2014/02/19 by Gordon J. Ross · 17 citations
Economics, Econometrics and Finance · Physics and Astronomy · #Artificial intelligence #Business #Cluster analysis #Complex Network Analysis Techniques #Complex Systems and Time Series Analysis #Computer science #Econometrics #Economic geography #Economics #Finance #Financial Risk and Volatility Modeling #Financial crisis #Financial economics #Financial sector #Geography #Macroeconomics #Portfolio #Stock (firearms) #q-fin.ST
paper · pdf · doi:10.1103/physreve.89.022809
published in Physical Review E 89(2), 022809 (American Physical Society) · 9 pages
openalex publication_date 2014/02/19 · arxiv created 2015/05/07 · arxiv updated 2015/05/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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 structure.