Portfolio Selection: A Target-Distribution Approach

European Journal of Operational Research, 310(1), 302-314

63 Pages Posted: 28 Jul 2021 Last revised: 25 Nov 2024

Date Written: February 13, 2023

Abstract

We introduce a novel framework for the portfolio selection problem in which investors aim to target a return distribution, and the optimal portfolio has a return distribution as close as possible to the targeted one. The proposed framework can be applied to a variety of investment objectives. In this paper, we focus on improving the higher moments of mean-variance-efficient portfolios by designing the target so that its first two moments match those of the chosen efficient portfolio but has more desirable higher moments. We show theoretically that the optimal portfolio is in general different from the mean-variance portfolio, but remains mean-variance efficient when asset returns are Gaussian. Otherwise, it can move away from the efficient frontier to better match the higher moments of the target distribution. An extensive empirical analysis using three characteristic-sorted datasets and a dataset of 100 individual stocks indicates that the proposed framework delivers a satisfying compromise between mean-variance efficiency and improved higher moments.

Keywords: portfolio optimization, higher moments, downside risk, Kullback-Leibler divergence

JEL Classification: G11, G12

Suggested Citation

Lassance, Nathan and Vrins, Frederic Daniel, Portfolio Selection: A Target-Distribution Approach (February 13, 2023). European Journal of Operational Research, 310(1), 302-314, Available at SSRN: https://ssrn.com/abstract=3893870 or http://dx.doi.org/10.2139/ssrn.3893870

Nathan Lassance (Contact Author)

LFIN/LIDAM, UCLouvain ( email )

151 Chaussée de Binche
Mons, 7000
Belgium

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