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1、外文文献翻译原文及译文标题:Anadaptiveportfoliotradingsystem:Arisk-returnportfoliooptimizationusingrecurrentTeinfbrcementlearningwithexpectedmaximumdrawdown作者：Saud,Steve期干H:ExpertSystemswithApplications,第87卷,267・279页年份：2017原文Anadaptiveportfoliotradingsystem:Arisk-returnportfoliooptimizationusingrecurre
2、ntreinforcementlearningwithexpectedmaximumdrawdownSaud,SteveAbstractDynamiccontroltheoryhaslongbeenusedinsolvingoptimalassetallocationproblems,andanumberoftradingdecisionsystemsbasedonreinforcementlearningmethodshavebeenappliedinassetallocationandportfoliorebalancing.Inthispaper,weextendt
3、heexistingworkinrecurrentreinforcementlearning(RRL)andbuildanoptimalvariableweightportfolioallocationunderacoherentdownsideriskmeasure,theexpectedmaximumdrawdown,E(MDD).Inparticular,weproposearecurrentreinforcementlearningmethod,withacoherentriskadjustedperformanceobjectivefunction,theCal
4、marratio,toobtainbothbuyandsellsignalsandassetallocationweights.Usingaportfolioconsistingofthemostfrequentlytradedexchange-tradedfunds,weshowthattheexpectedmaximumdrawdownriskbasedobjectivefunctionyieldssuperiorreturnperformancecomparedtopreviouslyproposedRRLobjectivefunctions(i.e.theShar
5、peratioandtheSterlingratio),andthatvariableweightRRLlong/shortportfoliosoutperformequalweightRRLlong/shortportfoliosunderdifferenttransactioncostscenarios.WefurtherproposeanadaptiveE(MDD)riskbasedRRLportfoliorebalancingdecisionsystemwithatransactioncostandmarketconditionstop-lossretrainin
6、gmechanism,andweshowthattheproposedportfoliotradingsystemrespondstotransactioncosteffectsbetterandoutperformshedgefundbenchmarksconsistently.Keywords:Recurrentreinforcementlearning,Expectedmaximumdrawdown,Optimalportfoliorebalancing,Downsiderisk1.1ntroductionInfinancialinvesting,ageneralg
7、oalistodynamicallyallocateasetofassetstomaximizethereturnsovertimeandminimizerisksimultaneously.Forinvestorsitisessentialtobeabletoinvestinaportfoliothatcansatisfytheirpresetgoalsbybuildinganoptimalportfolioinitiallyandsubsequentlyrebalancingitoptimally.Portfoliothe
