多边贸易信用风险评估课件教材讲义

1/40Predictingmultilateraltradecreditrisks:

comparisonsofLogitandFuzzyLogicmodelsusingROCcurveanalysis

多边贸易信用风险评估：使用ROC曲线分析

比较Logit与FuzzyLogic模型2/40Abstract本篇主要是应用

ROC(receiveroperatingcharacteristic)曲线分析比较Logit与FuzzyLogic

模型的performance。3/401.IntroductionDefaultproblemsoftradecreditapplicantsfallbasicallywithinthescopeofdiscriminationandclassificationproblems(Johnson&Wichern,1998).Altman(1968)andBeaver(1966)[Altman,1968,Beaver,1966]contributesignificantlytotheclassificationandpredictionofbusinesscreditworthinessbyadoptingaunivariatemodelandamultiplediscriminantanalysis(MDA),respectively.4/40然而，信度与效度需建立在大规模的限制与假设上。ToavoidthedefectsofMDA,Ohlson(1980)proposesthelogisticregression(Logit),whichisfoundtobethemostaccurateofthetraditionalstatisticalmethodsforpredictionandclassificationproblems.5/40贸易风险评估决策主要决定于人主观性的意见及判断。而这些价值与观感通常会造成不准确或模糊不清的语意，也无法直接由传统的数学方式表达呈现。Inlightofthis,sincethe1980sscholarshavetriedadoptingartificialintelligencetechniquestofindbetterclassificationtools.6/40FuzzyLogic(FL),formulatedbyZadeh(1965),providesasystematicwayofhandlingvagueandimpreciseinformationoninputdata,theireffectsonthesystem,andtheoutput.ManyotherresearchersalsoreportthattheFL-basedmodelisfoundtohavegoodpredictiveability(Dourra&Siy,2002;Lia,Mukaidonob,&Turksenc,2002[DourraandSiy,2002,Liaetal.,2002]).7/40Thereceiveroperatingcharacteristic(ROC)curveanalysisisoftenusedinbiomedicalandpsychophysicalapplicationstosummarizethediscriminatoryaccuracyofadiagnostictestaswellastocomparetheperformanceofdifferentmodelsforbinaryoutcomes(Lloyd,1998;Marzban,1998;Pepe,2000[Lloyd,1998,Marzban,1998,Pepe,2000]).8/402.FuzzyinferencemechanismGenerally,theinferenceprocessproceedsinthreesteps:fuzzification,inference,anddefuzzification.Inthefuzzificationstep,therealinputistranslatedfromnumericalvaluetofuzzymembershipvaluesineachoftheinputfuzzysets.9/4010/40Inthefuzzyinferencestep,‘if-then’rulesthatdefinethesystembehaviorareevaluated.Inthedefuzzificationstep,theweightedaverageoftheoutputfuzzymembershipresultistranslatedbacktoanumericalvalue.11/4012/40ROCcurveanalysis13/4014/4015/4016/4017/4018/4019/4020/4021/40D：违约N：未违约TrainingsetTPR：truepositiverateFPR：falsepositiverateTNR：truenegativerateFNR：falsenegativerate22/40AUCprob.(N>D)TheareaundertheROCcurve(c-index,c-statistic)Score23/403.Researchdesign24/40VariableselectionThisstudyemploysliteraturesurveyandexpertinterviewmethodstoinvestigatethekeypredictivevariables.25/4026/4027/4028/4029/404.ResultsThecostsofFNRcanincludeprincipal,interest,collectionfees,andlegalfees.AnFPRincludesthecostsofforegonebusinesssales.30/40Differentcutoffshavebeenusedinpriorstudiesformeasuringclassificationaccuracy.Forthehighestoverallpredictionaccuracypurpose,weusetheoptimalcutoffthatisdeterminedbythelargestAUC.（最佳的门坎值决定于最大的AUC）Cutoff门坎值31/4032/4033/40TheresultshowsthattheLogitmodelprovidesagoodfittothedataandtheestimateofthevariables'parametersinthemodelismeaningful.34/40FLmodelresults35/40TestingsummaryonthetrainingsetTestingsummaryonthetestingset36/40ROCcurvesformodelresults(trainingset)37/40ROCcurvesformodelresults(testingset)38/40ComparisonsofLogitandFLmodels(trainingset)39/40ComparisonsofLogitandFLmodels(testingset)40/405.ConclusionsTheFLmodelsexceedtheLogitmodelsintermsofoverallclassificationaccuracyandpredictionaccuracy.However,byincorporatingmeasurementintheformofROCcurves,wealsofindthatLogitoutperformsFLinclassifyingnon-defaul