An Application of Correlation Clustering to Portfolio Diversification

TL;DR

This paper applies a phylogenetic network-based correlation clustering method to stock data, demonstrating improved portfolio diversification and risk reduction during market growth compared to traditional methods.

Contribution

It introduces a novel use of correlation clustering via phylogenetic network techniques for portfolio diversification in stock markets.

Findings

Reduced portfolio risk during market increases

Effective visualization of stock correlations

Outperforms random and industry-based selection methods

Abstract

This paper presents a novel application of a clustering algorithm developed for constructing a phylogenetic network to the correlation matrix for 126 stocks listed on the Shanghai A Stock Market. We show that by visualizing the correlation matrix using a Neighbor-Net network and using the circular ordering produced during the construction of the network we can reduce the risk of a diversified portfolio compared with random or industry group based selection methods in times of market increase.