Abstract
Portfoliooptimizationinvolvestheoptimalassignmentoflimitedcapitaltodifferentavailablefinancialassetstoachieveareasonabletrade-offbetweenprofitandriskobjectives.Inthispaper,westudiedtheextendedMarkowitz’smean-varianceportfoliooptimizationmodel.Weconsideredthecardinality,quan-tity,pre-assignmentandroundlotconstraintsintheextendedmodel.Thesefourreal-worldconstraintslimitthenumberofassetsinaportfolio,restricttheminimumandmaximumproportionsofassetsheldintheportfolio,requiresomespecificassetstobeincludedintheportfolioandrequiretoinvesttheassetsinunitsofacertainsizerespectively.Anefficientlearning-guidedhybridmulti-objectiveevolutionaryalgo-rithmisproposedtosolvetheconstrainedportfoliooptimizationproblemintheextendedmean-varianceframework.Alearning-guidedsolutiongenerationstrategyisincorporatedintothemulti-objectiveopti-mizationprocesstopromotetheefficientconvergencebyguidingtheevolutionarysearchtowardsthepromisingregionsofthesearchspace.Theproposedalgorithmiscomparedagainstfourexistingstate-of-the-artmulti-objectiveevolutionaryalgorithms,namelyNon-dominatedSortingGeneticAlgorithm(NSGA-II),StrengthParetoEvolutionaryAlgorithm(SPEA-2),ParetoEnvelope-basedSelectionAlgorithm(PESA-II)andParetoArchivedEvolutionStrategy(PAES).ComputationalresultsarereportedforpubliclyavailableOR-librarydatasetsfromsevenmarketindicesinvolvingupto1318assets.Experimentalresultsontheconstrainedportfoliooptimizationproblemdemonstratethattheproposedalgorithmsignificantlyoutperformsthefourwell-knownmulti-objectiveevolutionaryalgorithmswithrespecttothequalityofobtainedefficientfrontierintheconductedexperiments
Original language | English |
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Pages (from-to) | 757-772 |
Journal | Applied Soft Computing Journal |
Volume | 24 |
Early online date | 27 Aug 2014 |
DOIs | |
Publication status | Published - Nov 2014 |