We compare three network portfolio selection methods; hierarchical clustering trees, minimum spanning trees and neighbor-Nets, with random and industry group selection methods on twelve years of data from the 30 Dow Jones Industrial Average stocks from 2001 to 2013 for very small private...

We show that the last few components in principal component analysis of the correlation matrix of a group of stocks may contain useful financial information by identifying highly correlated pairs or larger groups of stocks. The results of this type of analysis can easily be included in the...

We propose a stock selection method that is based on a variable selection method used with principal component analysis. We applied our method to stocks in the ASX200 and show that a portfolio of as little as 15 stocks can closely replicate the behaviour of the index. We show that the number of...

This paper applies graphical modelling to the S&P 500, Nikkei 225 and FTSE 100 stock market indices to trace the spillover of returns and volatility between these three major world stock market indices before, during and after the 2008 financial crisis. We find that the depth of market...

This paper presents a novel application of software 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...

We present four methods of assessing the diversification potential within a stock market, two of these are based on principal component analysis. They were applied to the Australian stock exchange for the years 2000 to 2014 and all show a consistent picture. The potential for diversification...