Identifying Highly Correlated Stocks Using the Last Few Principal Components
Portfolio Management
2015-12-14 v1 Statistical Finance
Abstract
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 information an investor uses to manage their portfolio.
Keywords
Cite
@article{arxiv.1512.03537,
title = {Identifying Highly Correlated Stocks Using the Last Few Principal Components},
author = {Libin Yang and William Rea and and Alethea Rea},
journal= {arXiv preprint arXiv:1512.03537},
year = {2015}
}
Comments
16 pages, 1 table, 8 figures