This chapter demonstrates how network theory, long established in data science, can be applied to investment problems in ways that reveal connections and risks missed by traditional models. Classical measures such as clustering and centrality remain central, while modern data techniques, including machine learning, extend the analysis to larger and more dynamic settings.
For practitioners, the takeaway is practical: Conventional models still matter, but today’s interconnected markets call for a network perspective that can capture systemic risk, contagion, diversification, and forecasting. Network analysis, supported by modern data techniques, provides a clearer framework for managing complexity and uncertainty in investment practice.
This summary is based on the CFA Institute Research Foundation and CFA Institute Research and Policy Center chapter “Unsupervised Learning II: Network Theory,” by Gueorgui S. Konstantinov, PhD, and Agathe Sadeghi, PhD, which demonstrates how network theory, extended with modern data methods, can be applied to practical investment problems.