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.
@article{arxiv.1511.07945,
title = {An Application of Correlation Clustering to Portfolio Diversification},
author = {Hannah Cheng Juan Zhan and William Rea and Alethea Rea},
journal= {arXiv preprint arXiv:1511.07945},
year = {2015}
}