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1、June5,200812:27WSPC/173-IJITDM00295InternationalJournalofInformationTechnology&DecisionMakingVol.7,No.2(2008)339–359cWorldScientificPublishingCompanyFALSEFINANCIALSTATEMENTS:CHARACTERISTICSOFCHINA’SLISTEDCOMPANIESANDCARTDETECTINGAPPROACH∗BELINNABAICreditDepartment,AgricultureBankofChina(HKBranch)2
2、3/F,Tower1,AdmiraltyCentre,HongKongbailinghk@gmail.comJEROMEYENDepartmentofFinanceTheHongKongUniversityofScienceandTechnologyClearWaterBay,Kowloon,HongKongrisksolution@gmail.comXIAOGUANGYANGAcademyofMathematicsandSystemsScienceChineseAcademyofSciences,Beijing,100080,Chinaxgyang@iss.ac.cnFalseFinan
3、cialStatements(FFS)havelongbeenaseriousprobleminChinaandotherAsiancountries,whichsignificantlydampentheconfidenceoftheinvestors.Regardlessoflistedcompaniesornon-listedcompanies,thepercentageoffinancialstatementsthatcontainedfalseinformationisquitehigh,whichisoneofthemajorreasonswhyChinastockmarketsmo
4、vedintheoppositedirectiontowardsitswonderfuleconomicgrowthoverthepastfewyears.Theobjectiveofthisresearchistointroduceonestatisticaltech-nique—ClassificationandRegressionTree(CART),toidentifyandpredicttheimpactsofFFS.Wesurveyfinancialstatementsmanipulationtricks,FFSindicatorsandFFSdetectiontechniques
5、frombothChinaandinternationalperspective,andfurtherlookintotenlistedcompanieswithknownFFShistoryinChina;combiningthesefindings,weproposekeyindicatorstoworkwithCART.Ouranalysisincludes24falsefinancialreports,and124non-falsefinancialreports.WeuseCARTtodeveloptwoFFSdetectingmodels:CARTwithoutindustryben
6、chmarkandCARTwithindustrybenchmark.Forsupportingcomparison,wealsobuildaLogitregressionwhichisacommonlyusedtechniqueinFFSdetecting.WefindthatCARTiseffectiveindistinguishingFFSfromnon-FFS.BothCARTmodelsachievebetteraccuracyinidentifyingfraudcasesandmakingpredictionsthanLogitregressiondoes,andCARTwithi
7、ndustrybenchmarkisslightlybetterthanCARTwithoutbenchmark,butitdoesnotalwayshavesuperiorperformance.OurCARTmodelalsotriestocapture∗TheresearchissupportedbyNSFC(Nos:70425004,700221001).339June5,200812:27WSPC/173-IJ
