Asset Selection via Correlation Blockmodel Clustering

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 datadriven 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.

Correlation Matrix Clustering for Statistical Arbitrage Portfolios

“…Other lines of work that follow this second approach employ various clustering methods and selection mechanisms to identify the representative stock in each cluster. For example, Wang et al (2022) selects the stock with the lowest volatility within each cluster, and Tang et al (2021) selects the stock with the highest Sharpe ratio in each cluster.…”