监管科技与银行流动性风险：来自中国上市银行的证据

AppliedEconomicsLetters

ISSN:(Print)(Online)Journalhomepage:

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Internalregulatorytechnology(RegTech)andbank

liquidityrisk:evidencefromChineselistedbanks

YufeiXia,XuyanLu,ZimingHao&HuiyiShi

Tocitethisarticle:YufeiXia,XuyanLu,ZimingHao&HuiyiShi(09Sep2024):Internal

regulatorytechnology(RegTech)andbankliquidityrisk:evidencefromChineselistedbanks,AppliedEconomicsLetters,DOI:

10.1080/13504851.2024.2400310

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APPLIEDECONOMICSLETTERS

/10.1080/13504851.2024.2400310

checkfrupdates

Internalregulatorytechnology(RegTech)andbankliquidityrisk:evidencefromChineselistedbanks

Yufei

Xiaa

,XuyanLu

b

,ZimingHao

b

andHuiyiShi

a

aBusinessSchool,JiangsuNormalUniversity,Xuzhou,Jiangsu,PRChina;bSino-RussiaCollege,JiangsuNormalUniversity,Xuzhou,Jiangsu,PRChina

KEYWORDS

Regulatorytechnology;bank;liquidityrisk;text-mining

JELCLASSIFICATION

G21;G28;O14

ABSTRACT

Weexaminetheeffectofinternalregulatorytechnology(RegTech)onbankliquidityrisk.BasedonadatasetofChineselistedbanksfrom2015to2022,weinitiallyemployatext-miningmethodtobuildtheinternalRegTechindexfromthebanks’annualreports.WesubsequentlyrevealasignificantmitigationeffectofinternalRegTechonbankliquidityrisk.Thefindingremainsrobustafteralleviatingtheendogeneityissueandunderalternativeproxyofthedependentvariableanddifferentsampleperiods.ApossiblechannelisthatinternalRegTechbooststheregulatorycapabilityandmitigatesbanks’riskybehaviours.Moreover,ourresultsdemonstratesomehetero-geneitiesacrossRegTechsubindices.Thecomplianceandtechnologicalfoundationssubindicesaremoreprofoundinaffectingbankliquidityrisk.ThefindingsprovidevaluableimplicationsforfinancialregulatorsregardingtheadoptionofRegTech.

I.Introduction

Bankliquidityriskreferstoabank’sinabilitytomeetitspaymentobligationsasliabilitiesfalldue.Thetraditionalbankingtheoryhighlightsthatmaturitytransformationenhancesbanks’profitabilitybutexposesbankstoathreatofliquidityriskandbankruns.ThedownfallofSiliconValleyBankshockedtheglobalfinancialsystem,andoneofthemajorreasonswaspoorliquiditymanagement.Currentresearchhighlightsthedeterminantsofliquidityrisk,includ-ingbank-levelfactors(Abdul-Rahman,Sulaiman,andMohdSaid

2018

)andmonetarypolicy(Nguyen,Nguyen,andDuong

2023

).Moreover,financialregulationisadirectsolutiontobankliquid-ityrisk(Raz,McGowan,andZhao

2022

).

Thestringentregulatorystandardsandtraditionalregulatorymodelshaveposednumerouscompliancecoststobanksandhavelongbeendebatedbecauseofregulatorylagandinconsistency,whichspurtheemergenceofregulatorytechnology(RegTech).RegTechdescribedtechnologyusageinregulation,monitoring,reporting,andcompliance(Buckleyetal.

2020

).ThedefinitionfallsintotheinternalRegTechpoweredbytheregulatedentitiestoenhancecomplianceeffectiveness(Teichmann,

Boticiu,andSergi

2023

)andimproveriskmanage-ment(Chaoetal.

2022

).

RegTechcanalsobedrivenbyregulatoryautho-rities,referredtoasexternalRegTech,whichimpliesadoptingtechnologyinfinancialregulationforhigherregulatoryefficiencyandcapability.ThereisapotentialinteractionbetweeninternalandexternalRegTech.Theoretically,internalRegTechcanmitigateliquidityriskintwoways.MonitoringandKnow-Your-Customersystemscandirectlyidentifyandprovideanearlywarningofpotentialrisks.Indirectly,theinternalRegTechestablishesanefficientinformationandreportingsystem,whichboostsautomaticandtimelyregula-tion.However,thereisanoticeableresearchgapregardingtheroleofinternalRegTechonbankliquidityrisk.Thispaper,therefore,aimstobridgetheknowledgegap.

Themarginalcontributionofthispaperisthree-fold.First,weenterintothedebateonthenexusbetweenfinancialregulationandbankliquidityriskandprovidesolidevidenceontherisk-mitigationeffectofinternalRegTech.Second,wearethefirsttomeasurethebank-levelRegTechandempiricallyexplorethenexus.Theempiricalresultscan

CONTACTYufeiXia6020180093@自BusinessSchool,JiangsuNormalUniversity,Xuzhou,Jiangsu221116,PRChinaSupplementaldataforthisarticlecanbeaccessedonlineat

/10.1080/13504851.2024.2400310

.

©2024InformaUKLimited,tradingasTaylor&FrancisGroup

2四Y.XIAETAL.

supplementtheabundanttheoreticalanalysisofRegTechonriskmanagement(Buckleyetal.

2020

;Chaoetal.

2022

).Finally,weperformamechanismanalysisofhowinternalRegTechaffectsbankliquid-ityrisk.

II.Dataandvariables

Sample

Weselectedall41commercialbankslistedinthe

ChineseA-sharemarketfrom2015to2022asthesample.Thesebanksaccountedforapproximately84%ofthetotalassetsofthewholeChinesebank-ingindustryin2022.ThedatasetwascollectedfromtheCSMARdatabase,thecommercialbanks’annualreport,andtheStateAdministrationforFinancialRegulationwebsite.

Variables

Dependentvariable

WefollowGhenimi,Chaibi,andOmri(

2017

)toemploytheliquidityratio(LDR)asaproxyofbankliquidityrisk,whichiscomputedas:

whereLAandLLdenotetheliquidassetsandliquidliabilities,respectively.WetakethenaturallogarithmoftheLDR(LnLDR).

Coreexplanatoryvariable

TheinternalRegTechindexisutilizedtomea-suretheapplicationofRegTechinbanks.Weemployatext-miningmethodtobuildthe

internalRegTechindexfromthebanks’annualreports.Brieflyspeaking,weselectaseriesofRegTech-relatedkeywordsandcountthewordfrequencyofthesekeywords.Thenaturalloga-rithmofthefrequencyisemployedasaproxyoftheinternalRegTech(LnRegTech)(seedetailsinAppendixA).

Controlvariables

WefollowAbdul-Rahman,Sulaiman,andMohdSaid(

2018

)andRaz,McGowan,andZhao(

2022

)toemployreturnonassets(ROA),banksize(SIZE),capitaladequacyratio(CAR),netinterestmargin(NIM),non-interestincomeratio(NIIR),netprofitmarginonincome(NPGOI),administrativeexpensesonincome(AEGOI),andtotalassetsturnover(TAT).Thesummarystatisticsanddefinitionsaredisplayedin

Table1

.

Econometricmodel

ToexaminetheimpactofinternalRegTechonbankliquidityrisk,weconstructthefollowingtwo-wayfixed-effectmodel:

LnLDRi,t=uo+u1lnregtechi,t+a2controli,t+i+t+ei,t,

(2)

whereiandtdenotethei-thbankandthet-thyear,respectively.TheControlindicatesthecontrolvariables.δandμimplythebankandyearfixedeffects,respectively.εistheerror

term.

Table1.Summarystatistics.

Variable

Obs

Mean

SD

Min

Max

Definition

LnLDR

326

4.737

0.928

1.386

5.720

,、ln100×

LnRegTechROA

326

326

5.383

0.008

0.709

0.002

2.890

0.004

7.069

0.016

Thenaturallogarithmofwordfrequencyoftheinternal

RegTech-relatedkeywords

Netprofit/Totalassets

SizeCAR

326

326

1.44313.789

0.0261.582

1.40110.940

1.49418.420

Naturallogarithmoftotalassets(inhundredbillion)Capital/Risk-weightedassets

NIM

326

2.226

0.401

1.520

3.460

100×Netinterestincome/Interest-earningassets

NIIR

326

21.122

10.201

0.679

44.233

100×Non-interestincome/Netincomefromoperations

NPGOI

326

32.637

6.306

14.771

44.570

100×Netprofit/Revenue

AEGOI

326

30.272

6.137

20.644

60.807

100×AdministrativeExpenses/Netincomefromoperations

TAT

326

0.027

0.004

0.018

0.039

Revenue/Totalassets

Thecontinuousvariablesarewinsorizedatthe1%and99%levels.Obs=thenumberofobservations.SD=standarddeviation.Min=theminimumvalue.Max=themaximumvalue.

APPLIEDECONOMICSLETTERS四3

III.Results

Resultsofbaselineregression

Table2

displaystheresultsofbaselineestima-tions.ThecoefficientsofLnRegTechinallthecolumnsaresignificantlypositiveata5%sig-nificancelevel,suggestingthatadoptinginter-nalRegTechcansignificantlyreducebankliquidityrisk.Column(2)indicatesthatifLnRegTechincreasesbyonestandarddeviation,theLnLDRwillincreaseby26.45%,roughlyconstituting5.58%(=0.373×0.709/4.737)ofthemeanvalueoftheLnLDR.TheresultsofColumns(3)to(6)indicatethatourfindingsareinsensitivetodifferentclusteredstandarderrors,whichpartiallyshowstherobustnessofourconclusion.TheresultsareconsistentwithLiu,Wang,andZhang(

2024

).AlthoughsomepriorstudieshaverevealedthatFinTechreducedbanks’liquidity(Tang,Hu,etal.

2024

)andspurredcorporaterisk-taking(Tang,Hou,etal.

2024

),ourfindingssupporttheuseofRegTechtoalleviatethepotentialeffectsofFinTechonfinancialinstability.

Robustnesschecks

Endogeneity

WeinitiallyfollowBorusyakandHull(

2023

)toemployaninstrumentalvariable(IV)approachandconstructaBartikIV(orShift-ShareIV),whichistheproductofthelaggedfirst-orderinter-nalRegTechindexLnRegTechi;t—1(Share)andthefirst-ordertimedifferenceofinternalRegTechindex,ΔLnRegTecht;t—1(Shift).BartikIVinherentlyusestheweightedaverageofShareandShifttocreateapredictionofLnRegTechforeachobserva-tion.BartikIVispotentiallyvalidsinceitisuncor-relatedwiththeresidualsbecausetheinitialshareandcommonshockinBartikIVareexogenousvariablesnotdrivenbythecurrentbank’sdecision-making,whichmeetstheindependenceassump-tion.Moreover,asapredictionofLnRegTech;theBartikIVishighlycorrelatedwiththeinternalRegTechlevel,whichsatisfiestherelevanceassumption.

Table3

displaystheresultsofthetwo-stageleastsquaresregression.Column(1)showsthattheBartikIVissignificantlypositiveata1%level.Meanwhile,thesecond-stageregressionresultsare

Table2.Baselineresults.

Variables

(1)

(2)

(3)

(4)

(5)

(6)

LnLDR

LnLDR

LnLDR

LnLDR

LnLDR

LnLDR

LnRegTech

0.323**

0.373***

0.373***

0.373***

0.373***

0.373**

(0.125)

(0.128)

(0.046)

(0.110)

(0.088)

(0.149)

ROA

−0.611

−0.611**

−0.611

−0.611*

−0.611**

(0.397)

(0.181)

(0.387)

(0.351)

(0.235)

Size

33.134

33.134

33.134

33.134**

33.134

(29.650)

(33.287)

(36.207)

(16.136)

(59.794)

CAR

0.012

0.012

0.012

0.012

0.012

(0.044)

(0.042)

(0.048)

(0.050)

(0.055)

NIM

0.419

0.419***

0.419*

0.419*

0.419

(0.257)

(0.026)

(0.246)

(0.244)

(0.246)

NIIR

0.011

0.011

0.011

0.011*

0.011

(0.008)

(0.005)

(0.008)

(0.005)

(0.009)

NPGOI

0.004

0.004

0.004

0.004

0.004

(0.019)

(0.004)

(0.019)

(0.015)

(0.023)

AEGOI

−0.059***

−0.059***

−0.059***

−0.059***

−0.059**

(0.021)

(0.006)

(0.020)

(0.012)

(0.026)

TAT

−34.278

−34.278***

−34.278

−34.278

−34.278

(32.226)

(3.201)

(32.277)

(27.120)

(27.082)

Constant

2.999***

−43.314

−43.314

−43.314

−43.314*

−43.314

(0.676)

(42.783)

(49.063)

(52.041)

(23.990)

(86.671)

Observations

326

326

326

326

326

326

R-squared

0.486

0.510

0.510

0.510

0.510

0.510

Bankfixedeffect

Yes

Yes

Yes

Yes

Yes

Yes

Yearfixedeffects

Yes

Yes

Yes

Yes

Yes

Yes

Clusteredstandarderror

Bank

Bank

Type

Province×Year

Type×Year

Province×Bank

***,**,and*denotesignificanceatthe1%level,5%,and10%levels,respectively.Clusteredstandarderrorsarereportedinparentheses.Typedenotesthetypeofbank.Provinceimpliestheprovincewheretheheadquartersofthebankislocated.Column(1)excludesthecontrolvariables,andColumn(2)correspondstothemodelspecificationdescribedinEq.(1).Theremainingcolumnsdifferinclusteredstandarderror.

1TheinstitutionalbackgroundofEASTisclarifiedintheAppendixB.

4四Y.XIAETAL.

Table3.Resultsoftwo-stageleastsquares(2SLS)regression.

VARIABLES

(1)

LnRegTech

(2)LnLDR

LnRegTech

0.913***(0.309)

BartikIV

0.409***(0.133)

Controls

Yes

Yes

Bankfixedeffect

Yes

Yes

Yearfixedeffects

Yes

Yes

Observations

285

285

Cstatistic

4.55**

Kleibergen-PaaprkLMstatistic

8.13***

Cragg-DonaldWaldFstatistic

70.31***

***,**,and*denotesignificanceatthe1%level,5%,and10%levels,respectively.Bank-andyear-fixedeffectsareconsidered.Therobuststandarderrorsarereportedinparentheses.Columns(1)and(2)presenttheresultsofthefirstandsecondstagesofthe2SLSregression,withLnRegTechandLnLDRasthedependentvariables,respectively.TherowsCstatistic,Kleibergen-PaaprkLMstatistic,andCragg-DonaldWaldFstatisticdenotetheresultsoftheendogeneity,underidentification,andweakidentificationtests,respectively.

consistentwith

Table2

andconfirmourexpecta-tions.TheCstatisticshowsthatthebank-levelRegTechshouldnotbetreatedasexogenous.Moreover,theKleibergen-PaaprkLMandCragg-DonaldWaldFstatisticssuggestnounderidentifi-cationorweakIVconcerns,whichshowstheIVapproach’srobustness.

WesubsequentlyemployaRegTech-relatedquasi-experiment,namelythepromotionofExaminationandAnalysisSystemTechnology(EAST),

1

toexploretheroleofinternalRegTechonbankliquidityrisk.Wedeterminethesampleperiodfrom2012to2022toensuresufficientpre-

eventobservations.Astaggereddifference-in-difference(DID)approachisemployedtoalleviateendogeneitybiasasfollows:

LnLDRit='o+uEASTit+u2controlit+i+t十Eit,

(3)

whereEASTistheinteractiontermoftimeandtreatmentvariables.

Table4

suggeststhatthecoef-ficientofEASTissignificantlypositiveforallmodelspecifications.Ineconomicterms,theLnLDRincreasedby5.83%(=0.276×1/4.737)afteradoptingEAST,asdisplayedinColumn(2).Theresults,again,agreewiththefindingsinbase-lineregression.Wefurtherperformparalleltrendsandplacebotestsforrobustnesschecks(seeAppendixC).Theresultssupporttheconsistencyofourconclusions.

Alternativeproxyofbankliquidityrisk

Weemploythenaturallogarithmoftheliquiditycoverageratio(LnLCR)asaproxyofthedependentvariable,followingtherequirementofBaselIII.TheestimatedcoefficientsaredisplayedinColumn(5)of

Table4

,suggestinginternalRegTechcansignificantlydecreaseliquidityrisk.Theresultsagainconfirmtherobustnessoftheempiricalresearch.

Table4.Resultsofthedifference-in-difference(DID)approachandotherrobustnesschecks.

(1)

(2)(3)

(4)

(5)

(6)

DIDestimation

Alternativecorevariable

Alternativesampleperiod

VARIABLES

LnLDR

LnLDR

LnLDR

LnLDR

LnLCR

LnLDR

EAST

0.240***

0.276***

0.240*

0.276**

(0.021)

(0.020)

(0.120)

(0.126)

LnRegTech

0.080**(0.037)

0.333**(0.159)

Constant

4.862***

−8.811

4.862***

−8.811

−5.050

−53.440

(0.014)

(27.265)

(0.084)

(37.982)

(19.024)

(57.256)

Controls

No

Yes

No

Yes

Yes

Yes

Observations

439

439

439

439

274

285

R-squared

0.456

0.492

0.492

0.491

0.731

0.503

Bankfixedeffect

Yes

Yes

Yes

Yes

Yes

Yes

Yearfixedeffects

Yes

Yes

Yes

Yes

Yes

Yes

Clusteredstandarderror

Type

Type

Province

Province

Bank

Bank

***,**,and*denotesignificanceatthe1%level,5%,and10%levels,respectively.Bank-andyear-fixedeffectsareconsidered.Typedenotesthetypeofbank.Provinceimpliestheprovincewheretheheadquartersofthebankislocated.EASTrepresentsthepromotionofExaminationandAnalysisSystemTechnology.

EASTisassignedto

1ifthecorre,spondingbankhasbeenincludedintheEA、STand0otherwise.LnLCRindicatesthenaturallogarithmoftheliquiditycoverage

ratio,computedas

LnLCR=ln100×.

2SeeadditionalchanneltestsundertheendogeneityconcerninAppendixD.

APPLIEDECONOMICSLETTERS四5

Table5.Resultsofchanneltestandcross-sectionalanalysis.

(1)

C

(2)

(3)

t

(4)

(5)

(6)

hanneltes

Compliance

Regulation

Technologicalfoundation

VARIABLES

LnAAP

LnAAP

LnAAP

LnLDR

LnLDR

LnLDR

LnRegTech

−0.570***

−0.570**

−0.570***

(0.188)

(0.166)

(0.126)

LnCom

0.366**(0.151)

LnReg

0.216(0.129)

LnTech

0.341***(0.114)

Constant

−185.426*

−185.426

−185.426

−52.531

−31.332

−38.993

(106.822)

(106.790)

(124.584)

(55.182)

(49.070)

(52.205)

Controls

Yes

Yes

Yes

Yes

Yes

Yes

Observations

326

326

326

326

326

326

R-squared

0.781

0.781

0.781

0.510

0.503

0.509

Bankfixedeffect

Yes

Yes

Yes

Yes

Yes

Yes

Yearfixedeffects

Yes

Yes

Yes

Yes

Yes

Yes

Clusteredstandarderror

Bank

Type

Province

Bank

Bank

Bank

***,**,and*denotesignificanceatthe1%level,5%,and10%levels,respectively.Bank-andyear-fixedeffectsareconsidered.Typedenotesthetypeofbank.Provinceimpliestheprovincewheretheheadquartersofthebankislocated.ThecolumnsofCompliance,Regulation,andTechnologicalfoundationrepresenttheresultscorrespondingtothecasesemployingthethreeapplication-scenario-relatedsubindices(i.e.complianceapplication,regulatoryapplication,andtechnologicalfoundationasthecorevariable.ThethreeRegTechsubindicesaremeasuredbythenaturallogarithmofthekeywordfrequencies,denotedasLnCom,LnReg,andLnTech,respectively.

Alternativesampleperiod

TheChineseA-sharemarketexperiencedahistoricfallinJune2015.Thehugemarketfluctuationmayspillovertothebankingsectorandleadtoaliquiditycrisis.We,therefore,removetheperiodoftheextra-ordinaryfall(i.e.theyear2015)andshowtheresultsinColumn(6)of

Table4

.ThesignificantlypositivecoefficientofinternalRegTechagainconfirmstherobustnessofourresults.

Potentialchannels

ThenexusbetweeninternalandexternalRegTechsuggeststhattheformercanboosttheautomaticandtimelyregulationofbanks’behaviours,contri-butingtoenhancedregulatorycapability.Thetigh-tenedregulationandsanctionsmayfurtherdisciplinebanks’risk-taking(Gaoetal.

2020

).

Tovalidateourconjecture,weexaminethe

effectsofinternalRegTechonregulatorycapabil-

ity,proxiedbythenaturallogarithmofadminis-

trativepenalty(LnAAP)inColumns(1)-(3)in

Table5

.TheresultssuggestthatinternalRegTech

significantlydecreasesLnAAPatthe5%level,and

thecoefficientsare−0.570forallcolumns.

2

ApossiblereasonisthatinternalRegTechcan

helptheregulatorsaccuratelyidentifybanks’mis-

conductanddeterbanks’riskybehaviours.

Cross-sectionalanalysis

InternalRegTechmayplaydiverserolesandimpactsindifferentapplicationscenarioswithinthebankingindustry.WefurtherseparatetheinternalRegTechindexintothreesubindicesbasedontheapplicationscenarios:complianceapplication,regulatoryapplication,andtechnolo-gicalfoundation.Columns(4)to(6)in

Table5

displaytheresultsforRegTechsubindices,reveal-ingthatthecomplianceandtechnologicalfounda-tionaremoreprofoundinaffectingbanks’liquidityrisk.Apossibleexplanationistheirrelativelymatureandwidespreadapplicationscomparedtosupervisionusage.

IV.Conclusions

Inthispaper,weexplorethecausaleffectofinternalRegTechonbankliquidityrisk.BasedonadatasetofChineselistedbanksfrom2015to2022,werevealasignificantmitigationeffectofinternalRegTechonbankliquidityrisk.Thefindingremainsrobustafteralleviatingtheendogeneityissueandunderalternativeproxyofthedependentvariableanddifferentsampleperiods.ApossiblechannelisthatinternalRegTechbooststheregulatorycapabilityandmitigatesbanks’riskybehaviours.The

6四Y.XIAETAL.

complianceandtechnologicalfoundationssub-indicesaremoreprofoundinaffectingbankliquidityrisk.

Ourresearchoffersvaluableimplicationsforbanksandfinancialregulators.Forbanks,investmentininternalRegTechshouldbeencouragedtolowercompliancecostsandimproveliquidityriskmanagement.Furtherexplorationsonsupervisionusageareexpected.Fromaregulatorystandpoint,advancedtech-nologiescanbeincorporatedintoregulatorypracticetoenforcepenetratingsupervisionandbridgetheregulatorygap.Theenhancedexter-nalregulatorycapabilitycanboostinternalRegTech’seffectonbanks’liquidityriskcontrol.Ourresearchstillsuffersfromsomelimitations.

First,weconsideronlythelistedbanksduetodataavailability.Second,morechannelswithinthenexusbetweenRegTechandbankliquidityriskwarrantfurtherinvestigations.

Disclosurestatement

Nopotentialconflictofinterestwasreportedbytheauthor(s).

Funding

ThisworkwassupportedbytheNationalNaturalScienceFoundationofChina[72103082]andtheNationalTrainingProgramofInnovationandEntrepreneurshipforUndergraduates[202310320016Z].

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