A Biased-Randomized Iterated Local Search Algorithm for Rich Portfolio Optimization

This research develops an original algorithm for rich portfolio optimization (ARPO), considering more realistic constraints than those usually analyzed in the literature. Using a matheuristic framework that combines an iterated local search metaheuristic with quadratic programming, ARPO efficiently...

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Detalles Bibliográficos
Autores: Kizys, Renatas, Juan, Angel A., Bartosz, Sawik, Calvet-Mir, Laura
Tipo de recurso: artículo
Fecha de publicación:2019
País:España
Institución:Universitat Oberta de Catalunya (UOC)
Repositorio:O2, repositorio institucional de la UOC
OAI Identifier:oai:openaccess.uoc.edu:10609/100326
Acceso en línea:http://hdl.handle.net/10609/100326
Access Level:acceso abierto
Palabra clave:constrained portfolio optimization
metaheuristics
efficiency indices
financial assets
iterated local search
biased randomization
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spelling A Biased-Randomized Iterated Local Search Algorithm for Rich Portfolio OptimizationKizys, RenatasJuan, Angel A.Bartosz, SawikCalvet-Mir, Laura constrained portfolio optimizationmetaheuristicsefficiency indicesfinancial assetsiterated local searchbiased randomizationThis research develops an original algorithm for rich portfolio optimization (ARPO), considering more realistic constraints than those usually analyzed in the literature. Using a matheuristic framework that combines an iterated local search metaheuristic with quadratic programming, ARPO efficiently deals with complex variants of the mean-variance portfolio optimization problem, including the well-known cardinality and quantity constraints. ARPO proceeds in two steps. First, a feasible initial solution is constructed by allocating portfolio weights according to the individual return rate. Secondly, an iterated local search framework, which makes use of quadratic programming, gradually improves the initial solution throughout an iterative combination of a perturbation stage and a local search stage. According to the experimental results obtained, ARPO is very competitive when compared against existing state-of-the-art approaches, both in terms of the quality of the best solution generated as well as in terms of the computational times required to obtain it. Furthermore, we also show that our algorithm can be used to solve variants of the portfolio optimization problem, in which inputs (individual asset returns, variances and covariances) feature a random component. Notably, the results are similar to the benchmark constrained efficient frontier with deterministic inputs, if variances and covariances of individual asset returns comprise a random component. Finally, a sensitivity analysis has been carried out to test the stability of our algorithm against small variations in the input data.Applied SciencesUniversitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)201920192019info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttp://hdl.handle.net/10609/100326reponame:O2, repositorio institucional de la UOCinstname:Universitat Oberta de Catalunya (UOC)Inglés(17);9https://www.mdpi.com/2076-3417/9/17/3509/htmhttp://creativecommons.org/licenses/by-nd/4.0info:eu-repo/semantics/openAccessoai:openaccess.uoc.edu:10609/1003262026-05-28T12:42:01Z
dc.title.none.fl_str_mv A Biased-Randomized Iterated Local Search Algorithm for Rich Portfolio Optimization
title A Biased-Randomized Iterated Local Search Algorithm for Rich Portfolio Optimization
spellingShingle A Biased-Randomized Iterated Local Search Algorithm for Rich Portfolio Optimization
Kizys, Renatas
constrained portfolio optimization
metaheuristics
efficiency indices
financial assets
iterated local search
biased randomization
title_short A Biased-Randomized Iterated Local Search Algorithm for Rich Portfolio Optimization
title_full A Biased-Randomized Iterated Local Search Algorithm for Rich Portfolio Optimization
title_fullStr A Biased-Randomized Iterated Local Search Algorithm for Rich Portfolio Optimization
title_full_unstemmed A Biased-Randomized Iterated Local Search Algorithm for Rich Portfolio Optimization
title_sort A Biased-Randomized Iterated Local Search Algorithm for Rich Portfolio Optimization
dc.creator.none.fl_str_mv Kizys, Renatas
Juan, Angel A.
Bartosz, Sawik
Calvet-Mir, Laura
author Kizys, Renatas
author_facet Kizys, Renatas
Juan, Angel A.
Bartosz, Sawik
Calvet-Mir, Laura
author_role author
author2 Juan, Angel A.
Bartosz, Sawik
Calvet-Mir, Laura
author2_role author
author
author
dc.contributor.none.fl_str_mv Universitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)
dc.subject.none.fl_str_mv constrained portfolio optimization
metaheuristics
efficiency indices
financial assets
iterated local search
biased randomization
topic constrained portfolio optimization
metaheuristics
efficiency indices
financial assets
iterated local search
biased randomization
description This research develops an original algorithm for rich portfolio optimization (ARPO), considering more realistic constraints than those usually analyzed in the literature. Using a matheuristic framework that combines an iterated local search metaheuristic with quadratic programming, ARPO efficiently deals with complex variants of the mean-variance portfolio optimization problem, including the well-known cardinality and quantity constraints. ARPO proceeds in two steps. First, a feasible initial solution is constructed by allocating portfolio weights according to the individual return rate. Secondly, an iterated local search framework, which makes use of quadratic programming, gradually improves the initial solution throughout an iterative combination of a perturbation stage and a local search stage. According to the experimental results obtained, ARPO is very competitive when compared against existing state-of-the-art approaches, both in terms of the quality of the best solution generated as well as in terms of the computational times required to obtain it. Furthermore, we also show that our algorithm can be used to solve variants of the portfolio optimization problem, in which inputs (individual asset returns, variances and covariances) feature a random component. Notably, the results are similar to the benchmark constrained efficient frontier with deterministic inputs, if variances and covariances of individual asset returns comprise a random component. Finally, a sensitivity analysis has been carried out to test the stability of our algorithm against small variations in the input data.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019
2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10609/100326
url http://hdl.handle.net/10609/100326
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv (17);9
https://www.mdpi.com/2076-3417/9/17/3509/htm
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nd/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nd/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Applied Sciences
publisher.none.fl_str_mv Applied Sciences
dc.source.none.fl_str_mv reponame:O2, repositorio institucional de la UOC
instname:Universitat Oberta de Catalunya (UOC)
instname_str Universitat Oberta de Catalunya (UOC)
reponame_str O2, repositorio institucional de la UOC
collection O2, repositorio institucional de la UOC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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