A memetic algorithm for cardinality-constrained portfolio optimization with transaction costs

This is the author’s version of a work that was accepted for publication in Applied Soft Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may...

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Detalles Bibliográficos
Autores: Ruiz-Torrubiano, Rubén, Suárez González, Alberto
Tipo de recurso: artículo
Fecha de publicación:2015
País:España
Institución:Universidad Autónoma de Madrid
Repositorio:Biblos-e Archivo. Repositorio Institucional de la UAM
Idioma:inglés
OAI Identifier:oai:repositorio.uam.es:10486/676217
Acceso en línea:http://hdl.handle.net/10486/676217
https://dx.doi.org/10.1016/j.asoc.2015.06.053
Access Level:acceso abierto
Palabra clave:Combinatorial optimization
Genetic algorithms
Portfolio selection
Transaction costs
Informática
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spelling A memetic algorithm for cardinality-constrained portfolio optimization with transaction costsRuiz-Torrubiano, RubénSuárez González, AlbertoCombinatorial optimizationGenetic algorithmsPortfolio selectionTransaction costsInformáticaThis is the author’s version of a work that was accepted for publication in Applied Soft Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Soft Computing, Vol 36 (2015) DOI 10.1016/j.asoc.2015.06.053A memetic approach that combines a genetic algorithm (GA) and quadratic programming is used to address the problem of optimal portfolio selection with cardinality constraints and piecewise linear transaction costs. The framework used is an extension of the standard Markowitz mean–variance model that incorporates realistic constraints, such as upper and lower bounds for investment in individual assets and/or groups of assets, and minimum trading restrictions. The inclusion of constraints that limit the number of assets in the final portfolio and piecewise linear transaction costs transforms the selection of optimal portfolios into a mixed-integer quadratic problem, which cannot be solved by standard optimization techniques. We propose to use a genetic algorithm in which the candidate portfolios are encoded using a set representation to handle the combinatorial aspect of the optimization problem. Besides specifying which assets are included in the portfolio, this representation includes attributes that encode the trading operation (sell/hold/buy) performed when the portfolio is rebalanced. The results of this hybrid method are benchmarked against a range of investment strategies (passive management, the equally weighted portfolio, the minimum variance portfolio, optimal portfolios without cardinality constraints, ignoring transaction costs or obtained with L1 regularization) using publicly available data. The transaction costs and the cardinality constraints provide regularization mechanisms that generally improve the out-of-sample performance of the selected portfolios.Elsevier LtdDepartamento de Ingeniería InformáticaEscuela Politécnica SuperiorAprendizaje Automático (ING EPS-001)20152015-11-01research articlehttp://purl.org/coar/resource_type/c_2df8fbb1AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10486/676217https://dx.doi.org/10.1016/j.asoc.2015.06.053reponame:Biblos-e Archivo. Repositorio Institucional de la UAMinstname:Universidad Autónoma de MadridInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:repositorio.uam.es:10486/6762172026-06-23T12:46:27Z
dc.title.none.fl_str_mv A memetic algorithm for cardinality-constrained portfolio optimization with transaction costs
title A memetic algorithm for cardinality-constrained portfolio optimization with transaction costs
spellingShingle A memetic algorithm for cardinality-constrained portfolio optimization with transaction costs
Ruiz-Torrubiano, Rubén
Combinatorial optimization
Genetic algorithms
Portfolio selection
Transaction costs
Informática
title_short A memetic algorithm for cardinality-constrained portfolio optimization with transaction costs
title_full A memetic algorithm for cardinality-constrained portfolio optimization with transaction costs
title_fullStr A memetic algorithm for cardinality-constrained portfolio optimization with transaction costs
title_full_unstemmed A memetic algorithm for cardinality-constrained portfolio optimization with transaction costs
title_sort A memetic algorithm for cardinality-constrained portfolio optimization with transaction costs
dc.creator.none.fl_str_mv Ruiz-Torrubiano, Rubén
Suárez González, Alberto
author Ruiz-Torrubiano, Rubén
author_facet Ruiz-Torrubiano, Rubén
Suárez González, Alberto
author_role author
author2 Suárez González, Alberto
author2_role author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Informática
Escuela Politécnica Superior
Aprendizaje Automático (ING EPS-001)
dc.subject.none.fl_str_mv Combinatorial optimization
Genetic algorithms
Portfolio selection
Transaction costs
Informática
topic Combinatorial optimization
Genetic algorithms
Portfolio selection
Transaction costs
Informática
description This is the author’s version of a work that was accepted for publication in Applied Soft Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Applied Soft Computing, Vol 36 (2015) DOI 10.1016/j.asoc.2015.06.053
publishDate 2015
dc.date.none.fl_str_mv 2015
2015-11-01
dc.type.none.fl_str_mv research article
http://purl.org/coar/resource_type/c_2df8fbb1
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10486/676217
https://dx.doi.org/10.1016/j.asoc.2015.06.053
url http://hdl.handle.net/10486/676217
https://dx.doi.org/10.1016/j.asoc.2015.06.053
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
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
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier Ltd
publisher.none.fl_str_mv Elsevier Ltd
dc.source.none.fl_str_mv reponame:Biblos-e Archivo. Repositorio Institucional de la UAM
instname:Universidad Autónoma de Madrid
instname_str Universidad Autónoma de Madrid
reponame_str Biblos-e Archivo. Repositorio Institucional de la UAM
collection Biblos-e Archivo. Repositorio Institucional de la UAM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.298079

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