Diversification and portfolio analysis are traditionally framed through covariance, correlation, and factor exposures. While foundational, these tools summarize co-movement without revealing the structure through which information and risk propagate, particularly during periods of market stress. This article reframes diversification as a network problem. By viewing the market as an interconnected system of assets, network-based representations make dependence, hidden concentration, and shock transmission more transparent. Building on this intuition, the authors focus on Graph Neural Networks (GNNs) as a practical framework for learning from market connectivity. Through intuitive examples and portfolio-relevant applications, the article shows how GNNs formalize reasoning that portfolio managers already apply informally, making diversification analysis more transparent, repeatable, and scalable in modern asset management.
Chin et al. (Tue,) studied this question.