Asset Selection via Correlation Blockmodel Clustering

TL;DR

This paper introduces a correlation blockmodel clustering method to identify representative stocks for diversification, combining theoretical analysis with empirical validation.

Contribution

It develops a novel data-driven clustering algorithm based on correlation structures, with theoretical guarantees and practical effectiveness demonstrated empirically.

Findings

The algorithm successfully identifies meaningful asset clusters.

Clustering improves diversification approximation.

Theoretical analysis supports the method's consistency.

Abstract

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.