Dynamic Multi-Factor Clustering of Financial Networks

TL;DR

This paper explores how financial instruments form complex clusters influenced by sector and geography, using robust regression to analyze correlation structures and revealing shifts in clustering behavior around the 2008 financial crisis.

Contribution

It introduces a novel multi-factor clustering approach that accounts for sector and geographic influences, surpassing standard community detection methods.

Findings

Pre-crisis, geography had little impact on clustering.

Post-crisis, geography became a key clustering factor.

Robust regression effectively isolates sector and geographic effects.

Abstract

We investigate the tendency for financial instruments to form clusters when there are multiple factors influencing the correlation structure. Specifically, we consider a stock portfolio which contains companies from different industrial sectors, located in several different countries. Both sector membership and geography combine to create a complex clustering structure where companies seem to first be divided based on sector, with geographical subclusters emerging within each industrial sector. We argue that standard techniques for detecting overlapping clusters and communities are not able to capture this type of structure, and show how robust regression techniques can instead be used to remove the influence of both sector and geography from the correlation matrix separately. Our analysis reveals that prior to the 2008 financial crisis, companies did not tend to form clusters based on geography. This changed immediately following the crisis, with geography becoming a more important determinant of clustering.