitioners

This article provides an educational overview of graph neural networks (GNNs) and their applications to asset and investment management. Financial markets are naturally relational. Companies and securities are connected through supply chains, sectors, ownership, financing channels, common investors, derivatives exposures, and information flows. GNNs provide a framework for incorporating these relationships into forecasts, risk models, and portfolio decisions. We introduce the basic ideas behind GNNs, explain where financial graphs come from, and review applications in return forecasting, stock selection, factor modeling, portfolio construction, risk management, derivatives, and alternative data. The discussion emphasizes that graph construction is often more important than the specific neural-network architecture. Practical adoption requires careful graph construction, point-in-time data, strong baselines, controls for leakage and turnover, and governance standards that make the learned relationships interpretable and robust. We conclude with practitioner takeaways and an outlook on future directions, including dynamic financial graphs, hypergraphs, knowledge graphs, and hybrid models. Throughout this study, we emphasize that GNNs are most useful when they complement, rather than replace, established factor models, risk models, and portfolio optimizers.