Cluster analysis for portfolio optimization

TL;DR

This paper explores how clustering algorithms can enhance the reliability of portfolio optimization by reducing the impact of correlation matrix uncertainty, with validation through bootstrap analysis across various asset and horizon sizes.

Contribution

It introduces clustering-based filtering methods to improve portfolio risk prediction accuracy, demonstrating robustness across different parameters and investment horizons.

Findings

Clustering algorithms improve the ratio of predicted to realized risk.

Bootstrap analysis confirms robustness across asset numbers and horizons.

Enhanced portfolio stability with clustering methods.

Abstract

We consider the problem of the statistical uncertainty of the correlation matrix in the optimization of a financial portfolio. We show that the use of clustering algorithms can improve the reliability of the portfolio in terms of the ratio between predicted and realized risk. Bootstrap analysis indicates that this improvement is obtained in a wide range of the parameters N (number of assets) and T (investment horizon). The predicted and realized risk level and the relative portfolio composition of the selected portfolio for a given value of the portfolio return are also investigated for each considered filtering method.