Portfolio implementation risk management using evolutionary multiobjective optimization
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/53022 |
Resumo: | Portfoliomanagementbasedonmean-varianceportfoliooptimizationissubjecttodifferent sources of uncertainty. In addition to those related to the quality of parameter estimates used in the optimization process, investors face a portfolio implementation risk. The potential temporary discrepancybetweentargetandpresentportfolios,causedbytradingstrategies,mayexposeinvestors to undesired risks. This study proposes an evolutionary multiobjective optimization algorithm aiming at regions with solutions more tolerant to these deviations and, therefore, more reliable. The proposed approach incorporates a user’s preference and seeks a fine-grained approximation of the most relevant efficient region. The computational experiments performed in this study are based on a cardinality-constrained problem with investment limits for eight broad-category indexes and 15 years of data. The obtained results show the ability of the proposed approach to address the robustness issue and to support decision making by providing a preferred part of the efficient set. The results reveal that the obtained solutions also exhibit a higher tolerance to prediction errors in asset returns and variance–covariance matrix. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Portfolio implementation risk management using evolutionary multiobjective optimizationrobustnessmulti-objective optimizationevolutionary computationportfolio optimizationCiências Naturais::Ciências da Computação e da InformaçãoScience & TechnologyPortfoliomanagementbasedonmean-varianceportfoliooptimizationissubjecttodifferent sources of uncertainty. In addition to those related to the quality of parameter estimates used in the optimization process, investors face a portfolio implementation risk. The potential temporary discrepancybetweentargetandpresentportfolios,causedbytradingstrategies,mayexposeinvestors to undesired risks. This study proposes an evolutionary multiobjective optimization algorithm aiming at regions with solutions more tolerant to these deviations and, therefore, more reliable. The proposed approach incorporates a user’s preference and seeks a fine-grained approximation of the most relevant efficient region. The computational experiments performed in this study are based on a cardinality-constrained problem with investment limits for eight broad-category indexes and 15 years of data. The obtained results show the ability of the proposed approach to address the robustness issue and to support decision making by providing a preferred part of the efficient set. The results reveal that the obtained solutions also exhibit a higher tolerance to prediction errors in asset returns and variance–covariance matrix.Sandra Garcia-Rodriguez and David Quintana acknowledge financial support granted by the Spanish Ministry of Economy and Competitivity under grant ENE2014-56126-C2-2-R. Roman Denysiuk and Antonio Gaspar-Cunha were supported by the Portuguese Foundation for Science and Technology under grant PEst-C/CTM/LA0025/2013 (Projecto Estratégico-LA 25-2013-2014-Strategic Project-LA 25-2013-2014).info:eu-repo/semantics/publishedVersionMDPI AGUniversidade do MinhoQuintana, DavidDenysiuk, RomanGarcia-Rodriguez, SandraGaspar-Cunha, A.20172017-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/53022eng2076-341710.3390/app7101079info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:45:37Zoai:repositorium.sdum.uminho.pt:1822/53022Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:43:29.036952Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Portfolio implementation risk management using evolutionary multiobjective optimization |
title |
Portfolio implementation risk management using evolutionary multiobjective optimization |
spellingShingle |
Portfolio implementation risk management using evolutionary multiobjective optimization Quintana, David robustness multi-objective optimization evolutionary computation portfolio optimization Ciências Naturais::Ciências da Computação e da Informação Science & Technology |
title_short |
Portfolio implementation risk management using evolutionary multiobjective optimization |
title_full |
Portfolio implementation risk management using evolutionary multiobjective optimization |
title_fullStr |
Portfolio implementation risk management using evolutionary multiobjective optimization |
title_full_unstemmed |
Portfolio implementation risk management using evolutionary multiobjective optimization |
title_sort |
Portfolio implementation risk management using evolutionary multiobjective optimization |
author |
Quintana, David |
author_facet |
Quintana, David Denysiuk, Roman Garcia-Rodriguez, Sandra Gaspar-Cunha, A. |
author_role |
author |
author2 |
Denysiuk, Roman Garcia-Rodriguez, Sandra Gaspar-Cunha, A. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Quintana, David Denysiuk, Roman Garcia-Rodriguez, Sandra Gaspar-Cunha, A. |
dc.subject.por.fl_str_mv |
robustness multi-objective optimization evolutionary computation portfolio optimization Ciências Naturais::Ciências da Computação e da Informação Science & Technology |
topic |
robustness multi-objective optimization evolutionary computation portfolio optimization Ciências Naturais::Ciências da Computação e da Informação Science & Technology |
description |
Portfoliomanagementbasedonmean-varianceportfoliooptimizationissubjecttodifferent sources of uncertainty. In addition to those related to the quality of parameter estimates used in the optimization process, investors face a portfolio implementation risk. The potential temporary discrepancybetweentargetandpresentportfolios,causedbytradingstrategies,mayexposeinvestors to undesired risks. This study proposes an evolutionary multiobjective optimization algorithm aiming at regions with solutions more tolerant to these deviations and, therefore, more reliable. The proposed approach incorporates a user’s preference and seeks a fine-grained approximation of the most relevant efficient region. The computational experiments performed in this study are based on a cardinality-constrained problem with investment limits for eight broad-category indexes and 15 years of data. The obtained results show the ability of the proposed approach to address the robustness issue and to support decision making by providing a preferred part of the efficient set. The results reveal that the obtained solutions also exhibit a higher tolerance to prediction errors in asset returns and variance–covariance matrix. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017 2017-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/1822/53022 |
url |
http://hdl.handle.net/1822/53022 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2076-3417 10.3390/app7101079 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
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:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799132992142049280 |