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

[EN] 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 financi...

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Autores: Hilario Caballero, Adolfo|||0000-0002-3237-8652, Garcia-Bernabeu, Ana|||0000-0003-3181-7745, Salcedo-Romero-de-Ávila, José-Vicente|||0000-0003-1577-5039, Vercher Ferrandiz, Mª Luisa|||0000-0002-1822-5158
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/161873
Acceso en línea:https://riunet.upv.es/handle/10251/161873
Access Level:acceso abierto
Palabra clave:Genetic algorithms
Low-carbon economy
Multi-objective optimization
Sustainable finance
Investor&apos
s preferences
INGENIERIA DE SISTEMAS Y AUTOMATICA
ECONOMIA APLICADA
08.- Fomentar el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo, y el trabajo decente para todos
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repository_id_str
spelling Tri-Criterion Model for Constructing Low-Carbon Mutual Fund Portfolios: A Preference-Based Multi-Objective Genetic Algorithm ApproachHilario Caballero, Adolfo|||0000-0002-3237-8652Garcia-Bernabeu, Ana|||0000-0003-3181-7745Salcedo-Romero-de-Ávila, José-Vicente|||0000-0003-1577-5039Vercher Ferrandiz, Mª Luisa|||0000-0002-1822-5158Genetic algorithmsLow-carbon economyMulti-objective optimizationSustainable financeInvestor&aposs preferencesINGENIERIA DE SISTEMAS Y AUTOMATICAECONOMIA APLICADA08.- Fomentar el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo, y el trabajo decente para todos[EN] 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 epsilon-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.MDPI AGDepartamento de Ingeniería de Sistemas y AutomáticaDepartamento de Economía y Ciencias SocialesInstituto Universitario de Automática e Informática IndustrialEscuela Técnica Superior de Ingeniería IndustrialEscuela Politécnica Superior de AlcoyGrupo de Investigación de Economía Internacional y DesarrolloRepositorio Institucional de la Universitat Politècnica de València Riunet20202020-09-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/161873reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1618732026-06-13T07:49:27Z
dc.title.none.fl_str_mv Tri-Criterion Model for Constructing Low-Carbon Mutual Fund Portfolios: A Preference-Based Multi-Objective Genetic Algorithm Approach
title Tri-Criterion Model for Constructing Low-Carbon Mutual Fund Portfolios: A Preference-Based Multi-Objective Genetic Algorithm Approach
spellingShingle Tri-Criterion Model for Constructing Low-Carbon Mutual Fund Portfolios: A Preference-Based Multi-Objective Genetic Algorithm Approach
Hilario Caballero, Adolfo|||0000-0002-3237-8652
Genetic algorithms
Low-carbon economy
Multi-objective optimization
Sustainable finance
Investor&apos
s preferences
INGENIERIA DE SISTEMAS Y AUTOMATICA
ECONOMIA APLICADA
08.- Fomentar el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo, y el trabajo decente para todos
title_short Tri-Criterion Model for Constructing Low-Carbon Mutual Fund Portfolios: A Preference-Based Multi-Objective Genetic Algorithm Approach
title_full Tri-Criterion Model for Constructing Low-Carbon Mutual Fund Portfolios: A Preference-Based Multi-Objective Genetic Algorithm Approach
title_fullStr Tri-Criterion Model for Constructing Low-Carbon Mutual Fund Portfolios: A Preference-Based Multi-Objective Genetic Algorithm Approach
title_full_unstemmed Tri-Criterion Model for Constructing Low-Carbon Mutual Fund Portfolios: A Preference-Based Multi-Objective Genetic Algorithm Approach
title_sort Tri-Criterion Model for Constructing Low-Carbon Mutual Fund Portfolios: A Preference-Based Multi-Objective Genetic Algorithm Approach
dc.creator.none.fl_str_mv Hilario Caballero, Adolfo|||0000-0002-3237-8652
Garcia-Bernabeu, Ana|||0000-0003-3181-7745
Salcedo-Romero-de-Ávila, José-Vicente|||0000-0003-1577-5039
Vercher Ferrandiz, Mª Luisa|||0000-0002-1822-5158
author Hilario Caballero, Adolfo|||0000-0002-3237-8652
author_facet Hilario Caballero, Adolfo|||0000-0002-3237-8652
Garcia-Bernabeu, Ana|||0000-0003-3181-7745
Salcedo-Romero-de-Ávila, José-Vicente|||0000-0003-1577-5039
Vercher Ferrandiz, Mª Luisa|||0000-0002-1822-5158
author_role author
author2 Garcia-Bernabeu, Ana|||0000-0003-3181-7745
Salcedo-Romero-de-Ávila, José-Vicente|||0000-0003-1577-5039
Vercher Ferrandiz, Mª Luisa|||0000-0002-1822-5158
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería de Sistemas y Automática
Departamento de Economía y Ciencias Sociales
Instituto Universitario de Automática e Informática Industrial
Escuela Técnica Superior de Ingeniería Industrial
Escuela Politécnica Superior de Alcoy
Grupo de Investigación de Economía Internacional y Desarrollo
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Genetic algorithms
Low-carbon economy
Multi-objective optimization
Sustainable finance
Investor&apos
s preferences
INGENIERIA DE SISTEMAS Y AUTOMATICA
ECONOMIA APLICADA
08.- Fomentar el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo, y el trabajo decente para todos
topic Genetic algorithms
Low-carbon economy
Multi-objective optimization
Sustainable finance
Investor&apos
s preferences
INGENIERIA DE SISTEMAS Y AUTOMATICA
ECONOMIA APLICADA
08.- Fomentar el crecimiento económico sostenido, inclusivo y sostenible, el empleo pleno y productivo, y el trabajo decente para todos
description [EN] 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 epsilon-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.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-09-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/161873
url https://riunet.upv.es/handle/10251/161873
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869403480714641408
score 15.300719

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