Abstract
We apply recent innovations in network science to analyze how correlations of stock returns evolve over time. To illustrate these techniques we study the returns of 30 industry stock portfolios from 1927 to 2014. We calculate Pearson correlation matrices for each year, and apply multilayer network tools to these correlation matrices to uncover mesoscale architecture in the form of communities. These communities are easily interpretable as groups of industries with highly correlated stock returns. We observe that the flexibility, or the likelihood of industries to switch communities, exhibits a statistically significant increase after 1970, and that the communities evolve in ways consistent with changes in the structure of the U.S. economy. We find that these patterns are not explained by changes in average pairwise correlations or industry market betas. These results therefore underscore the potential for using multilayer network tools to study time-varying correlations of financial assets.
Highlights
Correlations of asset returns are central to our understanding of financial markets
Over the course of 88 years from July 1, 1926 to December 31, 2014, we demonstrate that 30 common industries change in their patterns of return correlation with one another
We study these changes using an ordered multilayer network representation of the data, and apply a dynamic community detection technique to reveal clusters of industries that change in their composition over time
Summary
Correlations of asset returns are central to our understanding of financial markets. the high dimensionality and time-varying nature of correlation matrices poses challenges for both estimation and interpretation. Dynamic Conditional Correlation (DCC) models (Engle et al 1992) allow for estimation of changing correlation matrices, and principal components analysis reduces the dimensionality of return correlation matrices (Connor and Korajczyk 1993) Neither of these technique yields results that allow for an intuitive understanding of which assets share a high degree of correlation at a given point in time. Tools from network science (2019) 4:31 can be used to assess so-called mesoscale architecture in the correlation matrix (Tilak et al 2011; Pollet and Wilson 2010; Yang et al 2014; Coats and Fant 1993) These mesoscale techniques allow for the representation of a high-dimensional structure which falls between the microscale, which in our context would be the full correlation matrix, and the macroscale, such as a single number representing the average correlation across the entire matrix. The mesoscale network structure of a correlation matrix can be characterized by a variety of features, with the most common and well-studied being the presence of communities or modules, which can be thought of as clusters of highly correlated assets
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