  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).
