A simheuristic algorithm for the portfolio optimization problem with random returns and noisy covariances

[EN] The goal of the portfolio optimization problem is to minimize risk for an expected portfolio return by allocating weights to included assets. As the pool of investable assets grows, and additional constraints are imposed, the problem becomes NP-hard. Thus, metaheuristics are commonly employed f...

ver descrição completa

Detalhes bibliográficos
Autores: Kizys, Renatas, Doering, Jana, Polat, Onur, Calvet, Laura, Panadero, Javier, Juan, Angel A.|||0000-0003-1392-1776
Formato: artículo
Fecha de publicación:2022
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/200493
Acesso em linha:https://riunet.upv.es/handle/10251/200493
Access Level:acceso abierto
Palavra-chave:Constrained portfolio optimization
Metaheuristics
Simulation
Financial assets
Variable neighborhood search
Biased randomization
ESTADISTICA E INVESTIGACION OPERATIVA
id ES_cc4a65fe48cdbf64d2549ee4fd340ccb
oai_identifier_str oai:riunet.upv.es:10251/200493
network_acronym_str ES
network_name_str España
repository_id_str
spelling A simheuristic algorithm for the portfolio optimization problem with random returns and noisy covariancesKizys, RenatasDoering, JanaPolat, OnurCalvet, LauraPanadero, JavierJuan, Angel A.|||0000-0003-1392-1776Constrained portfolio optimizationMetaheuristicsSimulationFinancial assetsVariable neighborhood searchBiased randomizationESTADISTICA E INVESTIGACION OPERATIVA[EN] The goal of the portfolio optimization problem is to minimize risk for an expected portfolio return by allocating weights to included assets. As the pool of investable assets grows, and additional constraints are imposed, the problem becomes NP-hard. Thus, metaheuristics are commonly employed for solving large instances of rich versions. However, metaheuristics do not fully account for random returns and noisy covariances, which renders them unrealistic in the presence of heightened uncertainty in financial markets. This paper aims to close this gap by proposing a simulation-optimization approach - specifically, a simheuristic algorithm that integrates a variable neighborhood search metaheuristic with Monte Carlo simulation - to deal with stochastic returns and noisy covariances modeled as random variables. Computational experiments performed on a well-established benchmark instance illustrate the advantages of our methodology and analyze how the solutions change in response to a varying degree of randomness, minimum required return, and probability of obtaining a return exceeding an investor-defined threshold.This work has been partially funded by the Erasmus+ SEPIE program, Spain (2019-I-ES01-KA103-062602).ElsevierDepartamento de Estadística e Investigación Operativa Aplicadas y CalidadCentro de Investigación en Gestión e Ingeniería de ProducciónEscuela Politécnica Superior de AlcoyEuropean CommissionRepositorio Institucional de la Universitat Politècnica de València Riunet20222022-03-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/200493reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengErasmus+ Erasmus+ 2019-1-ES01-KA103-062602 Higher education student and staff mobility projectopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2004932026-06-13T07:49:27Z
dc.title.none.fl_str_mv A simheuristic algorithm for the portfolio optimization problem with random returns and noisy covariances
title A simheuristic algorithm for the portfolio optimization problem with random returns and noisy covariances
spellingShingle A simheuristic algorithm for the portfolio optimization problem with random returns and noisy covariances
Kizys, Renatas
Constrained portfolio optimization
Metaheuristics
Simulation
Financial assets
Variable neighborhood search
Biased randomization
ESTADISTICA E INVESTIGACION OPERATIVA
title_short A simheuristic algorithm for the portfolio optimization problem with random returns and noisy covariances
title_full A simheuristic algorithm for the portfolio optimization problem with random returns and noisy covariances
title_fullStr A simheuristic algorithm for the portfolio optimization problem with random returns and noisy covariances
title_full_unstemmed A simheuristic algorithm for the portfolio optimization problem with random returns and noisy covariances
title_sort A simheuristic algorithm for the portfolio optimization problem with random returns and noisy covariances
dc.creator.none.fl_str_mv Kizys, Renatas
Doering, Jana
Polat, Onur
Calvet, Laura
Panadero, Javier
Juan, Angel A.|||0000-0003-1392-1776
author Kizys, Renatas
author_facet Kizys, Renatas
Doering, Jana
Polat, Onur
Calvet, Laura
Panadero, Javier
Juan, Angel A.|||0000-0003-1392-1776
author_role author
author2 Doering, Jana
Polat, Onur
Calvet, Laura
Panadero, Javier
Juan, Angel A.|||0000-0003-1392-1776
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Estadística e Investigación Operativa Aplicadas y Calidad
Centro de Investigación en Gestión e Ingeniería de Producción
Escuela Politécnica Superior de Alcoy
European Commission
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Constrained portfolio optimization
Metaheuristics
Simulation
Financial assets
Variable neighborhood search
Biased randomization
ESTADISTICA E INVESTIGACION OPERATIVA
topic Constrained portfolio optimization
Metaheuristics
Simulation
Financial assets
Variable neighborhood search
Biased randomization
ESTADISTICA E INVESTIGACION OPERATIVA
description [EN] The goal of the portfolio optimization problem is to minimize risk for an expected portfolio return by allocating weights to included assets. As the pool of investable assets grows, and additional constraints are imposed, the problem becomes NP-hard. Thus, metaheuristics are commonly employed for solving large instances of rich versions. However, metaheuristics do not fully account for random returns and noisy covariances, which renders them unrealistic in the presence of heightened uncertainty in financial markets. This paper aims to close this gap by proposing a simulation-optimization approach - specifically, a simheuristic algorithm that integrates a variable neighborhood search metaheuristic with Monte Carlo simulation - to deal with stochastic returns and noisy covariances modeled as random variables. Computational experiments performed on a well-established benchmark instance illustrate the advantages of our methodology and analyze how the solutions change in response to a varying degree of randomness, minimum required return, and probability of obtaining a return exceeding an investor-defined threshold.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-03-01
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/10251/200493
url https://riunet.upv.es/handle/10251/200493
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Erasmus+ Erasmus+ 2019-1-ES01-KA103-062602 Higher education student and staff mobility project
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/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 - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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_ 1869419685000249344
score 15,300719

Registos relacionados