情绪波动与货币：金融科技与家庭信贷-英.docx

《情绪波动与货币：金融科技与家庭信贷-英.docx》由会员分享，可在线阅读，更多相关《情绪波动与货币：金融科技与家庭信贷-英.docx（46页珍藏版）》请在第壹文秘上搜索。

1、MoodSwingsandMoney:TheRoleofFinancialTechnologyinHouseholdCreditDemandRanDuchin,PaulFreed,andJohnHackney*December2023AbstractFintechlendingallowsborrowerstoapplyforloansanytimeandfromanywhere,completetheirapplicationswithinminutes,andobtainimmediatecreditdecisions.Assuch,transientmoodswingsthatwould

2、bemitigatedinatraditionalloansettingcanplayanimportantroleinmodernhouseholdcreditdemand.Usinghourlyfluctuationsinlocalsunshineasaninstrumentforsentiment,wefindthatpositivesentimentleadstohigherloandemandbothattheextensivemargin(moreloanapplications)andtheintensivemargin(higherloanamountsandloan-to-i

3、ncomeratios).Theeffectsleadtohigherdefaultrates,especiallyforlower-incomeandinexperiencedborrowers.Wealsofindevidenceconsistentwithself-correctiveactionswhereindividualslaterwithdrawIheirapplications,suggestingthatucooling-off,periodscanbeaneffectiveconsumerprotectionmechanism.Overall,weprovidesomeo

4、fthecleanestestimatestodatethatsentimentaffectsthedemandforconsumercredit.KeyWords:FinTech,ConsumerCreditDemand,Sentiment,MarketplaceLending,DefaultJELClassifications:D12,D14,G4,G21,G23,033Contact:RanDuchin,CarrollSchoolofManagement,BostonCollege,e-mail:duchinr(5)bc.edu;PaulFreed,DarlaMooreSchoolofB

5、usiness,UniversityofSouthCarolina,e-mail:Paul.Freedgrad.moore.sc.edu：JohnHackney,DarlaMooreSchoolofBusiness,UniversityofSouthCarolina,e-mail:iohn.hackneymoore.sc.edu.WethankseminarparticipantsattheUniversityofWashington,OldDominionUniversity,andtheUniversityofSouthCarolinaforhelpfulcomments.1. Intro

6、ductionTheadventoffinancialtechnologyhasfundamentallychangedthelandscapeofhouseholds,financialdecision-making.Borrowersononlinemarketplaceplatformscanapplyforloansfromthecomfortoftheirhomes,dayornight,completetheirloanapplicationswithinminutesusingtheirsmartphoneorcomputer,andneverspeaktoabankeroral

7、oanofficer.Suchdevelopments,inturn,canhaveamaterialeffectonoverallfinancialdecision-making.Attheextensivemargin,lowertransactioncostscanincreasetheconsumptionofcredit.Theunsecuredconsumerloanmarkethasgrowndramaticallyinthelastdecade,from$57.7billionin2009to$156billionin2019,withmarketplacelendersres

8、ponsibleforroughly40%ofthemarket.Based on TransUnion data - see:Altheintensivemargin,theycanaffectthequalityofcreditdecisionsandsubjectthemtoinfluencesthatmoretraditionalsettingswouldmitigate.Inthispaper,Weusemicro-leveldatafromanonlinemarketplacelendingplatformtostudytheroleofsentimentandfinancialt

9、echnologyinhouseholds,creditdemand.Theanalysesutilize1.4milliontimestampedloanapplicationsfrom2007-2021tostudytheeffectsoftransitoryemotionalstatesonhouseholds,borrowingdecisions,therealconsequencesofthosedecisions,andtheefficacyoffeaturessuchastcooling-ofP,periodsinmitigatingtheemotionaleffects.Asa

10、sourceofexogenousvariationinconsumers,sentimentthatmatchesthehighfrequencyofloanapplications,weexploithourlyvariationinlocalsunshineacross2,482countiesduringtheperiod2007-2021.Thisapproachisgroundedinpriorevidenceontheeffectofsunshineonanagentsmoodfrompsychology(SchwarzandClore,1983),experimentaleco

11、nomics(Bassi,Colacito,andFulghieri,2013),andnancialmarkets(HirshleiferandShumway,2003;Goetzmann,Kim,Kumar,andWang,2015).Akeyempiricalchallengeistoseparatetheeffectofsentimentonhouseholds,borrowingdecisions,orcreditdemand,fromitseffectoncreditsupplyandlocaleconomicconditions.Indeed,priorstudieshavesh

12、ownthatsunshineaffectsbothcreditsupply(Cortesetal.,2016)andeconomicexpectations(Chhaochhariaetal.,2019).Ourempiricalsettinghasseveralfeaturesthatallowustoovercomethischallenge.First,thedatacontainloanapplicationsirrespectiveoftheireventualoriginationorfundingstatus,thuscapturinghouseholds5creditdema

13、ndratherthancreditsupply.Second,thetestspecificationsmatcheachapplicationsgranulartimestampwithhourlyvariationinsunshinewithinacounty-week,thusholdingconstantlocaleconomicconditionsandremovingseasonalvariationinsunshineforagivencounty.Third,bydesign,allcreditdecisionsontheonlinemarketplacelendingpla

14、tformarebasedonanalgorithmiccreditmodel,andtheinvestorsarenonlocalandinstitutional.Assuch,thesupplyofcreditontheplatformisunrelatedtovariationinlocalsunshine.Weconfirmthathourlyvariationinsunshinedoesnotaffectcreditsupplybystudyingloanpricing,riskassessment,andfunding.Consistentwithouridentifyingass

15、umption,wefindthatsunshineisUncorrelatedwithloaninterestrates,theplatfrm,sestimatedlossrate,ortheproportionoftheapplicationthatisfunded.Theseresultssuggestthatvariationinlocalsentimentdoesnotaffectloanoriginationorloanterms,norisitaccountedforbytheplatformorinvestors.Ourmainfindingscanbesummarizedas

16、follows.First,positivesentiment,attributabletohourlyvariationinlocalsunshine,correspondstohighercreditdemandbothattheextensiveandintensivemargins.Attheextensivemargin,wefindthatthenumberofapplicationsis2%higherduringsunnyhourscomparedtocloudyhours.Attheintensivemargin,wefindthatrequestedloanamounts,loan-to-incomeratios,andmonthlypayment-to-incomeratiosincreaseby1.3%,1.3%,and1.1%,respectively,duringsunnyhours.Combined,theseresultssuggestthatsentimentopera