  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 have the same correlations with all other
assets. We devise an algorithm to detect the clusters, with a theoretical
analysis and a practical guidance. Finally, we conduct an empirical analysis to
attest the performance of the algorithm.
