  We aim to cluster financial assets in order to identify a small set of stocks
to approximate the level of diversification of the whole universe of stocks. We
develop a data-driven approach to clustering based on a correlation blockmodel
in which assets in the same cluster are highly correlated with each other and,
at the same time, have the same correlations with all other assets. We devise
an algorithm to detect the clusters, with theoretical analysis and practical
guidance. Finally, we conduct an empirical analysis to verify the performance
of the algorithm.
