中小企业信用风险建模：来自美国市场的证明外文翻译（可编辑）

中小企业信用风险建模来自美国市场的证明外文翻译外文翻译MODELINGCREDITRISKFORSMESEVIDENCEFROMTHEUSMARKETMATERIALSOURCEWILEYCHINAAUTHOREDWARDIALTMANGABRIELESABATOCONSIDERINGTHEFUNDAMENTALROLEPLAYEDBYSMALLANDMEDIUMSIZEDENTERPRISESSMESINTHEECONOMYOFMANYCOUNTRIESANDTHECONSIDERABLEATTENTIONPLACEDONSMESINTHENEWBASELCAPITALACCORD,WEDEVELOPADISTRESSPREDICTIONMODELSPECIFICALLYFORTHESMESECTORANDTOANALYZEITSEFFECTIVENESSCOMPAREDTOAGENERICCORPORATEMODELTHEBEHAVIOUROFFINANCIALMEASURESFORSMESISANALYZEDANDTHEMOSTSIGNIFICANTVARIABLESINPREDICTINGTHEENTITIESCREDITWORTHINESSARESELECTEDINORDERTOCONSTRUCTADEFAULTPREDICTIONMODELUSINGALOGITREGRESSIONTECHNIQUEONPANELDATAOFOVER2,000USFIRMSWITHSALESLESSTHAN65MILLIONOVERTHEPERIOD19942002,WEDEVELOPAONEYEARDEFAULTPREDICTIONMODELTHISMODELHASANOUTOFSAMPLEPREDICTIONPOWERWHICHISALMOST30PERCENTHIGHERTHANAGENERICCORPORATEMODELANASSOCIATEDOBJECTIVEISTOOBSERVEOURMODELSABILITYTOLOWERBANKCAPITALREQUIREMENTSCONSIDERINGTHENEWBASELCAPITALACCORDSRULESFORSMESINTRODUCTIONSMALLANDMEDIUMSIZEDENTERPRISESAREREASONABLYCONSIDEREDTHEBACKBONEOFTHEECONOMYOFMANYCOUNTRIESALLOVERTHEWORLDFOROECDMEMBERS,THEPERCENTAGEOFSMESOUTOFTHETOTALNUMBEROFFIRMSISGREATERTHAN97PERCENTINTHEUS,SMESPROVIDEAPPROXIMATELY75PERCENTOFTHENETJOBSADDEDTOTHEECONOMYANDEMPLOYAROUND50PERCENTOFTHEPRIVATEWORKFORCE,REPRESENTING997PERCENTOFALLEMPLOYERSTHANKSTOTHESIMPLESTRUCTUREOFMOSTSMES,THEYCANRESPONDQUICKLYTOCHANGINGECONOMICCONDITIONSANDMEETLOCALCUSTOMERSNEEDS,GROWINGSOMETIMESINTOLARGEANDPOWERFULCORPORATIONSORFAILINGWITHINASHORTTIMEOFTHEFIRMSINCEPTIONFROMACREDITRISKPOINTOFVIEW,SMESAREDIFFERENTFROMLARGECORPORATESFORMANYREASONSFOREXAMPLE,DIETSCHANDPETEY2004ANALYZEASETOFGERMANANDFRENCHSMESANDCONCLUDETHATTHEYARERISKIERBUTHAVEALOWERASSETCORRELATIONWITHEACHOTHERTHANLARGEBUSINESSESINDEED,WEHYPOTHESIZETHATAPPLYINGADEFAULTPREDICTIONMODELDEVELOPEDONLARGECORPORATEDATATOSMESWILLRESULTINLOWERPREDICTIONPOWERANDLIKELYAPOORERPERFORMANCEOFTHEENTIRECORPORATEPORTFOLIOTHANWITHSEPARATEMODELSFORSMESANDLARGECORPORATESTHEMAINGOALOFTHISPAPERISTOANALYZEACOMPLETESETOFFINANCIALRATIOSLINKEDTOUSSMESANDFINDOUTWHICHARETHEMOSTPREDICTIVEVARIABLESAFFECTINGANENTITIESCREDITWORTHINESSONEMOTIVATIONISTOSHOWTHESIGNIFICANTIMPORTANCEFORBANKSOFMODELINGCREDITRISKFORSMESSEPARATELYFROMLARGECORPORATESTHEONLYSTUDYTHATWEAREAWAREOFTHATFOCUSEDONMODELINGCREDITRISKSPECIFICALLYFORSMESISAFAIRLYDISTANTARTICLEBYEDMISTER1972HEANALYZED19FINANCIALRATIOSAND,USINGMULTIVARIATEDISCRIMINANTANALYSIS,DEVELOPEDAMODELTOPREDICTSMALLBUSINESSDEFAULTSHISSTUDYEXAMINEDASAMPLEOFSMALLANDMEDIUMSIZEDENTERPRISESOVERTHEPERIOD19541969WEEXPANDANDIMPROVEHISWORKUSING,FORTHEFIRSTTIME,THEDEFINITIONOFSMEASCONTAINEDINNEWBASELCAPITALACCORDSALESLESSTHAN0MILLIONANDAPPLYINGALOGITREGRESSIONANALYSISTODEVELOPTHEMODELWEEXTENSIVELYANALYZEALARGENUMBEROFRELEVANTFINANCIALMEASURESINORDERTOSELECTTHEMOSTPREDICTIVEONESTHEN,THESEVARIABLESAREUSEDASPREDICTORSOFTHEDEFAULTEVENTTHEFINALOUTPUTISNOTONLYANEXTENSIVESTUDYOFSMEFINANCIALCHARACTERISTICS,BUTALSOAMODELTOPREDICTTHEIRPROBABILITYOFDEFAULTPD,SPECIFICALLYTHEONEYEARPDREQUIREDUNDERBASELIITHEPERFORMANCEOFTHISMODELISALSOCOMPAREDWITHTHEPERFORMANCEOFAWELLKNOWNGENERICCORPORATEMODELKNOWNASZSCOREINORDERTOSHOWTHEIMPORTANCEOFMODELINGSMECREDITRISKSEPARATELYFROMAGENERICCORPORATEMODELWEACKNOWLEDGETHATOURANALYSISCOULDSTILLBEIMPROVEDUSINGQUALITATIVEVARIABLESASPREDICTORSINTHEFAILUREPREDICTIONMODELTOBETTERDISCRIMINATEBETWEENSMESASRECENTLITERATURE,EGLEHMANN2003ANDGRUNETETAL2004,DEMONSTRATETHECOMPUSTATDATABASEUSED,HOWEVER,DOESNOTCONTAINQUALITATIVEVARIABLESNEVERTHELESS,THEPERFORMANCEACCURACYOFTHEMODELUSEDTOPREDICTSMEDEFAULTISSIGNIFICANTLYHIGHBOTHONANABSOLUTEANDRELATIVEBASISWHILETHEREHAVEBEENMANYSUCCESSFULMODELSDEVELOPEDFORCORPORATEDISTRESSPREDICTIONPURPOSES,ANDATLEASTTWOARECOMMONLYUSEDBYPRACTITIONERSONAREGULARBASIS,NONEWEREDEVELOPEDSPECIFICALLYFORSMESINADDITION,THOSEORIGINALZSCOREMODELSDEVELOPEDBYONEOFTHEAUTHORSCANBEIMPROVEDUPONBYTRANSFORMINGSEVERALOFTHEVARIABLESTOADJUSTFORTHECHANGINGVALUESANDDISTRIBUTIONSOFSEVERALOFTHEKEYVARIABLESOFTHOSEMODELSINPARTICULAR,APARSIMONIOUSSELECTIONOFVARIABLES,SOMEOFWHICHARETRANSFORMED,CANCOMPENSATEFORTHEFACTTHATOURMODELCANNOTMAKEUSEOFQUALITATIVEVARIABLESTHATAREAVAILABLEONLYFROMBANKSANDOTHERLENDINGINSTITUTIONSFILESTHEANALYSISISCARRIEDOUTONASAMPLEOF2,010USFIRMSWITHSALESLESSTHAN65MILLIONINCLUDING120DEFAULTS,SPANNINGTHETIMEPERIOD1994TO2002SECTION2PROVIDESASURVEYOFTHEMOSTRELEVANTLITERATUREABOUTFAILUREPREDICTIONMETHODOLOGIESFIRST,THECHOICEOFUSINGALOGISTICREGRESSIONTODEVELOPASPECIFICSMECREDITRISKMODELISADDRESSEDANDJUSTIFIEDTHEN,FOLLOWSANOVERVIEWANDANALYSISOFTHEFINDINGSOFTHEMOSTRECENTSTUDIESABOUTSMESSECTION3DEVELOPSAMODELTOPREDICTONEYEARSMEDEFAULTWEEXAMINEDIFFERENTSTATISTICALALTERNATIVESTOIMPROVETHEPERFORMANCEOFOURMODELANDCOMPARETHERESULTSRESULTSUSINGALOGISTICALTECHNIQUEARECONTRASTEDWITHOTHERALTERNATIVES,PRINCIPALLYDISCRIMINANTANALYSISSECTION4EMPHASISESTHEVALUEOFDEVELOPINGASPECIFICMODELINORDERTOESTIMATESMEONEYEARPROBABILITYOFDEFAULTINPARTICULAR,THEBENEFITS,INTERMSOFLOWERCAPITALREQUIREMENTSFORBANKSOFAPPLYINGASPECIFICSMEMODELARESHOW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EFAULTEDCLIENTSTHANLOGISTICMODELSWHENTHESAMEVARIABLESAREUSEDASPREDICTORSLAST,WESHOWTHATMODELINGCREDITRISKSPECIFICALLYFORSMESALSORESULTSINSLIGHTLYLOWERCAPITALREQUIREMENTSAROUND05PERCENTFORBANKSUNDERTHEAIRBAPPROACHOFBASELIITHANAPPLYINGAGENERICCORPORATEMODELTHISISTRUEWHATEVERTHEPERCENTAGEOFSMEFIRMSCLASSIFIEDASRETAILORASCORPORATESTHISISDUETOTHEHIGHERDISCRIMINATIONPOWEROFASPECIFICSMECREDITRISKMODELAPPLIEDONASMESAMPLEOURFINDINGSALSOCONFIRM,TOSOMEEXTENT,WHATHASBEENFOUNDINTHEOTHERSTUDIESIE,THATSMALLANDMEDIUMSIZEDENTERPRISESARESIGNIFICANTLYDIFFERENTFROMLARGECORPORATESFROMACREDITRISKPOINTOFVIEWHOWEVER,WEDEMONSTRATETHATBANKSSHOULDNOTONLYAPPLYDIFFERENTPROCEDURESINTHEAPPLICATIONANDBEHAVIORALPROCESSTOMANAGESMESCOMPAREDTOLARGECORPORATEFIRMS,BUTTHESEORGANIZATIONSSHOULDALSOUSEINSTRUMENTS,SUCHASSCORINGANDRATINGSYSTEMS,SPECIFICALLYADDRESSEDTOTHESMEPORTFOLIOTHUS,BANKSSHOULDCAREFULLYCONSIDERTHERESULTSOFTHISSTUDYWHENSETTINGTHEIRINTERNALSYSTEMSANDPROCEDURESTOMANAGECREDITRISKFORSMES译文中小企业信用风险建模来自美国市场的证明资料来源WILEY中国网站作者爱德华一世奥特曼加布里埃莱萨巴托考虑到中小企业在一国经济中起到的重要作用以及新巴塞尔资本协议中对它的大量关注,我们特别为中小企业建立了一种危机预警模型,并通过与普通公司模型的比较,来分析它的有效性。为了构建这样一个默认的预测模型,我们分析了中小型企业金融措施的行为,以及选取了对预测实体公司的信用最重要的变量。通过使用LOGIT回归技术对19942004年期间超过2000家的美国公司规模小于6500万美元的固定样本数据进行分析,我们建立了一个默认的为期一年的预测模型。这个模型样本外的预测能力比一般的公司模型高出30。一个相关的目的是来观察我们这个模型在符合新巴塞尔资本协议对中小企业的规定的情况下,对于减少银行资本需求的能力。导言中小企业理所当然地被看作是全世界许多国家的经济支柱。经合组织的成员国中,中小企业所占的比例超过了所有企业总数的97。在美国,中小企业提供了美国经济大约75的净工作,雇佣了大约50的私人劳动力,代表了美国997的企业家。由于大多数中小企业的结构简单,它们就能够迅速地对经济状况的改变比如公司在短期内开始成长为一个大公司或者经营失败做出反应并满足当地消费者的需求。从信贷风险的角度来看,中小企业之所以与大型企业不同有很多原因。例如,DIETSCHANDPETEY2004分析了德国和法国的一些中小企业,发现虽然它们具有更高的风险,但对比大型企业,他们彼此之间的资产相关系数较小。事实上,假设我们运用一个基于大公司数据的默认的预测模型来对中小企业进行分析,将导致较低的预测效果以及相比于单独使用较差的企业投资组合业绩。本文的研究目的之一是体现出将中小型企业与大型公司的信用风险模型区分开来的重要性。EDMISTER1972发表的一篇文章是我们发现的唯一一项以中小型企业信用风险模型为重点的研究。他分析了19种财务比率,使用多元素判别类分析,开发出了一项预测小型企业违约模型。他的研究以19541969年间的一些中小企业为样本。我们第一次采用新巴塞尔资本协定中关于中小企业的定义来拓展他的研究,并运用数据回归分析来发展其模型。我们广泛分析了一系列的金融相关指标,以找到最具预测性质的指标。然后用这些变量来预测金融违约事件。本文不仅仅对中小企业的金融特征进行了广泛研究,也预测了他们违约率的模型,特别是在新巴塞尔资本协定2中要求的一年违约率。本文将这个模型与著名的一般公司模型又称阿特曼ZSCORE模型进行比较,以显示将中小型企业与大型公司的信用风险模型区分开来的重要性。诚然,我们还可以运用一些定性的变量来预测财务破产模型,以更好区分不同类型中小企业。COMPUSTAT数据库并不包含定性的变量,但是该模型预测中小企业违约率的准确性却是不容置疑的。关于公司财务困难预测目的的成功模型不胜枚举,其中至少有两个是经常被大家使用的,但是他们都不是为中小企业特别设计的。此外,通过转变一些变量来适应那些模型中关键变量的价值分布可以改进阿特曼ZSCORE模型。特别是,有针对性得选择变量,这样通过转变某些变量可以弥补我们模型不能充分利用定性变量的缺憾。这些定性变量往往只能从银行或是其他主要机构的文件内获得。此项分析以1994到2002年间2010个美国公司为样本,包括120家违约的企业。第二部分是与破产预测方法相关的文献概况。首先,我们提出并分析了采用数据回归分析来开发中小型企业信用风险模型。然后,本文分析了中小型企业的最新报道。第三部分开发了一个预测一年内小型企业破产的模型。我们分析了不同的数据以提高我们模型的效率,并对研究结果进行了比较。特别是运用数理方法与运用差别分析的方法所得到的结果。第四部分的重点在于开发一个估计中小型企业一年破产率的模型的价值,特别是对那些利用中小企业模型的银行在其在降低资本需求方面的好处。我们发现提高信用风险模型预测准确度有助于降低巴塞尔资本2中关于对中小企业的资本要求,与此同时,我们也运用AIRB方法,这样双管齐下,中小企业顾客的利息成本也能够降低。第五部分是本文的结论。首先,我们运用未记录回归分析关于这一模型的详细讨论见附录A,所有迹象都应证了我们的预测例如我们希望KPG和除了短期债务或BV股权的所有预测量都呈现正关联每个预测量的WALD测试、关联测试在数据上都很重要。我们可以肯定所选预测量与违约事件关联很大。HOSMERLEMESHOW测试表明运用合适的数据性方法对于整个测试在数据层面上而言很重要。本文用的逻辑回归分析方法不是一个很合适的方法。但是,HOSMERLEMESHOW的测试结果可能具有误导性,因为它只包括一些原始预测量的价值范围。最后,我们观察了关联性的测量标准