Portfolio selection: A target-distribution approach

Nathan Lassance and Frédéric Vrins

European Journal of Operational Research, 2023, vol. 310, issue 1, 302-314

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 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Portfolio selection: A target-distribution approach (2023)
Working Paper: Portfolio Selection: A Target-Distribution Approach (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:310:y:2023:i:1:p:302-314

DOI: 10.1016/j.ejor.2023.02.014

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