Evolution of correlation structure of industrial indices of US equity markets

TL;DR

This paper analyzes the evolving correlation structure of US industry indices over 40 years, revealing both slow and fast dynamics influenced by market events, with implications for diversification strategies.

Contribution

It introduces a detailed analysis of correlation dynamics across multiple time scales, highlighting the impact of market events on correlation eigenvalues over decades.

Findings

Correlation exhibits both fast and slow dynamics.

Fast variations in eigenvalues align with market crises.

Slow dynamics suggest long-term diversification potential.

Abstract

We investigate the dynamics of correlations present between pairs of industry indices of US stocks traded in US markets by studying correlation based networks and spectral properties of the correlation matrix. The study is performed by using 49 industry index time series computed by K. French and E. Fama during the time period from July 1969 to December 2011 that is spanning more than 40 years. We show that the correlation between industry indices presents both a fast and a slow dynamics. The slow dynamics has a time scale longer than five years showing that a different degree of diversification of the investment is possible in different periods of time. On top to this slow dynamics, we also detect a fast dynamics associated with exogenous or endogenous events. The fast time scale we use is a monthly time scale and the evaluation time period is a 3 month time period. By investigating the correlation dynamics monthly, we are able to detect two examples of fast variations in the first and second eigenvalue of the correlation matrix. The first occurs during the dot-com bubble (from March 1999 to April 2001) and the second occurs during the period of highest impact of the subprime crisis (from August 2008 to August 2009).