A dominance maximization approach to portfolio selection

TL;DR

This paper introduces a novel portfolio selection method that maximizes the product of distances from a reference point, offering a scale-invariant and efficient way to identify dominating portfolios in multiobjective optimization.

Contribution

The paper presents a new dominance maximization approach for portfolio selection that is scale-invariant, efficiently computable, and provides portfolios that dominate others relative to a reference point.

Findings

Method is scale-invariant and robust to objective scaling.

Efficient computation of the global optimal portfolio.

Numerical tests demonstrate the approach's effectiveness.

Abstract

In the portfolio multiobjective optimization framework, we propose to compare and choose, among all feasible asset portfolios of a given market, the one that maximizes the product of the distances between its values of risk and gain and those of a suitable reference point (e.g., the so-called nadir). We show that this approach has distinctive and remarkable features. While being not influenced by how the objectives are scaled, it provides one with an efficient (Pareto) portfolio that "dominates the most" with respect to the reference point. Furthermore, although our no-preference strategy generally requires the solution of a nonconvex (constrained) single-objective problem, we show how the resulting (global) optimal portfolio can be easily and efficiently computed. We also perform numerical tests based on some publicly available benchmark data sets often used in the literature, highlighting the nice properties of our approach.