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1、IEEETRANSACTIONSONNEURALNETWORKS,VOL.12,NO.4,JULY2001929BankruptcyPredictionforCreditRiskUsingNeuralNetworks:ASurveyandNewResultsAmirF.Atiya,SeniorMember,IEEEAbstract—Thepredictionofcorporatebankruptciesisansult,theregulatorsareacknowledgingtheneedandareurgingimportantandwide
2、lystudiedtopicsinceitcanhavesignifi-thebankstoutilizecuttingedgetechnologytoassessthecreditcantimpactonbanklendingdecisionsandprofitability.Thisriskintheirportfolios.Measuringthecreditriskaccuratelyalsoworkpresentstwocontributions.Firstwereviewthetopicofallowsbankstoengineerf
3、uturelendingtransactions,soastobankruptcyprediction,withemphasisonneural-network(NN)models.Second,wedevelopanNNbankruptcypredictionmodel.achievetargetedreturn/riskcharacteristics.TheotherbenefitInspiredbyoneofthetraditionalcreditriskmodelsdevelopedofthepredictionofbankruptcie
4、sisforaccountingfirms.IfanbyMerton,weproposenovelindicatorsfortheNNsystem.Weaccountingfirmauditsapotentiallytroubledfirm,andmissesshowthattheuseoftheseindicatorsinadditiontotraditionalgivingawarningsignal(saya“goingconcern”opinion),thenitfinancialratioindicatorsprovidesasigni
5、ficantimprovementinfacescostlylawsuits.the(out-of-sample)predictionaccuracy(from81.46%to85.5%forathree-year-aheadforecast).Thetraditionalapproachforbanksforcreditriskassessmentistoproduceaninternalrating,whichtakesintoaccountvar-IndexTerms—Asset-basedmodel,bankruptcypredictio
6、n,cor-poratedistress,corporatefailureprediction,creditrisk,defaultiousquantitativeaswellassubjectivefactors,suchasleverage,prediction,financialratios,financialstatementdata,multilayerearnings,reputation,etc.,throughascoringsystem[48].Thenetworks.problemwiththisapproachisofcou
7、rsethesubjectiveaspectoftheprediction,whichmakesitdifficulttomakeconsistentestimates.Somebanks,especiallysmallerones,usetheratingsI.INTRODUCTIONissuedbythestandardcreditratingagencies,suchasMoody’sANKRUPTCYpredictionhaslongbeenanimportantandandStandard&Poor’s.Theproblemwithth
8、eseratingsisthatBwidelystudiedtopic.Themainimpactofsuchresearchisthe
