Network Science, Graphical Models and Clustering for Portfolio Managers

Graphical and network-based methods provide portfolio managers with intuitive ways to visualize relationships among assets, identify hidden concentrations, and better understand diversification across multi-asset portfolios. This paper examines several network and clustering approaches applied to a broad investment universe that includes traditional assets, alternative investments, and hedging strategies. Hierarchical clustering and minimum spanning trees provide transparent representations of the market’s dependency structure and highlight how major asset groups and defensive strategies are interconnected. More advanced network approaches reveal direct relationships among assets after accounting for broader market effects and help identify central risk exposures, as well as genuinely diversifying strategies. The paper also compares graph-based clustering methods with more traditional distance-based approaches and shows how different techniques produce economically meaningful groupings of assets and strategies. The results suggest that graph-based approaches, particularly spectral clustering, can provide useful insights for portfolio construction, diversification analysis, and risk monitoring.