Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/45579
DC FieldValueLanguage
dc.contributor.authorMacedo, Luís Lobato-
dc.contributor.authorGodinho, Pedro-
dc.contributor.authorAlves, Maria João-
dc.date.accessioned2017-12-29T19:55:29Z-
dc.date.issued2017-08-15-
dc.identifier.issn0957-4174por
dc.identifier.urihttps://hdl.handle.net/10316/45579-
dc.description.abstractRecent work has been devoted to study the use of multiobjective evolutionary algorithms (MOEAs) in stock portfolio optimization, within a common mean-variance framework. This article proposes the use of a more appropriate framework, mean-semivariance framework, which takes into account only adverse return variations instead of overall variations. It also proposes the use and comparison of established technical analysis (TA) indicators in pursuing better outcomes within the risk-return relation. Results show there is some difference in the performance of the two selected MOEAs – non-dominated sorting genetic algorithm II (NSGA II) and strength pareto evolutionary algorithm 2 (SPEA 2) – within portfolio optimization. In addition, when used with four TA based strategies – relative strength index (RSI), moving average convergence/divergence (MACD), contrarian bollinger bands (CBB) and bollinger bands (BB), the two selected MOEAs achieve solutions with interesting in-sample and out-of-sample outcomes for the BB strategy.por
dc.language.isoengpor
dc.publisherElsevierpor
dc.rightsembargoedAccess-
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/por
dc.subjectMultiobjective optimizationpor
dc.subjectEvolutionary algorithmspor
dc.subjectStock portfoliopor
dc.subjectMean-semivariancepor
dc.subjectTechnical analysispor
dc.titleMean-semivariance portfolio optimization with multiobjective evolutionary algorithms and technical analysis rulespor
dc.typearticle-
degois.publication.firstPage33por
degois.publication.lastPage43por
degois.publication.titleExpert Systems with Applicationspor
dc.relation.publisherversionhttps://doi.org/10.1016/j.eswa.2017.02.033por
dc.peerreviewedyespor
dc.identifier.doi10.1016/j.eswa.2017.02.033por
degois.publication.volume79por
dc.date.embargo2019-12-29T19:55:29Z-
uc.controloAutoridadeSim-
item.fulltextCom Texto completo-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.languageiso639-1en-
item.openairetypearticle-
item.cerifentitytypePublications-
crisitem.author.researchunitCeBER – Centre for Business and Economics Research-
crisitem.author.researchunitINESC Coimbra – Institute for Systems Engineering and Computers at Coimbra-
crisitem.author.researchunitCeBER – Centre for Business and Economics Research-
crisitem.author.orcid0000-0002-1020-3106-
crisitem.author.orcid0000-0003-2247-7101-
crisitem.author.orcid0000-0002-2268-0110-
Appears in Collections:I&D CeBER - Artigos em Revistas Internacionais
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