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1、ApplyingDynamicFuzzyModelinCombinationwithSupportVectorMachinetoExploreStockMarketDynamismDeng-YivChiuandPing-JieChenDepartmentofInformationManagement,ChungHuaUniversityHsin-chuCity,Taiwan300,R.O.C.{chiuden,m09310006}@chu.edu.twAbstract.Inthestudy,anewdynamicfuzz
2、ymodelisproposedincombinationwithsupportvectormachine(SVM)toexplorestockmarketdynamism.Thefuzzymodelintegratesvariousfactorswithin?uentialdegreeastheinputvariables,andthegeneticalgorithm(GA)adjuststhein?uentialdegreeofeachinputvariabledynamically.SVMthenservestop
3、redictstockmarketdynamisminthenextphase.Inthemeanwhile,themultiperiodexperimentmethodisdesignedtosimulatethevolatilityofstockmarket.Then,wecompareitwithothermethods.Themodelfromthestudydoesgeneratebetterresultsthanothers.1IntroductionStockmarketisacomplicatedandv
4、olatilesystemduetotoomanypossiblein?uentialfactors.Inthepaststudies,asaresult,dynamisminthestockmar-ketwasoftenconsideredasrandommovement.Nevertheless,accordingtotheresearchesintherecentyears,itisnotentirelyrandom.Instead,itishighlycomplicatedandvolatile[1].Manyf
5、actors,includingmacroeconomicvariablesandstockmarkettechnicalindicators,havebeenproventohaveacertainlevelofforecastcapabilityonstockmarketduringacertainperiodoftime[2].Inthepastdecade,variousmethodshavebeenwidelyappliedinthestockmarketforecastsuchaslinearandnonli
6、nearmathematicalmodelsormulti-agentmech-anism[3]tosimulatethepotentialstockmarkettransactionmechanism,suchasarti?cialneuralnetwork(ANN)ofmultiplelayersofthresholdnonlinearfunction.Becauseoftheadvantagesofarbitraryfunctionapproximationandneedlessofstatisticsassump
7、tion,ANNiswidelyappliedinthesimulationofpotentialmar-kettransactionmechanism[4].Also,toimprovetheforecastperformance,somemachinelearningmethodsareapplied.Forexample,geneticalgorithm(GA)isusedtoreduceinputfeaturedimensionandselectbettermodelparameters[5]toincrease
8、theforecastaccuracyrate.Supportvectormachine(SVM)isanewlydevelopedmathematicalmodelwithoutstandingperformancesinhandlinghighdimensionentryspaceproblems.Suchafe
