Extended mean-variance model for reliable evolutionary portfolio optimization

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dc.contributor.author Garcia-Rodriguez, Sandra
dc.contributor.author Quintana, David
dc.contributor.author Galván, Inés M.
dc.contributor.author Isasi, Pedro
dc.date.accessioned 2015-08-28T11:15:42Z
dc.date.available 2015-08-28T11:15:42Z
dc.date.issued 2014-01-01
dc.identifier.bibliographicCitation García, S., Quintana, D., Galván, I., Isasi, P. (2014). Extended meanvariance model for reliable evolutionary portfolio optimization. AI Communications, vol. 27, no. 3, pp. 315-324.
dc.identifier.issn 0921-7126
dc.identifier.uri http://hdl.handle.net/10016/21503
dc.description.abstract Real world optimization of financial portfolios pose a challenging multiobjective problem that can be tackled using Evolutionary Algorithms. The fact that the optimization process is subject to the presence of uncertainty concerning asset returns is likely to lead to unreliable solutions. This work suggests extending the classic mean-variance optimization problem with a third explicit robustness objective. This results on sets of portfolios that can be subsequently grouped together according to their reliability. This additional information allows for a better informed decision making regarding asset allocation. © 2014 - IOS Press and the authors. All rights reserved.
dc.description.sponsorship Financial support granted by the Spanish Ministry of Science under contract TIN2011-28336 (MOVES).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.rights © IOS PRESS
dc.subject.other Financial portfolio optimization
dc.subject.other Multiobjective evolutionary algorithms
dc.subject.other Robustness (control systems)
dc.title Extended mean-variance model for reliable evolutionary portfolio optimization
dc.type article
dc.relation.publisherversion http://dx.doi.org/10.3233/AIC-140600
dc.subject.eciencia Informática
dc.identifier.doi 10.3233/AIC-140600
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. TIN-2011-28336
dc.type.version acceptedVersion
dc.identifier.publicationfirstpage 315
dc.identifier.publicationissue 3
dc.identifier.publicationlastpage 324
dc.identifier.publicationtitle AI Communications
dc.identifier.publicationvolume 27
dc.identifier.uxxi AR/0000015222
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