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ID:36829974
大小:3.43 MB
页数:68页
时间:2019-05-16
《基于LVQ神经网络的财务舞弊识别模型研究》由会员上传分享,免费在线阅读,更多相关内容在学术论文-天天文库。
1、东南人学硕上学位论文THERESEARCHoNTHEMoDELoFDETECTINGTHEFRAUDULENTFINANCIALSTATEMENTSBASEDONLVQNEURALNETWORKGraduate:GuNing-shengSupervisor:FengQin-ehaoSoutheastUniversityAbstractFinancialreportingfrauddoesnotonlyaffecttheinterestsofinvestors,butalsothreatthecapitalm
2、arketandeconomy.Governmentsofallcountrieshadtakenvariousmeasurestocurbthefraudulentfinancialstatements,butthephenomenonoffraudulentfinancialstatementsdidn’tdisappear.Fraudulentfinancialstatementsarestillthreateningtheinvestorsandthecapitalmarket.Soinordert
3、oprotecttheinvestorsandregulatethedomesticcapitalmarkets,thestudyoffinancialfrauddetectionisveryimportant.Inordertofindthevariablesofdetectingthefraudulentfinancialstatements,themotiveandmeansoffinancialfraudhasbeenanalyzed.Corporategovernanceandownerships
4、tructurealsohasbeenanalyzed.Bytheanalysis,66alternativeindicatorsofdetectingfinancialfraudhavebeenfound.InordertOmakesurewhichindicatorcandetectthefraudulentfinancialstatements,anempiricalresearchhasbeendone.Throughthisempiricalresearch,31indicatorswhichha
5、vetheabilityofdetectingthefraudulentfinancialstatementshavebeenfound.ThentheprincipalcomponentmethodWasusedtoreducethenumberofindicators.Throughit,12comprehensiveindicatorswereacquired.Thenthrough60modelingsamplesand32testsamples,themodelofdetectingthefrau
6、dulentfinancialstatementswasbuiltbytheLVQnetwork.ThenaempiricalresearchabouttestfI,earningVectorQuantization)neuralsampleshavebeendone,theaccuracyofthemodelbuiltbyLVQneuralnetworkWast090.6%.Atlastthreeothermodelsofthefraudulentfinancialstatementsupdetectin
7、gwerebuiltbytheBPneuralnetwork,GA—MLPneuralnetworkandSVM.Throughcomparingthefourmodels,thispaperfoundthatthemodelbuiltbyLVQneuralnetworkhasthehighestpredictionprecisionaboutthetestsamples.Keywords:fraudulentfinancialstatements;detection;LVQneuralnetwork;su
8、pportvectormachines(SVM)II目录摘要⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯.IAbstract....................................................................II第一章导论⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯l1.1研究背景⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯⋯.11.2研究财务舞弊识别的意义⋯⋯⋯
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