互联网金融P2P网络借贷外文翻译文献

文献信息：文献标题：EvaluatingcreditriskandloanperformanceinonlinePeer-to-Peer(P2P)lending（点对点(P2P)网络借贷的信用风险与贷款绩效评估）国外作者：RizaEmekter,YanbinTu,BenjamasJirasakuldech,MinLu文献出处：《AppliedEconomics》,2015,47(1):54-70字数统计：英文3063单词，15818字符；中文5110汉字外文文献：EvaluatingcreditriskandloanperformanceinonlinePeer-to-Peer(P2P)lendingAbstractOnlinePeer-to-Peer(P2P)lendinghasemergedrecently.Thismicroloanmarketcouldoffercertainbenefitstobothborrowersandlenders.UsingdatafromtheLendingClub,whichisoneofthepopularonlineP2Plendinghouses,thisarticleexplorestheP2Ploancharacteristics,evaluatestheircreditriskandmeasuresloanperformances.Wefindthatcreditgrade,debt-to-incomeratio,FICOscoreandrevolvinglineutilizationplayanimportantroleinloandefaults.Loanswithlowercreditgradeandlongerdurationareassociatedwithhighmortalityrate.TheresultisconsistentwiththeCoxProportionalHazardtestwhichsuggeststhatthehazardrateorthelikelihoodoftheloandefaultincreaseswiththecreditriskoftheborrowers.Finally,wefindthathigherinterestrateschargedonthehighriskborrowersarenotenoughtocompensateforhigherprobabilityoftheloandefault.TheLendingClubmustfindwaystoattracthighFICOscoreandhigh-incomeborrowersinordertosustaintheirbusinesses.Keywords:Peer-to-Peerlending;creditgrade;FICOscore;defaultriskI.IntroductionWiththeadventofWeb2.0,ithasbecomeeasytocreateonlinemarketsandvirtualcommunitieswithconvenientaccessibilityandstrongcollaboration.OneoftheemergingWeb2.0applicationsistheonlinePeer-to-Peer(P2P)lendingmarketplaces,wherebothlendersandborrowerscanvirtuallymeetforloantransactions.Suchmarketplacesprovideaplatformserviceofintroducingborrowerstolenders,whichcanoffersomeadvantagesforbothborrowersandlenders.Borrowerscangetmicroloansdirectlyfromlenders,andmightpaylowerratesthancommercialcreditalternatives.Ontheotherhand,lenderscanearnhigherratesofreturncomparedtoanyothertypeoflendingsuchascorporatebonds,bankdepositsorcertificateofdeposits.OneoftheproblemsinonlineP2Plendingisinformationasymmetrybetweentheborrowerandthelender.Thatis,thelenderdoesnotknowtheborrower'scredibilityaswellastheborrowerdoes.Suchinformationasymmetrymightresultinadverseselection(Akerlof,1970)andmoralhazard(StiglitzandWeiss,1981).Theoretically,someoftheseproblemscanbealleviatedbyregularmonitoring,butthisapproachposesachallengeintheonlineenvironmentbecausetheborrowersandthebuyersdonotphysicallymeet.Fosteringandenhancingthelender'strustintheborrowercanalsobeimplementedtomitigateadverseselectionandmoralhazardproblems.Inthetraditionalbank-lendingmarkets,bankscanusecollateral,certifiedaccounts,regularreporting,andevenpresenceoftheboardofdirectorstoenhancethetrustintheborrower.However,suchmechanismsaredifficulttoimplementintheonlineenvironmentwhichwillincurasignificanttransactioncost.Toreducelendingrisksassociatedwithinformationasymmetry,currentonlineP2Plendinghasthefollowingarrangements.First,theLendingClubscreensoutanypotentialhigh-riskborrowersbasedontheFICOscore.TheminimumFICOscoretobeabletoparticipateis640.Second,thetypicalsizeoftheloansproducedinthismarketissmall,whichisunder$35000attheLendingClub.Therefore,theseloansareessentiallymicroloanswhichposearelativelysmalllossincaseofdefault.Third,themarketmakeroffersmatchmakingsystemswhichcanbeusedtogenerateportfoliorecommendationsandminimizelendingrisks.Fourth,ifaborrowerfailstopay,themarketmakerwillreportthecasetoacreditagencyandhireacollectionagencytocollectthefundsonbehalfofthelender.AlthoughtherearecertainstructuresimposedintheonlineP2Pthathelptominimizetherisk,thisformoflendingisinherentlyassociatedwithgreateramountofriskcomparedtothetraditionallending.ThepurposeofthisarticleistoevaluatethecreditriskofborrowersfromoneofthelargestP2PplatformsintheUnitedStatesprovidedbytheLendingClub,whichhelplenderstomakemoreinformeddecisionsabouttheriskandreturnefficiencyofloansbasedontheborrowers'grade.Therearetworelatedresearchquestionsthisarticlewilladdress:(1)Whataresomeoftheborrowers'characteristicsthathelpdeterminethedefaultrisk?and(2)Isthehigherreturngeneratedfromtheriskierborrowerlargeenoughtocompensatefortheincrementalrisk?Lenderscanallocatetheirinvestmentsmoreefficientlyiftheyknowwhatcharacteristicsoftheborroweraffectthedefaultrisk.EachborrowerisclassifiedbycreditgradewithcorrespondingborrowingrateassignedbytheLendingClub.Tomakeanefficientallocation,alendershouldknowwhetherthehigherinterestratessetforhigh-riskborrowersaresufficienttocompensatethelendersforthehigherprobabilitiesofapotentialloss.OurfindingssuggestthatborrowerswithhighFICOscore,highcreditgrade,lowrevolvinglineutilizationandlowdebt-to-incomeratioareassociatedwithlowdefaultrisk.ThisfindingisconsistentwiththestudiesbyDuarteetal.(2012)whoreportthatborrowerswithatrustworthycharacteristicwillhavebettercreditscoresbutlowprobabilityofdefault.Thisresultalsosuggeststhatbesidestheloanapplicants'socialtiesandfriendshipasreportedbyFreedmanandJin(2014)andLinetal.(2013),thefourfactorsdiscussedabovearealsoimportantinexplainingthedefaultrisk.WhencomparingwithUSnationalborrowers,theresultsshowthattheLendingClubshouldcontinuetoscreenouttheborrowerswithlowerFICOscoreandattractthehighestFICOscoreborrowersinordertosignificantlyreducethedefaultrisk.Inrelatingtherisktothereturn,itshowsthathigherinterestratechargedfortheriskierborrowerisnotsignificantenoughtojustifythehigherdefaultprobability.OurfindinghereisconsistentwiththestudybyBerkovich(2011)whoreportsthathighqualityloansofferexcessreturn.II.LiteratureReviewThreemainstreamsofresearchhaveemergedinresponsetothegrowingpopularityofP2Plending.ThefirststreamofresearchexaminesthereasonsfortheemergenceofonlineP2Plending.Thesecondstreamofresearchfocusesondeterminingthefactorsthatexplainthefundingsuccessanddefaultrisk.ThelaststreamofresearchinvestigatestheperformanceofonlineP2Ploanforagivenleveloftherisk.Peergrouplendinghasbeenemerginginlocalcommunitiesandhasattractedtheresearchinthisarea.Conlin(1999)developsamodeltoexplaintheexistenceofpeergroupmicro-lendingprogrammesintheUnitedStatesandCanada.Hefindsthatpeergroupsenablefixedcoststobeimposedontheentrepreneurswhileminimizingtheprogramme'soverheadcosts.AshtaandAssadi(2008)investigatewhetherWeb2.0techniquesareintegratedtosupporttheadvancedsocialinteractionsandassociationswithlowercostsforP2Plending.HulmeandWright(2006)studyacaseofonlineP2Plendinghouse,Zopa,intheUnitedKingdom.TheysuggestthattheemergenceofonlineP2Plendingisadirectresponsetosocialtrendsandademandfornewformsofrelationshipinfinancialsectorunderthenewinformationage.Thereisextantliteraturethatidentifiesthefactorsdeterminingthefundingsuccessanddefaultrisk.UsingtheCanadianmicro-creditdata,GomezandSantor(2003)findthatgrouplendingofferslowerdefaultratesthanconventionalindividuallendingdoes.StudybyIyeretal.(2009)showsthatlenderscanevaluateonethirdofcreditriskusingbothhardandsoftdataabouttheborrower.Linetal.(2013)analysetheroleofsocialconnectionsinevaluatingcreditriskanddiscoverthatstrongsocialnetworkingrelationshipisanimportantfactorthatdeterminestheborrowingsuccessandlowerdefaultrisk.Linetal.(2013)furtherreportthatapplicants'friendshipcouldincreasetheprobabilityofsuccessfulfunding,lowerinterestratesonfundedloans,andtheseborrowersareassociatedwithlowerexpostdefaultratesatProsper.TheimportanceofsocialtiesindeterminingloansfundedisalsoexaminedbyFreedmanandJin(2014).Theresultshowsthatborrowerswithsocialtiesaremorelikelytohavetheirloansfundedandreceivelowerinterestrates.However,theyalsofindevidenceofriskstolendersregardingborrowerparticipationinsocialnetworks.Severalotherstudiesexaminewhethercertainborrowers'characteristicsandpersonalinformationdeterminethesuccessofloanfundinganddefaultrisk.Herzensteinetal.(2008)showthatborrowers'financialstrength,theirlistingandpublicizingefforts,anddemographicattributesaffectlikelihoodoffundingsuccess.StudybyDuarteetal.(2012)furtherarguesthatborrowerswhoappearmoretrustworthyhavebettercreditscorewithhigherprobabilitiesofhavingtheirloansfundedanddefaultlessoften.Larrimoreetal.(2011)demonstratethatborrowerswhouseextendednarratives,concretedescriptionsandquantitativewordshavepositiveimpactonfundingsuccess.However,humanizingpersonaldetailsorloanjustificationshavenegativeinfluencesonfundingsuccess.Qiuetal.(2012)furtherrevealthatinadditiontopersonalinformationandsocialcapital,othervariables,includingloanamount,acceptablemaximuminterestrateandloanperiodsetbyborrowers,significantlyinfluencethefundingsuccessorfailure.Galaketal.(2011)furthershowthatlenderstendtofavourindividualovergroupborrowersandborrowerswhoaresociallyproximatetothemselves.Theyalsofindthatlendersprefertheborrowerswhoaremorelikethemselvesintermsofgender,occupationandfirstnameinitial.Moreinterestingly,GonzalezandLoureiro(2014)havesimilarfindings:(1)whenperceivedagerepresentscompetence,attractivenesshasnoeffectonloansuccess;(2)whenlendersandborrowersareofthesamegender,attractivenessmightleadtoaloanfailure(i.e.,the‘beautyisbeastly'effect)and(3)loansuccessissensitivetotherelativeageandattractivenessoflendersandborrowers.Herzensteinetal.(2011)findthatherdingintheloanauctionispositivelyrelatedtoitssubsequentperformance,thatiswhetherborrowerspaythemoneybackontime.III.DataInthissection,theloanapplicants'dataisfirstdescribed,followedbyloandistributionbasedonloanpurposes,creditgradeandloanstatusanditendswiththedetaileddescriptivestatisticsoftheloanapplicants.Thisstudyuses61451loanapplicationsintheLendingClubfromMay2007toJune2012obtainedfrom.Overthestudyperiod,theLendingClublentabout$713milliontoborrowers.Toaddresstheborrowers'behaviourinonlineP2Plending,wefirstexaminethemainreasonsforborrowingmoneyfromothers.Table1liststheborrowers'self-claimedreasonssummarizedintheLendingClub.Almost70%ofloanrequestedarerelatedtodebtconsolidationorcreditcarddebtswithatotalloanamountrequestedofapproximately$387millionand$108million,respectively.Thenumberofloanapplicationsforeducation,renewableenergyandvacationcontributelessthan1%oftotalloanswiththetotalloanrequestedrangingfrom1to3million.TheborrowersstatethattheirpreferencestoborrowfromtheLendingClubarelowerborrowingrateandinabilitytoborrowenoughmoneyfromcreditcards.Thesecondpurposeforborrowingistopayhomemortgageortore-modelhome.Table1.Loandistributionsbyloanpurpose(May2007–June2012)Theloan-seekingpersonsareaskedtoprovidethereasonsforrequestingloans.TheLendingClubusestheborrower'sFICOcreditscoresalongwithotherinformationtoassignaloancreditgraderangingfromA1toG5indescendingcreditrankstoeachloan.Thedetailedprocedureisasfollows:afterassigningabasescorebasedonFICOratings,theLendingClubmakessomeadjustmentsdependingonrequestedloanamount,numberofrecentcreditinquiries,credithistorylength,totalopencreditaccount,currentlyopencreditaccountsandrevolvinglineutilizationtodeterminethefinalgrade,whichinturndeterminestheinterestrateontheloan.Table2reportstheloandistributionbycreditgrade.ThemajorityofborrowingrequestshavegradesbetweenA1andE5.TheHighestloanamountsrequestedarefromborrowerswith‘B'creditgrade,whichcontribute29.56%oftotalamountofloansrequested.Thetotalnumberofapplicantsforthis‘B'creditgradegroupis18707,whichrepresentstotalloansofapproximately$210million.Thelowestloanamountsrequestedarefromborrowerswiththelowest‘G'creditgradewhichaccountsfor1.53%oftotalloans.Thereareonly608loanapplicantsforthislowestcreditrating‘G'groupanditrepresentsapproximately$11millionintotalloanvalue.AccordingtotheLendingClub'spolicy,aloancreditgradeisusedtodeterminetheinterestrateandthemaximumamountofmoneythataborrowercanrequest.Thehighertheloangrade,thelowertheinterestrate.Aborrowingrequestwithalowgraderendersahigherinterestrateasacompensationforahighriskheldbylenders.Table2.Loansdistributionbycreditgrades(May2007–June2012)Notes:TheLendingClubusestheborrowers’FICOcreditscoresalongwithotherinformationtoclassifyaloanfromGradeA1toG5indescendingcreditrisk.Therefore,A1creditgraderepresentsthehighestcreditquality/low-riskborrowers,whereasG5creditgraderepresentsthelowestcreditquality/high-riskborrowers.Totalamountofloansrequestedasapercentageoftotalloanis19.35%forcreditgradegroup‘A’,29.56%for‘B’,19.94%for‘C’,14.84%for‘D’,10.15%for‘E’,4.59%for‘F’and1.53%for‘G’.Finally,PanelAofTable3showstheloanstatusforalltheloanrequestson20July2012.Overall,thedefaultrateis4.60%withtotallossesofapproximately$29million.Another2.45%oftotalloanrequestswhichconstitute$18.6millioncouldbepotentiallylostbecausetheborrowersarelateinmakingpaymentwithin30daysor120daysandnotpayingthenormalinstalments.17.98%oftheloansarefullypaidwithanapproximatevalueof$108million.The$557millionloansareincurrentstatusaccountfor74.91%oftotalloans.Naturally,loanswithalowergradedemonstrateahigherdefaultrate.Therefore,studyonriskmanagementonP2Plendingisrelevantforthelenderstooptimizetheirinvestmentportfolios.PanelBofTable3reportstheloanstatusforthematuredloans.Theoveralllossrateismuchhigherformaturedloans.Among4904maturedloans,914loansarecharged-off,whichrepresent18.6%.Thetotallossis$5.5millionwhichrepresents13%ofallmaturedloansamount.Lessthan1%ofthematuredloansarelateintermsofmakingpaymentwiththeunpaidbalanceofapproximately$27000.80.77%or$33millionofmaturedloansarefullypaid.Table3.Loandistributionbytheloanstatus(May2007–June2012)Table4reportsthegeneralcharacteristicsandcredithistoryoftheonlineP2PloanapplicantsfromtheLendingClub.Basedonoursampleof61451loanapplicants,theaveragemonthlyinterestchargedonaloanis12.34%.Onaverage,471dayspassedfromtheissuedateoftheloan.Theaveragecreditgradeofaborroweris25,whichcorrespondstocreditcategorybetweenBandC.Theaveragesizeofatypicalloanis$11604andtheaveragemonthlypaymentis$351.Theborroweringeneralpaysback$4384amonthandhas$7873lefttobepaid.Theaverageratiooftheremainingbalancetototalloansis63%.Examiningtheborrowers'characteristics,itshowsthatthemeanincomeofaborrowerfromtheLendingClubis$5796withthedebtstoincomeratioof0.1381.Onaverage,aborrowerhas9.56opencreditlinesand22totalcreditlines,carries$14315averagerevolvingcreditbalanceandalmosthalf(51.6%)ofhisorhercreditlimit.Inthelastsixmonths,thereis1creditinquiryrequestedbyanaverageborrower.AverageFICOscorecategoryofatypicalborroweris3.48,whichcorrespondstoaFICOscorebetween680and750.Table4.Descriptivestatistics(May2007–June2012)Notes:CreditGradeisthegradeassignedbytheLendingClubbasedontheFICOranocreditratinginformationalongwithotherinformation.CreditGrade‘1’istheloancategoryof‘G’whichistheriskiestclassofloans.CreditGrade‘7’istheloancategoryof‘A’whichisthelowestriskborrowers.FICOranoisthecreditratingoftheborrowersratedbycreditcardcompanies.FICO6correspondstoborrowerswiththeFICOscoreabove780,FICO5correspondstoFICOscorebetween750–779,FICO4=714–749,FICO3=679–713,FICO2=660–678andFICO1=640–659,respectively.IV.ConclusionsCreditriskisanimportantconcernfortheP2Ploans.ThisstudyemploysthedatafromtheLendingClubtoevaluatethecreditriskoftheP2Ponlineloans.Wefindthatcreditscore,debt-to-incomeratio,FICOscoreandrevolvinglineutilizationplayanimportantroleindeterminingloandefault.ThecreditcategorizationusedbytheLendingClubsuccessfullypredictsthedefaultprobabilitywithoneexceptionofnextlowestcreditgrade‘F'.Ingeneral,highercreditgradeloanisassociatedwithlowerdefaultrisk.Themortalityriskalsoincreaseswiththematurityoftheloans.Loanswithlowercreditgradeandlongerdurationareassociatedwithhighmortalityrate.TheCoxProportionalHazardTestresultsshowthatasthecreditriskoftheborrowersincreases,sodoesthelikelihoodofloanbeingdefault.However,thehigherinterestratecurrentlychargedfortheriskierborrowerisnotsignificantenoughtojustifythehigherdefaultprobability.Thissuggeststhatthelenderswouldbebetterofftolendonlytothesafestborrowersinthehighestgradecategoryof7orGradeA.Increasingspreadsonriskierborrowermayleadtoamoresevereadverseselectionresultinginhigherdefaultrisk.TheLendingClublendersshouldeitherextendcreditsonlytothehighestgradeborrowerortrytofindmorecreativewaystolowerthedefaultrateamongcurrentborrowers.WhencomparingwiththeUSnationalconsumers,borrowerswithrelativelyhigherincomeandpotentiallyhigherFICOscoresdonotparticipateintheP2Pmarket.Creatingincentivestoattractthesetypesofborrowerswouldhaveasignificantpotentialtodecreasethedefaultriskinthismarket.中文译文：点对点（P2P）网络借贷的信用风险与贷款绩效评估摘要近年来点对点（P2P）网络借贷开始兴起。这种小微贷款市场可以为借款人和贷款人提供一定的收益。本文利用受欢迎的P2P网络社交借贷平台之一的借贷俱乐部的数据，探讨了P2P贷款的特征，评估了其信用风险和贷款绩效。我们发现，信用等级、负债收入比、FICO评分和循环贷款利用率在贷款违约中起着重要的作用。信用等级较低、期限较长的贷款往往与高死亡率联系在一起。这一结果与Cox比例风险模型测试的相一致，这表明贷款违约的风险率或可能性随着借款人的信用风险而增加。最后，我们发现，对高风险借款人收取较高利率，并不能够降低贷款的高违约率。借贷俱乐部需要找到吸引高FICO评分和高收入借款人的方法，以维持其业务。关键词:P2P网络借贷；信用等级；FICO评分；违约风险1.引言随着Web2.0时代的到来，创建方便快捷、协作性强的在线市场和虚拟社区已经不是一件难事。新兴的Web2.0应用程序之一是点对点(P2P)网络借贷市场，在那里贷款人和借款人几乎可以完成贷款交易。将借款人引荐给贷款人，这种市场提供了平台服务，可以为借款人和贷款人提供一些优势。借款人可以直接从贷款人那里获得小额贷款，并且其支付的利率比商业贷款要低。另一方面，与任何其他类型的贷款如公司债券、银行存款或存单相比，贷款人可以赚取更高的回报率。借款人与贷款人之间的信息不对称，是P2P网络借贷的问题之一。也就是说，贷款人不了解借款人的信誉，同样借款人也不了解贷款人的信誉。这种信息不对称可能导致逆向选择（阿克洛夫，1970）和道德风险（斯蒂格里兹和温斯，1981）。从理论上讲，这些问题可以通过定期监测来得到缓解，但这种做法在网络环境下很难实施，因为借款人和贷款人没有实际接触。促进和提高贷款人对借款人的信任也可以实施，以减轻逆向选择和道德风险问题。在传统的银行贷款市场中，银行可以使用抵押品、认证账户、定期报告，甚至出席董事会来增强对借款人的信任。然而，这样的机制将产生巨大的交易成本，因此难以在网络环境中实现。为了减少由信息不对称所引起的贷款风险，目前的P2P网络借贷平台有以下安排。首先，借贷俱乐部根据FICO评分筛选出潜在的高风险借款人，能够参与平台借贷的最低FICO评分为640。第二，这个平台产生的贷款规模很小，借贷俱乐部的贷款不到35000美元。因此，这些贷款基本上是小额贷款，在违约的情况下造成的损失相对较小。第三，平台建设者提供配对体系，可以用来生成投资组合建议，并尽可能减少贷款风险。第四，如果借款人没有付款，则平台建设者将向信贷机构报告情况，并聘请收款机构代表贷款人收取资金。虽然P2P网络借贷中有一些有助于降低风险的强化结构，但与传统贷款相比，这种形式的贷款在本质上与更大的风险联系在一起。本文的目的是评估美国最大的P2P网络借贷平台之一的借贷俱乐部的借款人的信用风险，这有助于贷款人根据借款人的等级对贷款的风险和回报率做出更明智的决策。本文将讨论两个相关的研究问题：（1）有助于确定违约风险的借款人的特征有哪些？（2）高风险借款人的回报率是否高到能够弥补增量风险？如果贷款人知道借款人的哪些特征影响到违约风险，那么贷款人可以更有效地分配他们的投资。每个借款人按照信用等级进行分类，借贷俱乐部分配相应的借款利率。为了进行有效的分配，贷款人应该知道高风险借款人的高利率是否能够补偿贷款人潜在损失的更高概率。我们的研究结果表明，FICO评分高、信用等级高、循环贷款利用率低、负债收入比低的借款人，往往违约风险偏低。这一发现与杜阿尔特等人（2012年）的研究相一致，他们指出具有可信赖特征的借款人信用评分较好，而违约概率低。这一结果还表明，除弗里德曼和吉恩（2014）、林等人（2013）指出的贷款申请人的社会关系和朋友关系外，上述四个因素对解释违约风险也很重要。与美国国家借款人相比，结果显示，贷款俱乐部应继续筛选FICO评分低的借款人，吸引FICO评分高的借款人，从而大幅度降低违约风险。在将风险与回报相关联时，本研究表明，对高风险的借款人收取高的利率并不能对高违约率作出解释。我们在这里的发现与别尔科维奇（2011）的研究相一致，他指出高质量的贷款提供超额回报。2.文献综述随着P2P网络借贷的日益普及，出现了三大研究方向。第一个研究方向是分析P2P网络借贷出现的原因。第二个研究方向集中于确定筹资成功和违约风险的因素。第三个研究方向是调查在一定水平的风险下P2P网络贷款的表现。同侪团体贷款在当地社区涌现，并吸引了这一领域的研究。康林（1999）开发了一个模型来解释美国和加拿大存在同侪团体小额贷款项目。他发现，同侪团体可以将固定成本强加给企业家，同时最大限度地减少项目的管理成本。阿什达和亚沙（2008）研究了Web2.0技术是如何集成的，以支持高级社会互动和交往，降低P2P网络借贷成本。休姆和莱特（2006）研究了在英国的P2P网络借贷平台Zopa的案例。他们认为，P2P网络借贷的出现是新信息时代的金融业对社会趋势的直接反应，以及对新形式关系的需求。现有的文献中，有的确定了筹资成功和违约风险的决定因素。利用加拿大小额信贷数据，戈麦斯和桑托尔（2003）发现，团体贷款的违约率比传统的个人贷款要低。伊耶等人（2009）的研究显示，通过分析借款人的硬数据和软数据，贷款人可以评估出三分之一的信贷风险。林等人（2013）分析了社会关系在评估信用风险中的作用，发现强大的社交网络关系是决定借款成功和降低违约风险的重要因素。林等人（2013年）进一步指出，申请人的朋友关系可能会增加筹资成功的可能性，降低贷款利率，而且在Prosper上，这些借款人的事后违约率较低。弗里德曼和吉恩（2014）也研究了社会关系在确定贷款资金中的重要性。结果表明，具有社会关系的借款人更有可能获得贷款资金，且利率较低。然而，他们也发现这一迹象，即借款人参与社交网络，对贷款人也有风险。其他一些研究探讨了借款人的某些特征和个人信息是否决定了筹资成功和违约风险。赫斯坦恩等人（2008）表示，借款人的财务实力、上市和宣传工作以及个人特征，影响到筹资成功的可能性。杜阿尔特等人（2012）的研究进一步认为，看起来更值得信赖的借款人信用评分较高，往往筹资成功可能性更高，违约率更低。拉里莫尔等人（2011）表明，借款人使用扩展叙述、具体描述和定量单词，对筹资成功有积极的影响。然而，个性化的个人资料或贷款理由对筹资成功有负面影响。邱等人（2012）进一步揭示，除了个人信息和社会资本外，借款人设定的其他变量，包括贷款金额、可接受的最高利率和贷款期限，都显著影响了筹资的成败。加拉克等人（2011）进一步表明，与团体借款人相比，贷款人更倾向于个人借款人，以及社交上接近自己的借款人。他们还发现贷款人倾向于在性别、职业和名字的首字母上更像自己的借款人。更有趣的是，冈萨雷斯和洛雷罗（2014）也有类似的发现：（1）当把外表年龄视为其能力的体现时，吸引力对贷款成功没有影响；（2）当贷款人和借款人的性别相同时，吸引力可能导致贷款失败（即“红颜祸水”效应）；（3）贷款成功对贷款人和借款人的相对年龄与吸引力很敏感。赫斯坦恩等人（2011）发现，贷款拍卖中的羊群效应与其后续表现呈正相关，即借款人是否按时还钱。3.数据本部分首先对贷款申请人的资料进行了描述，然后是根据贷款目的、信用等级及贷款状况的贷款分配，最后是贷款申请人的详细描述统计。本研究利用了借贷俱乐部上的自2007年5月至2012年6月的61451笔贷款申请，这些数据是从网站获取的。在研究期间，借贷俱乐部贷给借款人约7.13亿美元。为了研究