PGP for portfolio optimization

application to ESG index family

Journal article (2023)

Authors

Ilyes Abid ISC Paris, Paris
Christian Urom Paris School of Business
Jonathan Peillex ICD business School, Paris
Majdi Karmani Excelia Business School, La Rochelle
G.O. Ndubuisi Economics of Technology and Innovation - TPM
Research Group
Economics of Technology and Innovation (TPM) (TU Delft)
Copyright: © 2023 Ilyes Abid, Christian Urom, Jonathan Peillex, Majdi Karmani, G.O. Ndubuisi
DOI
help_outline
https://doi.org/10.1007/s10479-023-05460-w
Kurtosis Skewness High-order portfolios PGP method
More Info
expand_more

Published Date

2023

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Faculty

Technology, Policy and Management

Department

Values Technology and Innovation

Research Group

Economics of Technology and Innovation

Abstract

The conventional portfolio design approach assumes Gaussian return distributions, but this is not accurate in practice. Asymmetric and heavy-tailed return distributions necessitate consideration of higher-order moments such as skewness and kurtosis, in addition to mean and variance. This study proposes a multi-objective approach using a mean-variance-skewness-kurtosis model to construct a diversified portfolio. A parametrized polynomial goal programming (PGP) method is used to optimize the portfolio by maximizing returns and skewness while minimizing variance and kurtosis. Empirical data from the S &P ESG index family is used, and PGP generates multiple portfolios reflecting investors’ preferences for the four moments. To compare between the obtained portfolios, we represent the empirical cumulative distribution of the portfolio returns for all studied values of weights and show how this can be used to assist the inverstor in selecting the best set of weights.