Portfolio selection: A target-distribution approach

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 portfo-lio, 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 indi-cates that the proposed framework delivers a satisfying compromise between mean-variance efficiency and improved higher moments. (c) 2023 Elsevier B.V. All rights reserved.

Automated summary

We present a new framework for portfolio selection that allows investors to target a specific return distribution, with the objective of designing an optimal portfolio that closely matches the target distribution. This framework is applicable to various investment objectives. Here, we focus on improving the higher moments of mean-variance-efficient portfolios by designing the target distribution to have desirable higher moments while matching the first two moments of the efficient portfolio. Theoretical analysis shows that the optimal portfolio, in general, differs from the mean-variance portfolio, but remains mean-variance efficient when asset returns follow a Gaussian distribution. Otherwise, it may deviate from the efficient frontier to better match the higher moments of the target distribution. Extensive empirical analysis using different datasets demonstrates that the proposed framework achieves a satisfactory compromise between mean-variance efficiency and improved higher moments.