A learning-guided multi-objective evolutionary algorithm for constrained portfolio optimization

Khin Lwin, Rong Qu, Graham Kendall

    Research output: Contribution to journalArticle

    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 languageEnglish
    Pages (from-to)757-772
    JournalApplied Soft Computing Journal
    Volume24
    Early online date27 Aug 2014
    DOIs
    Publication statusPublished - Nov 2014

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