RELATIONSHIP AND TRANSACTION LENDING IN A...

RELATIONSHIP AND TRANSACTIONLENDING IN A CRISIS

PatrickBolton(ColumbiaBusiness School)XavierFreixas (Universitat Pompeu Fabra)

LeonardoGambacorta(BIS)PaoloEmilioMistrulli (Bank ofItaly)

LUISS,7March2016

The views expressed in the following do not necessarily reflect those ofthe Bank for International Settlements or the Bank of Italy

AdefinitionofRELATIONSHIPLENDING

l Boot(2000),“RelationshipBanking:WhatDoWeKnow?”,JournalofFinancialIntermediation,9,3-25.

“…the provision of financial services by a financialintermediary that invests in obtaining customer-specificinformation, often proprietary in nature…and evaluates the profitability of these investmentsthrough multiple interactions with some customer overtime and/or across products” (p.10)

WhataretheprosandconsofRELATIONSHIPLENDING?

FIRMS’PERSPECTIVEl PROS

§ MITIGATES CREDITRATIONINGPHENOMENA:Easier access tothecreditmarket, inparticular foropaque firms (small, young,highlyinnovative firms) (ALOTOFPAPERSONTHIS)

§ FLEXIBILITY: Incaseofaidiosyncratic shocktothefirm (e.g.atemporary drop inits profitability/liquidity)banks may restructure theloan (e.g.longer maturity,lower cost ofcredit,…)(MANYPAPERSTOO)

§ CONFIDENTIALITY: Theinformationgathered bybanksisnotdisclosedtofirms’competitor (Yosha, 1995)anditmayencourageR&Dinvestments (Bhattacharya andChiesa, 1995)

l CONS§ HOLD-UP:banks gainaninformational advantage against other lenders

andthen may charge higher interest rates; theborrower isinformationally captured (classic references: Sharpe,1990;Rajan,1992;vonThadden,2004)


FIRMS’PERSPECTIVEl …CONS(continued)

HOLDUPPROBLEMS§ HOWTOMITIGATE HOLD-UPPROBLEMS?:multiplelending (i.e.

borrowing frommorethan one bank) tocreate competition amongbanks andthen tolimit theability ofbanks toextract informationalrents (vonThadden, 1992)- Diversifying bank liquidity risk:Detragiache, GarellaandGuiso(2000),“Multipleversus SingleBankingRelationships: Theory andEvidence”, Jnl ofFinance,55,1133-1161

…however- Multiplelending canalso lead tocoordination failures amonglenders incaseoffirms’ financial distress (BoltonandScharfstein,1996->optimal number ofcreditors)


BANKS’PERSPECTIVE

l PROS§ HIGHERPROFITS:banks gainaninformational rent (higher interest

rates)

§ BETTER KNOWLEDGEOFTHEIRCUSTOMERS: lower creditrisk (lowerdefault rates)

l CONSBanks might bealso “locked in”:incaseabank is tomuch dependantfromasetofborrowers (inparticular,when banks aresmallandlocal)

WhatDoWeKnowofRELATIONSHIPLENDINGinacrisis?

l NORMALTIMES§ Most ofwhat we know is based onnormal times

l CRISISTIMES§ We know much less about thefunctioning ofrelationship lending ina

crisis

§ somerecent papers onthat:- Berger, Cerquiero andPenas (2014),“Market size structure andsmallbusiness lending:Arecrisis different fromnormal times?”,Review ofFinance,30,1-31

- Beck,deHaas,Degryse,VanHoren (2014),“When Arm’s Lenght isTooFar.Relationship Bankingover theBusiness Cycle.”CEPRDiscussion Paper,n.10050.

Scopeofourpaper

• Westudyhowrelationship andtransaction lendingvaryoverthebusiness

cycle,frombothatheoretical andempirical perspective

• Weallowforaggregateshocksandcrises interactingwithidiosyncratic

cash-flowrisk fornon-financial corporations

• Weallowfirmstoborrowfrommultiplebanks andfrombothrelationship

andtransactionbanks(mixedfinance)

• Ourstudyisthefirst toaddress thesequestions inacomprehensiveway,

frombothatheoretical andempiricalperspective.

Roadmap

• Linkwiththeliterature

• Maincharacteristics ofthetheoreticalmodel

• Model’s testablehypotheses

• Empirical analysis:methodologyanddata

• Testofmodel’spredictions androbustness checks

• Conclusions

Linktotheliterature:Differentviewsaboutrelationshiplending

Differentviewson“relationshiplending”

Relationship lending theories What are the benefits of relationship lending?

1. Risk-sharing (implicit insurance)

Berger and Udell (1992), Berlin and

Mester (1999) Buying insurance regarding future access to credit

2. Interim monitoring (moral hazard)

Holmstrom and Tirole, (1997)

Only firms that don't have a sufficient stake in the project ask for a monitored bank loan, while firms with sufficient cash (or collateral) will prefer arm's length financing (transactional loans). Relationship banks' recognize the firms' type and monitoring is simply a way to limit the moral hazard problem.

3. Ex-ante screening (adverse

selection)

Agarwal and Hauswald (2010), Puri et

al. (2010)

Relationship banks may be better able to ex-ante screen the firms. The comparative advantage for relationship banks lead to an ex-post monopoly of information.

4. Learning

Rajan (1992), von Tadden (1995),

this paper.

Relationship banks do not know the firm type at the beginning but can learn it by monitoring. Monitoring is costly and this is reflected in higher lending rates in good times. However, in bad times R-banks have a strong incentive to shield the clients in order not to waste the information investment (and the sunk cost sustained).

Maincharacteristicsofthetheoreticalmodel

Themodel(1)• Extension of Bolton and Freixas (2006), which consider firms'

choice of the optimal mix of financing between borrowing froma relationship bank (R-bank) and issuing a corporate bond.Two key modifications:

1. AllowforachoicebetweenborrowingfromanR-bankoraT-bank(oracombinationofthetwo,mixedfinance)

2. Introduceaggregatebusiness-cyclerisk,toallowfirmstodifferintheirexposuretothisrisk,andtoconsiderhowtheresponseofR-bankstoacrisisdiffersfromthatofT-banks

Themodel(2)• Mainingredientsofthetheoreticalmodel:

1. RvsTbanks.1. Thefirm’stypeisprivateinformation int=0.Int=1,onlyR-banks can

observe itperfectly afterhavingpaidsomemonitoring costs𝒎2. R-banks havehigher intermediation costs (𝜌# > 𝜌%)because theyhave

toholdmoreequitycapitalagainsttheexpectationofmorefuture rollover lending

2. BusinesscycleandFirms.1. Theproject succeedsorfailsdepending onaknownprobability𝑝 thatis

commonlyobserved.2. FirmscanbeoftwotypesH (highvalue)orL (lowvalue).

1. Boththefirstperiodprobabilityofsuccess𝑝 andtheproportionofH-typefirms intheeconomyvarydependingonthestateoftheeconomy(Booms, Recessions) → 𝑝( > 𝑝) and𝐻(> 𝐻)

2. L-typefirmsarenotaffected bythestateoftheeconomy

Themodel(3)• Themodel’ssetupcontemplatesa2-periodcreditmarketin

whichfirmshavetoborrowanindivisibleloantoundertakeaproject

• Timing:• Att=0,firmschoosewhethertoborrowfromaT-bankandR-bank,oracombinationofthetwo;

• Att=1,theprojecteithersucceedsorfails,inwhichcasethebankdecideswhethertoliquidate theloanor torollitovertothelastperiod(t=2);

• Att=2,inarecessionstate,thesuccessprobabilityofarolled-overloanis1iffirm’stypeisH(highvalue)and0ifthefirmisL(lowvalue).

Themodel(4)• Firm’strade-off

§ FirmschoosewhethertoborrowfromaT- oranR-bankbytradingoffthecostsofborrowingvsflexibilityofrefinancing.CostishigherinthecaseofR-banks,withthebenefitsofbeingfinanciallysupportedinthefutureiftheprojectfailsint=1.

§ Financial support is sure for H-firms contracting with R-banks, while itdepends on the state of the economy for the firms -of all types-contractingwith T-banks.

• Equilibrium§ Giventhissetupandundersomeadditionalassumptions,itispossibletocharacterizeanequilibriumunderwhichsafefirms(withahighfirstperiodsuccessprobability𝑝)borrowfromT-banks,whileriskierfirmsmixbetweenT- andR- banks.Purerelationshipbankingisalwaysdominated (toohighcostofcredit)

Model’stestablehypotheses

Themodelprovidesfourtestablehypotheses:

1. R-banksarebetterinformedthanT-banksaboutfirms’type(monitoringcosts)=>delinquencyratesarelowerforR-banks

2. R-bankschargehigherinterestratesrelativetoT-Banksingoodtimesontheloanstheyrollover,butinbadtimes theylowerratesandincreasethesupplyoflendingtohelptheirbestclientsthroughthecrisis

3. Itexistsacriticalthresholdfortheprobabilityofsuccessinbadtimes �̂�) suchthatforany𝑝 > �̂�) firmspreferpuretransactionalbankingandforany𝑝 < �̂�) firmsprefertocombinethemaximumoftransactionalbankingandtheminimalamountofrelationshipbanking(mixedfinance)

4. ThecapitalbufferofanR-bankislargerthantheoneofaT-bankbecauseR-banksrestructurethedebtofH-firmsindistress

Empiricalanalysis:methodologyanddata

Methodologyanddata:challenges(1)

Howtoselect“goodtimes”and“badtimes”?

• Selecttwodates thatrepresent different statesoftheworld,possiblycausedbyanexogenous shockthathittheeconomy.Prior (2007:q2)andafter theLehman’sdefault (2010:q1)

• Italy isanexcellent laboratory forthree reasons:1)shocklargelyunexpected(exogenous) andhadasizable impactonItalianfirms;2)Multiple lendingisalong-standingcharacteristics ofthebank-firmrelationship;3)Detaileddata.

(a)Banklendingtotheprivatesector

Methodologyanddata:challenges(2)Howtodistinguish“T-banks”from“R-banks”?

• Theliterature hasshownthatthedistance affects theabilityofbankstogathersoftinformation (i.e.information thatisdifficult tocodify)whichisacrucialingredient ofrelationship lending(Petersen andRajan,2002;Berger etal.2005;AgarwalandHauswald,2010;Stein,2002).

• DegryseandOngena (2005):physicaldistance (bankbranch-borrower)• Wefocusontheinformational distance (notgeographical) between lenders

andborrowers:distancebetweenbankheadquarters andborrowers• …itisaproxyfortheabilityofbranchloanofficers topasssoftinformation throughhigherhierarchical layers(tohardensoftinformation)

• …andthenfortheincentives togather softinformation• Twodummyvariables:R-bank isequalto1iffirmk isheadquartered inthesame

provincewherebankj hasitsheadquarters; T-bank isequalto1ifR-bank=0.• AbankcanactbothasanR-bankoraT-bankaccording tothedistance from

thefirm

Descriptivestatistics

Bank-firm loan types Obs. %Spread

good time(2007:q2)

(a)

Spreadbad time(2010:q1)

(b)(b) -(a)

Log Loansgood time(2007:q2)

(c )

Log Loansbad time

(2010:q1)(d)

(d) -(c)

Capital to asset ratio(2007:q2)

(e)

Capital to asset ratio (2010:q1)

(f)

(f)-(e)

ALL FIRMS

i) Relationship only 18693 10.1% 4.3 6.2 1.9 7.74 7.73 -0.011 9.103 8.794 -0.31

ii) Both types 84598 45.8% 4.5 6.7 2.2 7.96 8.00 0.036 8.843 8.743 -0.10

iii) Transaction only 81604 44.1% 4.8 7.1 2.3 7.78 7.81 0.029 8.547 8.793 0.25

Total 184895 100.0% 4.6 6.8 2.2 7.86 7.89 0.028 8.739 8.770 0.03

H-FIRMS


ii) Both types 84129 45.9% 4.5 6.7 2.2 7.95 7.99 0.039 8.543 8.56 0.02

iii) Transaction only 80493 44.0% 4.8 7.1 2.3 7.77 7.80 0.032 8.842 8.79 -0.05

Total 183111 100.0% 4.6 6.8 2.2 7.85 7.88 0.031 8.730 8.69 -0.04

L-FIRMS


ii) Both types 439 24.6% 5.9 9.4 3.5 8.54 8.33 -0.207 8.949 9.04 0.09

iii) Transaction only 1139 63.8% 6.3 9.7 3.5 8.17 8.06 -0.113 8.648 8.88 0.24

Total 1784 100.0% 6.2 9.6 3.4 8.25 8.11 -0.143 8.760 8.90 0.14

Note: L-Firms are those that went into default in the period 2008:q3-2010:q1, H-Firms are the remaining ones.

Testforhypothesis1(DefaultProbability):

“R-banks are better able than T-banks at learning firms’ type”

Defaultprobability• Marginalprobabilityoffirmk togointodefaultinthe6quarters that

followedLehman'scollapse(2008:q3-2010:q1)asafunctionoftheproportion ofloansthatfirmk hasborrowed formaT-bankbeforeLehman(2008:q2),over thetotalamountborrowedbythefirmk:

• Marginalprobitmodel:

where𝛼.and𝜁0 are, respectively, bankandindustry-fixed effects andT-share istheproportionoftransactional loans(invalue) forfirmk.

• Result:AfirmrelyingmoreonT-financing hasahigherprobability togointodefault

ResultsDependent variable: P(defaultk=1)

(I)

Baseline equation

(II)

Firm specific characteristics

(III)

Alternative Weight

Coef. Sig. Coef. Sig. Coef. Sig.

T-sharek (in value) 0.0032 *** 0.0029 ***

(0.0008) (0.0007)

T-sharek (number of banks) 0.0028 ***

(0.0007)

Ζ 0.0051 *** 0.0051 ***

(0.0005) (0.0001)

LTD -0.0002 -0.0002

(0.0018) (0.0018)

Small firm -0.0021 -0.0021

(0.0034) (0.0034)

CREDIT_HISTORY -0.0002 *** -0.0001 ***

(0.0000) (0.0000)

Bank fixed effects Yes Yes Yes

Industry-province dummies Yes Yes Yes

Number of obs. 72,489 72,489 72,489

Pseudo R2 0.1273 0.1395 0.1397

T-share=1:0.3%increase inthedefaultprobability relativetoR-share=1

Testforhypothesis2

(CostofCredit/LoanAmounts):

“R-banks charge higher lending rates in good

times, but in bad times they lower rates and extend

credit to help their best borrowers through the

crisis”

Lendingandcostofcredit• Weestimatetwocross-sectionalequationsfortheinterestrate

(𝑟.,0)chargedbybankj onthecreditlineoffirmk andthelogarithmoftotaloutstandingloansinrealterms(𝐿.,0)grantedbybankj tofirmk:

where𝑣and𝛿 arebank-fixedeffects𝛽 andϕarefirm-fixedeffectsandT-bankisadummyvariablethatequals1iftheloanisgrantedbyatransactionalbank

Results

Variables

Interest rate

good time

(2007:q2)

(I)

Interest rate

bad time

(2010:q1)

(II)

Log Loans

good time

(2007:q2)

(III)

Log Loans

bad time

(2010:q1)

(IV)

T-Bank -0.0805*** 0.1227*** -0.2753*** -0.3129***

(0.0174) (0.0210) (0.0123) (0.0110)

Bank fixed effects yes yes yes yes

Firm fixed effects yes yes yes yes

Number of obs. 184,859 184,859 184,859 184,859

Adjusted R-squared 0.529 0.585 0.426 0.473

• T-banks (compared toR-banks)grantloansatacheaper interestrateingoodtimesandatahigherinterest rateinbadtimes.Thedifference 0.204% isstatisticallysignificantatthe1%level.

• T-banksalwaysprovideonaveragealoweramountoflending,especially inbadtimes.InbadtimesT-banks reduce lending(relative toR-ones) by3.2%***.

Testforhypothesis3:(OptimalFundingChoice)

“It does exist a critical threshold for the expected (in t=0) probability of success in bad time �̂�) such that for any 𝑝 > �̂�) firms prefer pure transactional banking and for any 𝑝 < �̂�) firms prefer to combine transactional and relationship banking (mixed finance)”

Interestratesettingbybank’stypeandstateoftheworld

• The cost of credit of transactional lending is always lower than relationship banking in good times. This pattern is reversed in bad times. In line with the model, it is always cheaper for safe firms to use T-banks.

The horizontalaxisreportstheZ-score, anindicatoroftheprobabilityofdefaultoffirms.These scores canbemappedintofourlevelsofrisk:1)safe; 2)solvent;3)vulnerable;4)risky.Theverticalaxisindicatetheleveloftheinterestrate appliedbythetwo bank typesoncreditlinestothe4groupsoffirms.

(a1) Interest rate: good times (2007:q2) (a2) Interest rate: bad times (2010:q1)

Lendingbybank’stypeandstateoftheworld

• The roll-over effects of R-banks on lending is in place prevalently for risky firms while safe firms obtain always a greater level of financing from T-banks both in good and bad times.

The horizontalaxisreportstheZ-score, anindicatoroftheprobabilityofdefaultoffirms.These scores canbemappedintofourlevelsofrisk:1)safe; 2)solvent;3)vulnerable;4)risky.Theverticalaxisindicatethelogoflendinginrealtermssuppliedbythetwobanktypes.

(b1) Lending: good times (2007:q2) (b2) Lending: bad times (2010:q1)

Testforhypothesis4:(CapitalBuffer)

“R-banks need a larger capital buffers to preserve lending relationship in bad times”

Bankcapitalandrelationshiplending(1)

A pure T-bank that have a credit portfolio composed exclusively oftransactional loans (T-share =1) have a capital buffer around 3 percentagepoints lower than a pure R-bank (T-share=0).

Dependent variable is regulatory capital-to-

risk weighted assets of bank j at 2008:q2

Baseline model

(I)

Bank-specific

characteristics

(II)

Firm-specific

characteristics

(III)

Financially

constrained firms

(IV)

T-share -3.839*** -2.992** -3.154** -3.231**

(0.890) (1.344) (1.307) (1.308)

Bank size 0.181 0.038 0.068

(0.402) (0.390) (0.375)

Bank liquidity ratio -0.015 -0.010 -0.003

(0.018) (0.019) (0.018)

Retail ratio 0.054*** 0.030* 0.029*

(0.018) (0.017) (0.017)

Characteristics of banks’ credit portfolios yes yes

Proportion of financially constrained firms yes

Bank zone dummies yes yes yes yes

Number of obs. 179 179 179 179


Bankcapitalandrelationshiplending(2)

Variables

Interest rate

good time

(2007:q2)

(I)

Interest rate

bad time

(2010:q1)

(II)

Log Loans

good time

(2007:q2)

(III)

Log Loans

bad time

(2010:q1)

(IV)

T-Bank -0.0792** 0.1940** -0.1625*** -0.2208***

(0.0402) (0.0734) (0.0282) (0.0289)

CAP 0.0096 -0.0426*** -0.0112 0.0113**

(0.0185) (0.0123) (0.0086) (0.0052)

US>GR 0.1881*** 0.1611*** 0.5315*** 0.1403***

(0.0228) (0.0430) (0.0174) (0.0193)

MUTUAL -0.7812*** -1.0057*** 0.0573 0.0569

(0.1284) (0.1066) (0.0378) (0.0523)

Bank group and rescue dummies yes yes yes yes

Bank zone dummies yes yes yes yes

Firm fixed effects yes yes yes yes

Number of obs. 184,859 184,859 184,859 184,859


Duringadownturnbanks(R-Banks) withacapitalratio5percentage pointsgreater lend5%(12%)moreloansata20(27)basispointlower interestrate

Robustnesschecks

• Bydropping bankandfixedeffectsweobtainquitedifferent results,consistentlywiththeviewthatourmeasure ofdistance isagoodproxyforbanks’abilityatgatheringsoft information

• Inclusionofa“Mainbank”dummy,thattakesthevalueof1forthebankthatgrantsthehighest shareoflendingtothefirm(0elsewhere)

• Duration oftherelationships

• Regioninstead thanprovince toidentify relationshipbanks

• Allsubsidiaries offoreignbanks areT-banks

Conclusions

Keypointstotakeawayl Thepaperprovidesamicro-foundation fortheexistenceofmultiple

lending andmixedfinance(borrowingfrombothtypesofbanks)

l Relationshipbankingisanimportantmitigatingfactorofcrises

l Therolerelationshipbankscanplayinacrisisislimitedbytheamountofexcessequitycapital theyareabletoholdinanticipationofacrisis

§ Banksenteringthecrisiswithalargerequitycapitalcushionareabletoperformtheirrelationshipbankingrolemoreeffectively

l From a policy point of view the results of the paper extend the needof capital buffers not only to protect depositors but also to allowrelationship banks to shield their clients in case of an adverseshocks

Thanksforyourattention!

PaoloEmilioMistrulliBankofItalyEmail:[email protected]

AnnexA comparison with the predictions of other models

Predictionsofrelationshipbankingmodels

§ Use the results for test of prepositions 1 and 2.§ Preposition 1 on firms' default probability. § Preposition 2 on how R and T banks set their interest rates.

Possible explanations of

relationship vs transactional

lending

I.

Delinquency rate

II.

Lending rates

III.

Lending quantities

Bad time Good time Bad time Good time Bad time

1. Risk-sharing R=T R>T R>T R>T R>T

2. Interim monitoring R>T R>T R>T ? ?

3. Ex-ante screening R<T R>T R>T R=T R=T

4. Learning R<T R>T R<T ? ?

Notes: R= Relationship bank (R-bank); T= Transaction bank (T-bank)

Step1:Resultsondefaultprobabilities

§ Test on Hypothesis 1: Defaults are lower for R-banks

§ This is consistent with two possible explanations of the nature of relationship lending, namely "learning" and "ex-ante screening“: in both cases R-banks are better able than T-banks in learning firms’ type.



lending

I.

Delinquency rate

II.

Lending rates

III.

Lending quantities







Step2:Analysisoninterestrates

§ Test on Hypothesis 2: R-banks ask for higher interest rates in good time but reduce cost in bad time to preserve lending relationship.

§ Results are consistent only with the “learning” hypothesis.



lending

I.

Delinquency rate

II.

Lending rates

III.

Lending quantities







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