《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）》请在三一办公上搜索。
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,
17、loan-to-incomeratios,andmonthlypayment-to-incomeratiosincreaseby1.3%,1.3%,and1.1%,respectively,duringsunnyhours.Combined,theseresultssuggestthatsentimentoperatesthroughboththeextensiveandintensivemargins.Theabovefindingsholdaftertheinclusionofcounty-by-weekfixedeffects,whichabsorbweeklyvariationinec
18、onomicconditionsspecifictoeachcounty,aswellascreditrating,loanpurpose,hour,andday-of-weekfixedeffects,whichabsorbvariationacrossborrowercreditquality,loantype,time-of-day,andweekday,respectively.Theanalysesalsocontrolforawiderangeofborrowers,characteristics,suchasemploymentduration,incomelevel,prior
19、platformexperience,andparticipationontheplatformasalender.Assuch,Weprovidenovelcausalestimatesofabehavioralcreditdemandchannel,augmentingrecentstudiesthathavemostlyfocusedontheimplicationsofbehavioralfactorsforcreditsupply,includingpersonalconnections(Engelberg,Parsons,andYao,2012),theperceptionofbo
20、rrowertrustworthiness(Duarte,Siegel,andYoung,2012),andmostrelatedtoourstudy,sunshine-inducedsentiment(Cortesetal.,2016). A related literature examines the effect of sunshine-induced sentiment on other consumer decisions such as car choice (Busse et al., 2015) housing prices (Hu and Lee, 2020), and c
21、redit card spending (Agarwal et al., 2020).Incontrast,wefocusoncreditdemandinasettingthatholdsconstantcreditsupplyandeconomicconditions.Ourfinding,thatsentimenthasconsiderableimplicationsforcreditdemandintheFinTechconsumerloanmarketplace,differsfrompriorevidencethatsentimentdoesnotaffectcreditdemand
22、inmoretraditionalcreditmarkets(e.g.,Cortesetal.,2016).ThesefindingshighlighttheroleOftraditionalloanmarketfeatures,suchasliveinteractionswithloanofficers,inmitigatingtheimpactofsentimentoncreditdemand.Second,wefindthatloanapplicationsinitiatedonsunnyhoursaresignificantlymorelikelytobecharged-offcomp
23、aredtothoseinitiatedonovercasthoursduringthesameweekinthesamecounty.Inparticular,loanapplicationsinitiatedduringsunnyhoursare0.39percentagepointsmorelikelytobecharged-off,or1.49%relativetothesamplestandarddeviation,comparedtothoseinitiatedduringcloudyhours.Thesefindingsshowthatsentimenthasrealeffect
24、sonhouseholdsfinancialoutcomes.Third,wefindconsiderabledemographicdifferencesintheeffectsofsentimentoncreditoutcomesacrossincomegroups.Specifically,wefindthatloansinitiatedbylow-incomeindividualsduringsunnyhoursareroughly1.4percentagepointsmorelikelytobecharged-off,orabout5.3%relativetothesamplestan
25、darddeviation,comparedtothoseinitiatedduringcloudyhours.Incontrast,wedonotfindstrongsentimenteffectsforhigh-incomeborrowers.Theresultssuggestthatlow-incomeborrowersarelesscapableofbearingthefinancialburdenofsentiment-drivenloans,andsubsequentlyexperiencenegativeeconomicconsequences?Fourth,weinvestig
26、atetheroleofpreviousexperienceandcooling-offperiodsinmitigatingtheeffectofsentimentoncreditdemand.Consumerprotectionadvocatespointtobehavioralresearchtojustifyregulationsinfinancialmarkets.Onesuchregulationisatcooling-offperiod,whichallowsborrowerstowithdrawfromfinancialcontractswithinacertaintimewi
27、ndow.ThalerandSunstein(2008)notethatcooling-offperiods“makebestsense,andtendtobeimposed,whentwoconditionsaremet:(a)peoplemaketherelevantdecisionsinfrequentlyandthereforelackagreatdealofexperienceand(b)emotionsarelikelytoberunninghigh.”ConsistentwiththeThalerandSunstein(2008)view,wefindthattheeffecto
28、fsunlightonrequestedloanamounts,loan-to-incomeratios,andcharge-offsisconcentratedinfirst-timeborrowers,anddisappearsforborrowerswithpriorplatformexperience.Inparticular,loanapplicationsinitiatedduringsunnyhoursbyfirst-timeborrowersare1.3%largerandhave1.12-1.4%higherloan-to-incomeratioscomparedtoappl
29、icationsinitiatedduringcloudyhours.Wealsofindsubstantialnegativeoutcomesforinexperiencedborrowers,whoexperiencea0.62percentagepoints,or2.4%,increaseincharge-offratewhentheybegintheirapplicationduringsunnyhours.Incontrast,sunny-hourapplicationsinitiatedbyexperiencedborrowersareindistinguishablefromcl
30、oudy-hourapplications.3Theseindividualsmayalsobetheleastfinanciallyliterateandhencemostsusceptibletosentiment(see,i.e.,Campbell,2(X)6;ThalerandSunstcin,2008;LusardiandMitchcll,2014;RuandSchoar,2016).4Wealsofindthatexperiencedapplicantsareconsiderablymorelikelythanfirst-timeapplicantstowithdrawasunny
31、-hourloanlistingbeforeaccessingthefunds.Whileexperiencedborrowersare1.78%morelikelytowithdrawasunny-hourloanapplicationcomparedtoacloudy-hourloanapplication,first-timeborrowersarenoteconomicallyorstatisticallysignificantlymorelikelytowithdrawsuchapplications.Further,experiencedapplicantswhosepreviou
32、sapplicationwasinitiatedduringasunnyhouraremorelikelytowithdrawacurrentloanlistingcomparedtothosewhosepreviousapplicationwasinitiatedduringacloudyhour.Assuch,ourresultssuggestthatindividualsIeamfrompreviousexperience,andthatmandatingacooling-offperiodprovidesbenefitsintheconsumercreditmarketbypotent
33、iallyprotectinghouseholdsfromirrational,sentiment-baseddecisions.1.astly,wefindthatsentimentinfluencesthecompositionofloans.Localsunshineincreasesthedemandfordiscretionaryloanssuchasthoseforlargepurchases,vacations,orhomeimprovementby1.1%.Incontrast,itdoesnotaffectbusinessloanapplicationsandreducest
34、hedemandfordebtconsolidationby1.63%.Theseresultsareconsistentwithashifttoward“impulse”creditexpansionforhouseholdswhensentimentishigh.Thiseffect,however,doesnotspillovertobusinesscreditdemand.Thesefindingscomplementresearchinexperimentaleconomicsthatstudiesconsumptioncompositioninconsumermarkets(see
35、,e.g.,Busseetal.,2015).OurpapercontributestothegrowingliteratureontherealeffectsofFinTech.Onthepositiveside,technologymayimprovefinancialeducation(Breza,Kanz,andKIapper,2020),enhanceconsumption-smoothingandrisksharing(JackandSuri,2014;Suri,2017),increasefinancialattention(StangoandZinman,2014;Karlan
36、,Morten,andZinman,2017;Bursztyn,2019;Medina,2021),orrelieveinformationfrictions(Carlin,Olafsson,andPagel,2023).SeveralstudiesspecificallyexaminefutureoutcomesOfFinTechborrowersandfindmixedresults.Balyuk(2023)5findsthatFinTechborrowingprovidesinformationspilloversthatfacilitatefuturecreditaccessfromb
37、anks.Conversely,Chavaetal.(2021)andDiMaggioandYao(2021)findnegativelongtermconsequencesofFinTechborrowingintermsoflowercreditscores,highercostsofcredit,andhigherdefaultrates.WangandOverby(2022)findthatFinTechborrowersoverconsumeloansfrommarketplacelenders,leadingtobankruptcy.Ourresultssuggestthatthe
38、easeofaccessingconsumercreditheightenstheeffectoftransitorysentimentonfinancialdecisions,potentiallyleadingtooverconsumptionofcreditandnegativefutureoutcomes.Ourpaperalsocontributestotheliteratureonhouseholdparticipationindebtmarkets.Priorstudiesrelyprimarilyonsurveys,andprovideevidenceonsocio-demog
39、raphicvariables,economicvariables,anddeviationsfromoptimalchoice(e.g.,CoxandJappeIli,1993;DucaandRosenthaL1993;Gropp,Scholz,andWhite,1997;LeaandWebley,1995;Leece,2000;GrahamandIsaac,2002;Karlsson,Dellgran,Klingander,andGarlin,2004;Brown,Taylor,andWheatleyPrice,2005;Easterlin,2005;Magri,2007;Siemens,
40、2007;DelRioandYoung,2006,2008;Ranyardetal.,2006;MeierandSprenger,2007,2010;Rohde,2009;Etzioni,2010.).Weaddtothisliteraturebyprovidingevidencefrommicro-levelobservationaldataonhighfrequencycreditdemanddecisionsofhouseholds.Assuch,ourworkisrelatedtoBen-DavidandBos(2021),whouseobservationalSwedishdata,
41、andfindthatanincreaseintheavailabilityofliquorincreasescreditdemand,default,welfaredependence,andcrime.1.astly,wealsoaddtothegrowingliteratureontheroleofsentimentinfinancialmarkets.Earlyworkinthisfieldexaminestheeffectofsunshine-inducedmoodonthestockmarketandontradingbehavior(Suanders,1993;Kamstra,K
42、ramer,andLevi,2003;HirshleiferandShumway,2003;Goetzmannetal.,2015).Weaddtothisliteraturebystudyinghouseholdcreditdemand.62. InstitutionalDetailsandDataThissectiondescribestheinstitutionaldetailsoftheloanapplicationprocessofthemarketplacelender(ProsperMarketplace),aswellasthetheoreticalmotivationfors
43、unshineasamoodprimer.Additionally,thissectiondetailsthedatasourcesandvariablesWeuseinthisstudy.2.1. ProsperMarketplaceTheempiricalanalysesfocusonFinTechconsumerloanapplicationsfromtheuniverseOflistingsprovidedbyProsperMarketplace,thesecondlargestonlineconsumerlenderintheUnitedStates. Prosper has iss
44、ued over $21 billion in loans to over 1.2 million people since 2005.Toinitiatealoanapplication,individualsstatetheirdesiredloanamount(upto$40,000)andthepurposeoftheloan. Loan purpose is separated into 10 main categories. The most common loan purpose is debt consolidation, whichmakes up roughly 70% o
45、f the applications in our data.Theapplicantmustthenprovidehername,address,anddateofbirth,alongwithheroccupation,income,homeownershipstatus,employmentstatus,andothergeneralcredithistoryterms.Theplatformthenperformsasoftcreditinquiry,andgeneratesasetofpotentialloanpackageswitheithera3-or5-yeartermfrom
46、whichtheborrowerchooses.Oncetheapplicantacceptsthedesiredloanterms,theplatformconductsahardcreditinquiry,andusesaproprietarycredit-scoringmodeltoevaluatetheriskinessofeachloan.Theplatformmayalsorequestadditionaldocumentationtoverifycertainitemsontheborrowerscreditprofileandprovidedinformation.Thelis
47、tingthengoespublicontheProsperplatformandisavailabletobefundedbyinvestors.Theentireapplicationprocesstakesonlyminutes,andtheplatformapprovesmostloansandprovidesfundingwithin1day.Upongoingpublic,alistinghastwoweekstobefunded,andmaybefundedbyindividualsorinstitutionalinvestors.Inpractice,theloansareov
48、erwhelmingfundedbyinstitutionalinvestorsusingpassivemeansbasedonobservablequantitativevariablesfromthecreditbureau(BalyukandDavydenko,2019).Atanytimepriortofunding,aborrowermaywithdrawherapplicationatnocost.Investorsobservetherequestedamount,loanpurpose,andtheborrower,screditcharacteristics,butnotth
49、epersonalcharacteristicsoftheborrower,suchastheborrowers*preciselocation.Investorsthendecidewhethertofundtheloanpartiallyorcompletely.Oncefundingiscomplete,contingentontheborrowersapproval,theloanisoriginated,andthefundsaredepositedintheaccountoftheborrower.Borrowersincurfeesandnotificationsofpastdueaccountsontheircreditreportsiftheyareunabletomakethenecessaryloanpayments.Iftheborrowermissesfiveconsecutivemonthlypayments,theloanisconsidered“charged-off,“andtheentireloanbalanceisdueimmediately(
