Tri-Criterion Model for Constructing Low-Carbon Mutual Fund Portfolios: A Preference-Based Multi-Objective Genetic Algorithm Approach

Adolfo Hilario-Caballero, Ana Garcia-Bernabeu, Jose Vicente Salcedo and Marisa Vercher
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Adolfo Hilario-Caballero: Institute of Control Systems and Industrial Computing (ai2), Universitat Politècnica de València, 46022 Valencia, Spain
Ana Garcia-Bernabeu: Campus of Alcoi, Universitat Politècnica de València, 03801 Alcoi, Spain
Jose Vicente Salcedo: Institute of Control Systems and Industrial Computing (ai2), Universitat Politècnica de València, 46022 Valencia, Spain
Marisa Vercher: Campus of Alcoi, Universitat Politècnica de València, 03801 Alcoi, Spain

IJERPH, 2020, vol. 17, issue 17, 1-15

Abstract: Sustainable finance, which integrates environmental, social and governance criteria on financial decisions rests on the fact that money should be used for good purposes. Thus, the financial sector is also expected to play a more important role to decarbonise the global economy. To align financial flows with a pathway towards a low-carbon economy, investors should be able to integrate into their financial decisions additional criteria beyond return and risk to manage climate risk. We propose a tri-criterion portfolio selection model to extend the classical Markowitz’s mean-variance approach to include investor’s preferences on the portfolio carbon risk exposure as an additional criterion. To approximate the 3D Pareto front we apply an efficient multi-objective genetic algorithm called ev-MOGA which is based on the concept of ε -dominance. Furthermore, we introduce a-posteriori approach to incorporate the investor’s preferences into the solution process regarding their climate-change related preferences measured by the carbon risk exposure and their loss-adverse attitude. We test the performance of the proposed algorithm in a cross-section of European socially responsible investments open-end funds to assess the extent to which climate-related risk could be embedded in the portfolio according to the investor’s preferences.

Keywords: genetic algorithms; low-carbon economy; multi-objective optimization; sustainable finance; investor’s preferences (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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