Analysis of Optimal Portfolio Management Using Hierarchical Clustering

TL;DR

This paper enhances traditional portfolio optimization by integrating hierarchical clustering and machine learning to improve risk-adjusted returns over the classic Markowitz Model.

Contribution

It introduces a novel hierarchical clustering-based method combined with machine learning to refine portfolio optimization beyond existing models.

Findings

Improved risk-adjusted performance over the Markowitz Model

Effective clustering of assets based on market factors

Enhanced out-of-sample portfolio performance

Abstract

Portfolio optimization is a task that investors use to determine the best allocations for their investments, and fund managers implement computational models to help guide their decisions. While one of the most common portfolio optimization models in the industry is the Markowitz Model, practitioners recognize limitations in its framework that lead to suboptimal out-of-sample performance and unrealistic allocations. In this study, I refine the Markowitz Model by incorporating machine learning to improve portfolio performance. By using a hierarchical clustering-based approach, I am able to enhance portfolio performance on a risk-adjusted basis compared to the Markowitz Model, across various market factors.