Back to Search Start Over

An analysis of conditional mean-variance portfolio performance using hierarchical clustering.

Authors :
Owen, Stephen R.
Source :
Journal of Finance & Data Science; Nov2023, Vol. 9, p1-13, 13p
Publication Year :
2023

Abstract

This paper studies portfolio optimization through improvements of ex-ante conditional covariance estimates. We use the cross-section of stock returns over a 52-year sample to analyze trading performance by implementing the machine learning algorithm of hierarchical clustering. We find that higher out-of-sample risk-adjusted returns are achieved relative to the traditional Markowitz portfolio through hierarchical clustering using a 3-month buy-and-hold, long-only strategy. Additionally, the average change in portfolio weights at each rebalancing period is significantly lower for the portfolio formed using machine learning relative to Markowitz, decreasing investor trading costs. The results are robust to various settings and subsamples. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
24059188
Volume :
9
Database :
Complementary Index
Journal :
Journal of Finance & Data Science
Publication Type :
Academic Journal
Accession number :
178550451
Full Text :
https://doi.org/10.1016/j.jfds.2023.100112