loan contracting - information assymetry

8/13/2019 Loan Contracting - Information Assymetry

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Loan contracting:Asymmetric information

Pranav Singh


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Information Asymmetry and FinancingArrangements: Evidence from Syndicated Lo

The Journal of Finance - April, 2007

AMIR SUFI


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What is the paper about?Effort to understand:

How information asymmetry affects financing arrangements, and

What financial institutions can do to reduce problems associated with in

asymmetry

Financing Arrangements studied: Syndicated Loans, because it is close t

representative financing arrangement.

Firms from all points of the credit spectrum - privately held, unrated, hig

investment grade - utilize Syndicated Loans

Syndicated lending represents 51% of U.S. corporate finance originated,

generates more underwriting revenue for the financial sector than both

debt underwriting

Information Asymmetry and Financing Arrangements: Evidence from Syndicated Loans, Amir Sufi


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Syndicated loan A loan whereby at least two lend

offer funds to a borrowing firm

Borrower/Firm The firm that takes the loan

Lead Arranger Bank /Financial Intermediary that

a relationship with the Borrower, negotiates terms

contract, and guarantees an amount for a price ran

Participants Lenders that fund part of the loan

Overview


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Theoretical BackgroundLead Arranger: Informed Lender; Participants: Uninformed Lenders

Moral Hazard: If Lead Arranger does not adequately benefit from the Lo

shirk her responsibilities of Due Diligence and Monitoring

Adverse Selection: If Lead Arranger has negative information about the

then she might not share the information with Participants and hold sm

the loan.

Lead Arranger holding a larger share of the loan, apart from the fear of

reputation in the long-run, mitigates both Moral Hazard and Adverse Se


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Key Takeaways Importance of Information Asymin Syndicate Formation

Information Asymmetry between Borrower and Participants: In case of Opaque borrowers, participant lenders are "closer" to the bor

both geographically and in terms of previous relationships

Information Asymmetry between Lead Arranger and Participants:

A previous relationship between the lead arranger and a potential partic

increases the probability that the potential participant funds the loan

Previous lead arranger-participant relationships are much less imp

(both in magnitude and statistical significance) than the previous

relationships between the borrowing firm and the participant lend


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DataDealscan matched with Compustat

Dealscan provides information on Lead Arrangers, Participants and Loan

Terms (usable dataset of 12,672 loans)

Compustat provides financial information for the borrowers (9,681 loans

25% of Loans have more than one tranche. There is one contract for thehence, the analysis is done at the level of Loans and not individual Tran

A subset of Top 100 Lead Arrangers and Top 125 Participants used to an

Participants are chosen as syndicate members.


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Descriptive StatisticsBorrowers have $1.8 billion in sales on average,and the median is $367 million. They have 1.12

previous syndications on an average and 31%

have an S&P senior unsecured debt rating.

Lead Arranger The average market share of

Lead Arranger is 9%.Loan The average loan is $364 million with a

maturity of 1,103 days. It has 8.1 lenders, 1.7 lead

arrangers, and 6.4 participant lenders. The lead

arranger keeps 28.5% of the loan on average.


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Measure of Information AsymmetryBased on the availability of :

SEC filings, and

third-party credit ratings

Private firms: firms with no ticker and no S&P senior unsecured credit r

Unrated firms: public borrowers with publicly available accounting data

an S&P senior unsecured debt rating

Transparent firms: public firms with S&P senior unsecured debt ratings


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Measure of Information Asymmetry SummaryTransparent firms are larger,obtain larger loans and have alarger number of lenders, leadarrangers, and participantlenders.

When borrowers are opaque,lead arrangers retain a largershare of the loan and form amore concentrated syndicate.


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Information Asymmetry and Syndicate StructureA. Syndicate Structure Regressions: how variation in the opacity of the b

firm affects syndicate structure

Syndi a +=1

12Yeardumt+ Xib+ Opaqueic+ ei

Syndi: measures of the syndicate, such as the number of lead arrangers, the nu

participants, and the percentage retained by the lead arranger

Opaque: the degree of Opacity Private, Unrated, Private and Unrated

c: how increased opacity affects syndicate structure


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Information Asymmetry and Syndicate Structure

Syndicate Structure Regressions (usingtransparent borrowing firms as the

omitted group)

Lead arrangers form the most

concentrated syndicates with the fewest

participants and retain the largest share of

the loan when borrowing firms are private.

The same pattern is observed, to a weaker

degree, when borrowing firms are public

but unrated.Why do Participants prefer higher m


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Information Asymmetry and Syndicate StructureB. Borrowing Firm Reputation

Opaque: Private, or Unrated, or Both

Column1: lead arrangers hold more of the

credit when the borrowing firm is opaque.

Column2: lead arranger holds less of the

credit when the borrower has more

previous syndicated loans

Column3: the lead arranger retains 1.85%

less of the loan when the opaque borrower

has more previous syndicated loans vs. a

new opaque borrower


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Information Asymmetry and Syndicate StructureC. Lead Bank Reputation

Proxy for reputation: Market share, byamount, in the previous year

Column 1: more reputable lead arrangersretain less of the loan

Column 2: the effect of reputation is morepronounced on loans to opaque firms

Only reputable borrowers with marketshares of over 0.32 = 13.36/4.25 (in the99th percentile) are able to retain no moreof the loan when the borrower is opaque.Econometrics 101: Do not extrapolateRegression towards the tails.


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Information Asymmetry and Syndicate StructureD. Moral Hazard versus Adverse Selection

Adverse Selection: Lead arranger would beforced to retain a larger fraction of the loanby the participants when a previous lendingrelationship is present while the Authorfinds opposite effect.

But, Previous lending relationship could bea result so many other factors like size,credit quality,


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Information Asymmetry and Syndicate StructureE. Effect of Collateral Collateral data available for less than half

of observations.

59% of loans are secured in usable dataset

Results are stronger in magnitude and

statistical significance among unsecured

loans

Problems of information asymmetry are

more severe among unsecured loans


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Information Asymmetry and Syndicate StructureE. Robustness TestObservation: Lead arrangers retain a larger share

of the loan and form a more concentrated

syndicate when the borrower more heavily uses

positive accruals or has high R&D investment,

interpreted as requirement of more intense

monitoring of the borrower (!).

What does (Accrual-Income)/Assets mean?

For ex.: Average Rate of Depreciation of Asset.

If the borrower is a new firm, or the future value

of its assets is going to decline fast, Participants

might want to hold less of such loan.


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Information Asymmetry and Participant ChoiceA. Characteristics of Participant LendersDataset: Top 125 participants and top

100 lead arrangers

Private on Unrated Borrower:

Participants are smaller and better

capitalized and likely to be in the same

geographical area as the borrowing firm.

Transparent Borrower: Participants are

more likely to have been a former lead or

former participant for the borrower.


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Information Asymmetry and Participant Choice


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Information Asymmetry and Participant B. Participant Choice Probit Analysis

How the characteristics of loan i and the characteristics of bank j influence the probab

j is chosen (!) as a participant on loan i.

Pr(Participant = Bankij) =f (a + b * Loani + y * Bankj + eij)

how c varies with the opacity of the borrowing firm

For new borrowers: When problems of information asymmetry are potentially severe

arrangers are more likely to choose participants that are geographically closer to the f

being in the same region as the borrowing firm increases the probability of being in

for transparent firms, 6.9% for private and 7.4% for unrated firms

being a foreign or unregulated domestic financial institution is negatively related to

as a participant


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Information Asymmetry and Participant ChoiceB. Participant Choice Probit Analysis

For repeat borrowers: there is a large amount of persistence in borrowiparticipant relationships.

a lender that is a former lead arranger for a borrowing firm is 6.2% mbe chosen as a participant, but the effect is more than 50% stronger wborrowing firm is private.

A lender that is a former participant for a firm is 25.5% more likely to as a participant, but the effect is 10% stronger if the borrowing firm isunrated.


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Information Asymmetry and Participant ChoiceHow do lead arranger-participant relationships affect participant choic

Being in the same region as the lead arranger and having been on a recent sy

the lead arranger both positively affect the probability of being chosen as a

Effects are smaller when compared with the effects of being a former lead o

for the borrowing firm

Effect of lead arranger-participant relationships does not vary by borrowing

When information asymmetry is severe, a lead arranger selects particip

on the participant's familiarity with the borrowing firm, not based on th

participant's familiarity with the lead arranger itself.


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Comments

Nicely written paper and relevant to real Finance each section starts with

question; Inference and economic significance of all tables explained proper

Omitted Variables? Opaqueness is highly correlated with Poor Credit Qual

Is it only information asymmetry or the interaction of information asymme

credit quality? Keeping credit quality of the Loan same, does closeness re

investments?

Syndications are of 2 Types Underwritten or On Best Efforts Basis

Opaque borrowers would have higher share of Best Efforts Syndication att

the successful attempts would be present in the dataset. Not easy to gene

findings about opaque borrowers.


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The Supply Side Determinants of Loan ContrStrictnessThe Journal of Finance - October, 2007

Justin Murfin


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What is the paper about?

Explores supply side of borrower lender nexus

Holding borrower risk fixed, how the recent default experienclender impacts the strictness of the equilibrium contract, and

what factors influence the resulting change in lender preferencontingent control.

There is substantial research on the influence of shocks to Lecredit availability not none on the strictness of terms of the


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Key Takeaways

3. Why borrowers accept stricter contracts and the resulting increase

intervention, when their own risk is unchanged?

Lack of outside options of accessing finance: borrowers who rely uplimited number of relationship banks and/or lack access to alternatiof cheap capital are exposed to considerable lender-induced contracprecisely because of their limited outside options.


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Data and Methodology

Data: Dealscan merged with Compustat from 1984 to 2008

Measure of Loan Contract Strictness:

Consider a single financial ratio r that receives a shock in the period after the lr = r + N(0, 2)

If a covenant for r is written such that r < r`` allocates control to the lender, th

p 1 ``` , represents the ex ante probability of lender control, wherestandard normal cumulative distribution function.

In multivariate setting: STRICTNESS p = 1 FN(r` r``), where FN is the multivcumulative distribution function with mean 0 and variance

Strictness includes both slack in the covenants (r`-r``) and covariance between

Parameters estimated using loan data in Dealscan and financial data of the firm


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Measure Validation

Average Contract Strictnessover time plotted against the

percentage of respondents

reporting tightening credit

standards in the Federal

Reserves survey of senior

loan officers

The survey leads the measure

of Strictness.


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Measure Validation

The measure Strictness is

significantly correlated withCovenant Violations even aftercontrolling for slack

Slack measured as r`-r``. Should it

be measured as``` instead?

Presumably stricter contracts arepredictive of violations

Why does Collateral increasecovenant violations?


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Summary Statistics

Loans for which there is information on

covenants vs. missing information oncovenants

Median Borrowing Firm is larger, has

lower credit risk, has higher probability

of having a credit rating, better credit

rating, higher maturity of loan, higher

arrangers and participants.

Lead Lender experiences an average

(median) of 1.51 (zero) defaults in the 90

days leading up to a loan contracting

date, which represents 0.1% of the total

number of loans outstanding


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Contract Strictness and Recent DefaultExperiencewhere iindexes borrowe

controls inXi,tattempt to capture observable proxies for borrower risk:

separate intercepts for each S&P long term credit rating,

AltmanZ-score to capture repayment risk for unrated firms and to allow for lagged responses to distress by rating agencies

borrower fixed effects

year dummies to control for Macroeconomic Risk regional or industry-specific risk

loan characteristics, such as security, maturity, deal amount, etc.

Default counts are demeaned by lender and standard errors are clustereborrower and lender dimensions.

Contract Strictness and


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Contract Strictness andRecent DefaultExperience WithBorrower Fixed Effects

Column I: Significant tighteningby banks in response to recentdefaults

Columns IIV: a short-lived effect

Banks are most sensitive to defaultsoccurring in the 90 daysimmediately prior to contracting.

No Control for the age of loans

when they defaulted.Loans with lower maturity havelower risks. So, a Loan thatdefaults early is potentially moretroublesome for bank than aloan that defaults much laterand would result in highercontract strictness by the bank.

Controlling for


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Controlling for

Macroeconomic

Risk

replace time dummies with the sumof total defaults in the economyover the matching 90-day period

If the lenders defaults are capturingunobservable macroeconomic risk,then aggregate defaults over thesame period will control for

Macroeconomic risk.Even after controlling forMacroeconomic Risk, Lendersrespond to their own defaultexperiences.


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Controlling forMacroeconomic Risk

replace time dummies with thesum of total defaults in theeconomy over the matching 90-day period

If the lenders defaults arecapturing unobservablemacroeconomic risk, thenaggregate defaults over the

same period will control forMacroeconomic risk.

Even after controlling forMacroeconomic Risk, Lendersrespond to their own defaultexperiences.


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Do Lender DefaultsProxy for Industry orRegion-Specific Risk?

Lender defaults may proxy forgeographic or industry-specific risk.

Both time dummies, and aggregatedefault counts are ineffectivecontrols.

Remove defaults that are related tothe current borrower by state, one-digit SIC code, or both.

Still a comparable significant effectof Default experience on ContractStrictness.


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Distinguishing CapitalEffects from OtherEffects

Is tightening is a result of bankcapital depletion mechanicallyassociated with borrower defaults?

Add controls for bank capitalizationratios and changes to bankcapitalization ratios around the timethe loans were granted.

Bank Capitalization in the currentperiod does affect contractstrictness

The coefficient of the DefaultExperience do not change much,indicating that the mechanicaleffect of bank capitalization isminor.


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Recent Defaults andScreening Ability

The effect of defaults on loansoriginated recently to the effect ofdefaults on loans originated in thedistant past (legacy loans)

Defaults to lender portfolios 90 dayssorted into bins based on the vintageand demeaned.

only defaults on the newest loansare significant at the 5% level.

No Control for Amount Outstanding!

Given the Deal Amount, older loanswould have amortized more. Bankwould have less exposure to them andwould care less about their Contract

Strictness.


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Effects of Defaults onReputationParticipants see the defaults of theLead Arranger and might demandstricter contracts to compensate forthe deteriorating reputation of LeadArranger.

If that is true, then the coefficienton defaults should be larger for

syndicated loans than for bilateralloans.

Interaction effect of Bilateral Loanswith Defaults has the opposite sign.

Reputation of Lead Arranger is notdriving Contract Strictness.


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Lender Effects andBorrower OutsideOptionsWhy do borrowers submit tostricter contracts when theirown risk profile is unchanged?

Separate borrowers into aboveand below median based onthe number of banks used overthe last four transactions.

Only borrowers with smallernumber of Lenders are affectedby the Default Experience ofLenders!


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Lender Effects andBorrower OutsideOptionsThe impact of availability ofnonbank sources of fundingfor the Borrower

Borrowers divided into CPissuers and Non-CP issuersbased on the credit rating

cutoff (A2)CP Issuers are not affected byLenders Default Experience!

No control for the number ofLenders!


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Comments

Paper is easy to understand, uses simple but innovative contr

It extends the influence of shocks to Lenders from supply of ccontract strictness.

But,

All Controls are carried out one by one. There should be a tab

the controls together.Not enough Lender Controls: What if some banks lent aggresswitnessed an increase in defaults and made contracts stricter


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Finally

A third desirable property of any strictness measure is scale. Setting sla

one implies a very strict cash flow covenant (a one-dollar reduction in cwill trigger default) but a current ratio covenant devoid of meaning (thecurrent assets to total assets can vary between 0.01 and 1.0 without evpage 1570

Oh My God!!!

Did the Author call Current Ratio = current assets/total assets, and JF

it?

Later the paper mentions that Current Ratio = current assets/current lia

And current ratio covenant of 1 is a properly scaled and a very useful c


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Asymmetric information effects on loan spre

Journal of Financial Economics - 2009

Victoria Ivashina


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What is the paper about?

Diversification Premium demanded by the Lead Bank in an S

Asymmetric information will cause participants to demand a large loan by the lead bank.

But, higher ownership by the lead would result in higher idiosyncratic riportfolio. Higher the Ownership, Higher would be the diversification coownership for the Lead Bank.

In equilibrium, the asymmetric information premium demanded by paroffset by the diversification premium demanded by the lead.

Rationale: the lead banks portfolio is not perfectly diversified and is unexposed to idiosyncratic risk. There is a risk component that will be pricelead bank but not by the participant banks. Why?


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Summary Statistics

Data from 1993 to 2004From CreditPro,Compustat and Dealscan

The Loans in Compustathave lower spread,higher facility amount

and belong to largerfirms.


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l k


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Empirical Framework

For each loan, Author calculates the change in credit risk resulting fro

addition of that loan to the lead banks loan portfolio Standard deviation of the loan portfolio default probability is used as

of credit risk

Default probability standard deviation = , where w is the loanweights(bank-specific), the probability of default covariance matrixspecific).

Computed at the two-digit Standard Industrial Classification (SIC) leveThe instrument the loans contribution to the credit risk of the lead bportfolio is the difference between the default probability standard dmeasured after and before the loan was added to the portfolio.

In place of Leads ownership, Author uses Leads median ownership for size quartile. Medians also have the same bias of higher ownership leve

E i i l F k


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Empirical Framework

A fitted value of the lead share, computed using the first-stage estimate

replace the observable lead share in the second stage.First Stage: Lead share =

1*Controls +

2*Instruments + ,

Second Stage: Required loan spread = 1*Lead bank shareF + 2*Contro

The loan amount and other non-pricing features of the loansuch as mcollateral, and covenantsare fixed before the syndication process andjustifies use of loan characteristics as control variables. Not entirely true

Controls for lender and borrower characteristics, and market conditionsBank fixed effects to control for reporting by select banks to Dealscan.

Approximately 2% of the deals in the regression sample have multiple leMuch Lower than reported by other Authors!


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First Stage RegressionLead share =

1*Controls +

2*Instruments +

Both instrumentsare jointly statisticallysignificant in explaining the share retained bythe lead bank.

Change in default probability standarddeviation negatively affects Leads share, and

Lending limit positively affects Leads share

Column 1: Dealscan Data;

Column 2: Matched Compustat Data withoutadding financial ratios;

Column 3: Matched Compustat Data withfinancial ratios

Higher Credit Ratings increase Leads Share!


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Second Stage ResultsRequired loan spread = 1*Leadbank shareF + 2*Controls +

the dependent variable is the loanspread and the focus is on thecoefficient on the lead share

IV approach corrects the bias.

OLS has positive coefficient, that is,

higher Lead Share increases theloan spread. Opposite of what Sufifound!

Higher Maturity lowers spread, andCollateral and Covenants increasespread. Opposite of what onewould expect!

R b t T t Di ifi ti Eff t


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Robustness Tests: Diversification Effect

Test of the diversification effect, that is, whether higher leads share results in highe

the lead demands to compensate for cost of diversification.Uses an instrument that exogenously shifts the level of asymmetric information within

syndicate without directly affecting the lead banks credit-risk exposure: Lead banks r

Lead banks reputation: is the maximum number of deals arranged by the same lead

same participants, measured over a three-year horizon and expressed as a percent of

underwritten during this period.

First Stage: For a given spread, when reputation is high, information asymmetry is lowbank would syndicate a larger fraction of the loan. Confirmed in the first stage results

previous table.

Second stage results show positive relation between the share retained by the lead ba

required spread. Due to the cost of diversification spreads increase with increase in


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Robustness Tests:Diversification Effect

Second Stage Results

positive relation between the shareretained by the lead bank and the requiredspread

Coefficient on Previous Lending Relationshipschanges sign in the second stage.

Now more Previous Lending Relationships

result in higher spread for a given Leads share.Is it due to the definition of the instrumentthat excludes other participants?

Maximum number of deals arranged by thesame lead bank with the same participants

Is this the right definition of reputation?

Robustness Tests: Alternative Credit Ris


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Management Techniques

Measure of Credit Risks: Standard deviation of the default probability of the leads loa

Banks use unobservable risk-management techniques, including credit derivatives swasecuritization through collateralized loan obligations (CLO), and loan sales on the seco

A banks actual credit-risk exposure might be difficult to measure

Alternative Credit Risk Management Techniques were not very important between 19

loan CDSs started trading in 2004

Total CLO volume for 19972001 is estimated to be around $100 billion, less than 2%amount of syndicated loans

less than 5% of the loans originated between 2000 and 2004 are quoted in the seco

market

Excluded all quoted loans from sample. Does that influence the OLS estimates?


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Robustness Tests:Upfront Fee

Is the upfront fee, rather than the spread,

being used to compensate the lead bankfor its credit-risk exposure?

In this case, the diversification premiumwould not affect the all-in spread drawn.

Anecdotal evidence suggests that there isnot much cross-sectional variability in theupfront fee, making it an unlikely channelfor settling a diversification premium.May not be entirely true!

Default probability standard deviation isimportant in explaining spread and notupfront fee.

The sample should be sliced intodifferent subcategories to assess the useof upfront fee as the compensating

mechanism


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Robustness Tests:Monitoring synergiesif industry concentration of the

loan portfolio is associated withsynergies in information collectionand monitoring, the spreaddemanded by the lead bank shouldbe lower

Look at the cases in which the leadbank does not have industry-

monitoring expertise.If monitoring creates synergy, thenthe coefficients should be higher,but they are not.

Coefficients are lower with low Tstatistics!

Comments


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Comments

Adds a systemic component of diversification effects to consider whileeffects of Information Asymmetry, but does not convince the author.

Like many other Finance papers, Author uses IV strategy but does notvalidity in detail.

T stats are very low, lower that 10% significance levels in some cases.

Coefficients are pointing in opposite directions in some regressions.

Assumption that monitoring effort of bank is independent of the dcost C to get

If C depends on , then the monotonous relation between C andshare, does not hold anymore.

loan contracting - information assymetry

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