情绪波动与货币：金融科技与家庭信贷-英.docx

MoodSwingsandMoney:TheRoleofFinancialTechnologyinHouseholdCreditDemandRanDuchin,PaulFreed,andJohnHackney*December2023AbstractFintechlendingallowsborrowerstoapplyforloansanytimeandfromanywhere,completetheirapplicationswithinminutes,andobtainimmediatecreditdecisions.Assuch,transientmoodswingsthatwouldbemitigatedinatraditionalloansettingcanplayanimportantroleinmodernhouseholdcreditdemand.Usinghourlyfluctuationsinlocalsunshineasaninstrumentforsentiment,wefindthatpositivesentimentleadstohigherloandemandbothattheextensivemargin(moreloanapplications)andtheintensivemargin(higherloanamountsandloan-to-incomeratios).Theeffectsleadtohigherdefaultrates,especiallyforlower-incomeandinexperiencedborrowers.Wealsofindevidenceconsistentwithself-correctiveactionswhereindividualslaterwithdrawIheirapplications,suggestingthatucooling-off,periodscanbeaneffectiveconsumerprotectionmechanism.Overall,weprovidesomeofthecleanestestimatestodatethatsentimentaffectsthedemandforconsumercredit.KeyWords:FinTech,ConsumerCreditDemand,Sentiment,MarketplaceLending,DefaultJELClassifications:D12,D14,G4,G21,G23,033Contact:RanDuchin,CarrollSchoolofManagement,BostonCollege,e-mail:duchinr(5)bc.edu;PaulFreed,DarlaMooreSchoolofBusiness,UniversityofSouthCarolina,e-mail:Paul.Freedgrad.moore.sc.edu：JohnHackney,DarlaMooreSchoolofBusiness,UniversityofSouthCarolina,e-mail:iohn.hackneymoore.sc.edu.WethankseminarparticipantsattheUniversityofWashington,OldDominionUniversity,andtheUniversityofSouthCarolinaforhelpfulcomments.1. IntroductionTheadventoffinancialtechnologyhasfundamentallychangedthelandscapeofhouseholds,financialdecision-making.Borrowersononlinemarketplaceplatformscanapplyforloansfromthecomfortoftheirhomes,dayornight,completetheirloanapplicationswithinminutesusingtheirsmartphoneorcomputer,andneverspeaktoabankeroraloanofficer.Suchdevelopments,inturn,canhaveamaterialeffectonoverallfinancialdecision-making.Attheextensivemargin,lowertransactioncostscanincreasetheconsumptionofcredit.Theunsecuredconsumerloanmarkethasgrowndramaticallyinthelastdecade,from$57.7billionin2009to$156billionin2019,withmarketplacelendersresponsibleforroughly40%ofthemarket.Based on TransUnion data - see:Altheintensivemargin,theycanaffectthequalityofcreditdecisionsandsubjectthemtoinfluencesthatmoretraditionalsettingswouldmitigate.Inthispaper,Weusemicro-leveldatafromanonlinemarketplacelendingplatformtostudytheroleofsentimentandfinancialtechnologyinhouseholds,creditdemand.Theanalysesutilize1.4milliontimestampedloanapplicationsfrom2007-2021tostudytheeffectsoftransitoryemotionalstatesonhouseholds,borrowingdecisions,therealconsequencesofthosedecisions,andtheefficacyoffeaturessuchastcooling-ofP,periodsinmitigatingtheemotionaleffects.Asasourceofexogenousvariationinconsumers,sentimentthatmatchesthehighfrequencyofloanapplications,weexploithourlyvariationinlocalsunshineacross2,482countiesduringtheperiod2007-2021.Thisapproachisgroundedinpriorevidenceontheeffectofsunshineonanagent'smoodfrompsychology(SchwarzandClore,1983),experimentaleconomics(Bassi,Colacito,andFulghieri,2013),andnancialmarkets(HirshleiferandShumway,2003;Goetzmann,Kim,Kumar,andWang,2015).Akeyempiricalchallengeistoseparatetheeffectofsentimentonhouseholds,borrowingdecisions,orcreditdemand,fromitseffectoncreditsupplyandlocaleconomicconditions.Indeed,priorstudieshaveshownthatsunshineaffectsbothcreditsupply(Cortesetal.,2016)andeconomicexpectations(Chhaochhariaetal.,2019).Ourempiricalsettinghasseveralfeaturesthatallowustoovercomethischallenge.First,thedatacontainloanapplicationsirrespectiveoftheireventualoriginationorfundingstatus,thuscapturinghouseholds5creditdemandratherthancreditsupply.Second,thetestspecificationsmatcheachapplication'sgranulartimestampwithhourlyvariationinsunshinewithinacounty-week,thusholdingconstantlocaleconomicconditionsandremovingseasonalvariationinsunshineforagivencounty.Third,bydesign,allcreditdecisionsontheonlinemarketplacelendingplatformarebasedonanalgorithmiccreditmodel,andtheinvestorsarenonlocalandinstitutional.Assuch,thesupplyofcreditontheplatformisunrelatedtovariationinlocalsunshine.Weconfirmthathourlyvariationinsunshinedoesnotaffectcreditsupplybystudyingloanpricing,riskassessment,andfunding.Consistentwithouridentifyingassumption,wefindthatsunshineisUncorrelatedwithloaninterestrates,theplatfrm,sestimatedlossrate,ortheproportionoftheapplicationthatisfunded.Theseresultssuggestthatvariationinlocalsentimentdoesnotaffectloanoriginationorloanterms,norisitaccountedforbytheplatformorinvestors.Ourmainfindingscanbesummarizedasfollows.First,positivesentiment,attributabletohourlyvariationinlocalsunshine,correspondstohighercreditdemandbothattheextensiveandintensivemargins.Attheextensivemargin,wefindthatthenumberofapplicationsis2%higherduringsunnyhourscomparedtocloudyhours.Attheintensivemargin,wefindthatrequestedloanamounts,loan-to-incomeratios,andmonthlypayment-to-incomeratiosincreaseby1.3%,1.3%,and1.1%,respectively,duringsunnyhours.Combined,theseresultssuggestthatsentimentopera