A combinatorial optimization approach to scenario filtering in portfolio selection

TL;DR

This paper introduces a novel combinatorial optimization method for filtering noise in covariance matrices used in portfolio selection, outperforming existing strategies like Random Matrix Theory and Power Mapping.

Contribution

It proposes a new Mixed Integer Quadratic Programming model for better noise filtering in portfolio optimization, with an efficient heuristic for large datasets.

Findings

The new method outperforms existing filtering strategies in out-of-sample tests.

The heuristic procedure is both efficient and effective for large datasets.

The approach improves portfolio performance by reducing noise influence.

Abstract

Recent studies stressed the fact that covariance matrices computed from empirical financial time series appear to contain a high amount of noise. This makes the classical Markowitz Mean-Variance Optimization model unable to correctly evaluate the performance associated to selected portfolios. Since the Markowitz model is still one of the most used practitioner-oriented tool, several filtering methods have been proposed in the literature to fix the problem. Among them, the two most promising ones refer to the Random Matrix Theory or to the Power Mapping strategy. The basic idea of these methods is to transform the correlation matrix maintaining the Mean-Variance Optimization model. However, experimental analysis shows that these two strategies are not adequately effective when applied to real financial datasets. In this paper we propose an alternative filtering method based on Combinatorial Optimization. We advance a new Mixed Integer Quadratic Programming model to filter those observations that may influence the performance of a portfolio in the future. We discuss the properties of this new model and we test it on some real financial datasets. We compare the out-of-sample performance of our portfolios with the one of the portfolios provided by the two above mentioned alternative strategies. We show that our method outperforms them. Although our model can be solved efficiently with standard optimization solvers the computational burden increases for large datasets. To overcome this issue we also propose a heuristic procedure that empirically showed to be both efficient and effective.