risk-sharing in the syndicated loan market: evidence from...
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Risk-Sharing in the Syndicated LoanMarket: Evidence from Lehman Brothers’
Collapse
Hanh Le ∗†
Abstract
I examine the impact of banks’ liquidity risk on their risk-sharing arrange-
ments in the syndicated loan market. I use Lehman Brothers’ bankruptcy as a
shock to the liquidity risk banks face from revolver draw-downs, and banks’ level
of revolver co-syndication with Lehman as a measure of cross-bank exposure to
this shock. Using within-relationship estimators, I show that more exposed banks
are more likely to reduce the significance of their role in a syndicate. Moreover,
more exposed banks that stay as lead arrangers to the same borrower form more
“diversified” syndicates, choosing syndicate members whose loan portfolios are
less correlated with their portfolios. These adjustments do not occur in term
loans, but only in revolvers, where liquidity risk matters the most. Interestingly,
I find that more exposed banks do not reduce the lending amount, nor do they
charge higher interest rates relative to less exposed banks. Overall, my results
suggest that the ability of banks to limit their liquidity risk exposure via adjust-
ing syndicate structures might alleviate the negative consequences of a shock to
lending supply.
∗University of Illinois at Chicago; email: [email protected]†I am grateful to members of my committee: Viral Acharya, Kose John, Anthony Saunders, and
Philipp Schnabl for their guidance and support. For helpful comments, I thank Tobias Berg, DirkBurghardt, Gabriela Coiculescu, Matteo Crosignani, Jason Levine, Anthony Lynch, Rustom Irani,Oliver Randall, Or Shachar, Hyun Song Shin, Stoyan Stoyanov, David Yermack, Shaojun Zhang andseminar participants at NYU-Stern. All errors are mine.
1 Introduction
Syndicated lending facilitates the origination of large loans by pooling together capital
across various lenders, thereby allowing them to share risks. The financial crisis of
2007-2009 presents an experiment to study various aspects of this market. Contraction
of the syndicated lending activity in the wake of the crisis has been extensively inves-
tigated. However, how bank exposure to such a supply-side shock affects risk-sharing
arrangements is an important question that has escaped research attention. This pa-
per aims to provide empirical evidence to this effect. Specifically, I show that banks
which are exposed to negative liquidity shocks during the crisis actively manage their
syndicate structures ways that limits their exposure to future risks. More importantly,
I identify two novel mechanisms through which risk-sharing arrangements can happen:
1. the choice of exit options via the role a lender plays; and 2. the diversification among
syndicate members.
In order to understand how banks share risk when syndicating a loan, it is important
to first understand the risks attached to such a loan. Simply put, a syndicated loan is
one which is jointly provided to a borrower by two or more financial institutions. The
type of the loan determines how much each syndicate member exposes herself to credit
risk and/or liquidity risk. In a term loan contract, banks provide the loan amount
up-front, and the borrower is required to pay interest and principal before the maturity
of the loan. Therefore, a term loan exposes lenders to credit risk, the risk that the
borrower defaults on its repayment obligation. In a revolving line of credit contract
(thereafter referred to as “revolver”), syndicate members are committed to fund on
borrower demand up to the contractual amount of the loan, at any time over the life
of the loan. As such, a revolver not only exposes the lenders to credit risk as in a term
loan, on the amount the borrower has demanded (“drawn down”), but also the liquidity
risk on the amount of undrawn commitments.
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Each syndicate member chooses her level of risk-sharing in two ways. First, she
may choose the fraction she contributes to the syndicate. The higher the contribution,
the greater the risk to which she is exposed. This aspect has been largely examined
in Sufi (2007), Ball et al. (2008), and Ivashina (2009), among others. Secondly, and
more subtly, she may choose the depth of her involvement in the syndicate by taking on
certain roles. These roles range in the order of importance from “lead arranger” (most
important) to “co-agent” to “participant” (least important).1 While syndicate roles
are typically correlated with the amount of contribution, they differ in whether an exit
option is available. Theoretically, all lenders can sell their shares of a syndicated loan in
the secondary market. Anecdotal evidence suggests that a syndicate’s key lenders rarely
sell loans for fear of reputation damage (see, for example, Ivashina and Sun (2011), Esty
and Megginson (2003)). Because the absence of an option to exit the syndicate increases
the liquidity risk of having funds committed, a more senior syndicate member shares
greater risks compared to a junior one, even when both of them contribute the same
share to the syndicate.
Secondly, I argue that the propensity to share risk by a lead arranger is not only
manifested in her share of the loan, but also in the composition of syndicate members.
Choosing participants whose loan portfolios are distant , i.e. less correlated, with those
of the lead bank will increase the stability of the syndicate. Specifically, a “closer”
syndicate is less likely to fund a commitment when a shock occurs that affects loan
portfolios of both the lead arranger and other participants. Of course, this liquidity
risk-sharing concern should be traded off with the “efficiency hypothesis”, proposed by
Cai et al. (2011), who find that most banks form closer syndicates whose members are
likely to have similar lending expertise, as doing so would reduce the monitoring and
screening costs.
1For a discussion on syndicate roles, please refer to Cai et al. (2011) and Section 2 of this paper.
2
I study how bank exposure to a liquidity shock affects the two aspects of risk-sharing
discussed above. The unexpected collapse of Lehman Brothers in September of 2008,
coupled with the company’s substantial involvement in the syndicated loan market,
provides a useful laboratory to study this question.2 At the time of its bankruptcy
announcement, Lehman had $30 billion in outstanding commitments in the syndicated
loan market. Lehman’s collapse may pose potential liquidity problems for the com-
pany’s revolver cosyndicators (hereafter referred to as “exposed banks”), not because
they now have to stand in for Lehman’s share of the loan, but because they experience
a greater draw-down rate from their borrowers. In fact, these borrowers face liquidity
problems of not being able to borrow from Lehman following its collapse, hence they
may choose to draw down more on other cosyndicators for precautionary purposes.
This places additional stress on exposed banks’ liquidity, potentially leading to a run
on their other revolvers. In fact, Ivashina and Scharfstein (2010) find that draw-downs
occur more for banks that are more exposed to Lehman-cosyndicated revolvers, and
that these draw-downs are largely held in cash.
Given that cosyndicating revolvers with Lehman might subject a bank to the liquid-
ity problems from revolver draw-downs caused by Lehman’s defaulting on its lending
position, I use Lehman’s collapse as a shock to bank’s liquidity and bank-level exposure
to Lehman-cosyndicated revolvers as a cross sectional exposure to this shock. I examine
how this exposure affects the traditional bank lending channel, and subsequently, how
it affects changes in risk-sharing arrangements following Lehman’s collapse. Examining
such supply-side effects is challenging, for the following reasons. First, there could be
a credit demand shock that correlates with bank exposure in the cross section. For
example, suppose more exposed banks are more likely to lend to worse quality firms.
If the systemic crisis set out by Lehman’s collapse forces these firms to go bankrupt in
2Lehman Brothers’ bankruptcy announcement was largely unexpected. On the announcement date,Lehman’s equity lost over 90% of its value.
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the post-Lehman period, this would have led to a more pronounced drop in lending,
or a mechanical reduction in the depth of roles for more exposed banks. In this case,
any relationship between exposure and the outcome variables are not causal. Second,
exposure to Lehman revolvers is an endogenous choice variable that might depend on
other bank characteristics, which may also drive the observed outcome.
I address the first concern in three ways. Firstly, I control for time varying firm
characteristics that might affect lending and syndicate risk-sharing. Second, I include
borrower industry fixed effects in all regressions, to account for the fact that borrow-
ing firms in different industries may be affected differently by credit demand shocks.
Most importantly, I employ within relationship estimators, effectively including in my
sample only firms that borrow from the same bank both before and after Lehman’s
collapse. This eliminates the sample selection bias caused by unobservables that drive
firms’ decision to borrow. Finally, I include bank-firm fixed effects, which take away
the average of unobservable characteristics driving the bank-firm matching, leaving a
cleaner identification of the exposure effect.
To address the second concern, I examine whether banks with different exposure lev-
els are inherently different from each other. I find that non-exposed banks are smaller,
have more core deposits and less subordinated debt than exposed banks. On the other
hand, banks with positive exposure do not differ very much from each other, except
along the assets dimension. This motivates the use of time-varying bank control vari-
ables in all regressions. Furthermore, compared to exposed banks, non-exposed banks
participate much less frequently in the syndicated loan market. In light of the signifi-
cant difference between exposed and non-exposed banks, I exclude non-exposed banks
from the sample and confirm robustness of the main results.
I find that more exposed banks reduce the maturity of loans originated following
the shock, but do not reduce the lending amount or increase interest rate in the post-
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Lehman period in any significant way, relative to less exposed banks. At first glance,
this result appears difficult to reconcile with Ivashina and Scharfstein (2010), who find
that banks cosyndicating more revolvers with Lehman decrease their lending during the
financial crisis. However, note that I am only looking at the “intensive lending margin”,
effectively ignoring cases where banks cease to lend to certain borrowers post-event. My
results, coupled with results from Ivashina and Scharfstein (2010) suggest that exposed
banks may ration credit following the shock. Yet for borrowers to whom banks continue
to lend, they do not worsen the terms of loan contracts.
The main contribution of this study is to show that banks may be able to offer
similar lending terms post Lehman by restructuring their risk-sharing arrangements.
Specifically, I find that more exposed banks decrease the significance of their role in
revolvers originated following Lehman’s collapse. A one standard deviation increase in
exposure leads to an 11.5% in probability that the bank switches from an important
role (lead arranger or co-agent) to a participant role. By reducing the importance of
syndicate roles in revolvers, banks subject themselves to less future liquidity risk, as
they are more likely to be able to sell their commitments should liquidity risk arises.
Furthermore, banks are more likely to switch from being a co-agent to a participant,
rather than from a lead arranging role to a less important role. This is expected given
that lead arranging roles are attached with lending relationships that an exposed lender
may not want to forgo.
When examining the cross section of borrowers, I find that exposed banks adjust
the importance of their roles only for the riskiest borrowers, i.e. those without an
investment grade credit rating. As these borrowers have few alternative funding sources,
they are more likely to pose liquidity problems to banks by drawing down on revolvers
for precautionary purposes. The result further reinforces the impact of liquidity risk on
banks’ risk-sharing arrangements, by showing that banks actively manage with which
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borrowers they choose to increase their exit option.
Given that exposed lead banks do not reduce the depth of their role in revolvers,
do they restructure syndicates in a way that lowers future liquidity risk? To this end, I
find that exposed lead banks are more likely to form more diversified syndicates, where
Cai et al. (2011)’s “distance” measure is adapted as a proxy for lender diversification.
In fact, a one standard deviation increase in the lead bank’s exposure results in a 17%
increase in the measure of lender diversification. By diversifying, an exposed lead bank
subject itself to a smaller liquidity risk, caused by a correlated shock that may affect
liquidity demand or default probability of the borrowers from both the bank and other
syndicate members.
Finally, I examine with which syndicate members an exposed lead bank’s diversifi-
cation concern is the biggest. As co-leads and co-agents (“important lenders”) usually
contribute a larger share to a syndicate, relative to participants, I hypothesize that lead
banks’ diversification incentive is greater when it comes to selecting important lenders.
Indeed, I find that the effect of interest is positive and highly significant when syndicate
diversification is measured as the average distance between lead arrangers and impor-
tant lenders. However, when diversification is measured between the lead arrangers
and participants, the effect of exposure on diversification is still positive but loses its
significance. These results suggest that exposed lead banks reduce their risk exposure
by diversifying with the syndicate members who matter the most for syndicate stability.
My findings have a number of important implications. From a practical standpoint,
they suggest another way in which banks can restructure their assets to manage the
liquidity shock that occurred during the financial crisis, that is, via risk-sharing ar-
rangements in the syndicated loan market. From a theoretical standpoint, my results
pertaining to syndicate diversification suggest the need for theories on syndicated loans
to incorporate lead banks’ ability to select syndicate members. This is in contrast with
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the traditional banking models of multiple lenders (Diamond (1984), Holmstrom (1979)
and Holmstrom and Tirole (1997)), where the monitoring lender does not have such a
choice. Results presented in this paper imply that managing the composition of lend-
ing syndicates may result in welfare improving outcomes for both the lender and the
borrower.
Related Literature
This study is related to the empirical literature on syndicate structure, whose fo-
cus so far has been primarily on how asymmetric information between borrowers and
lenders, and that among syndicate participants, shapes syndicate ownership arrange-
ments. The literature builds upon the basic theoretical assumption that the need for
lender monitoring arises because of asymmetric information and moral hazard prob-
lems (Leland and Pyle (1977), and Diamond (1984)). Borrowers know their health,
collateral, industriousness better than lenders do (asymmetric information). But they
are not willing to transfer all their information to the lenders, as there are benefits
to exaggerating good attributes and understating bad ones (moral hazard). Diamond
(1984) argues that, when there are many lenders, monitoring efforts are superfluously
costly and may lead to “inefficient free-riding”. As a result, creditors may delegate
monitoring responsibilities to one financial institution. This delegation, nonetheless,
entails moral hazard on the part of the delegated lender. The delegated lender no
longer invests her own money, and therefore she may not have incentives to exert best
effort. In the context of syndicated lending, the lead arranger can be thought of as a
delegated monitor.
A similar moral hazard problem is featured in the framework of Holmstrom (1979)
and Holmstrom and Tirole (1997). Under this framework, there are uninformed lenders,
who rely on information and monitoring provided by the informed lender to make
investments in firms. As the informed lender’s effort is unobservable, she exerts less
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than first-best optimal effort. This “shirking” behaviour is more costly to the informed
lender the higher her financial interests in the firm. In anticipation of this, uninformed
lenders are only willing to invest in the firm provided the informed lender has taken a
large enough stake. These models lend an explanation for why the lead-arranger, being
the informed lender, should retain a share of the loan; and why she should retain a
larger share when the borrower requires greater monitoring effort.
Dennis and Mullineaux (2000), Jones et al. (2005), and Sufi (2007) provide empir-
ical evidence consistent with such theoretical predictions, showing that the lead share
increases in borrowers’ measures of opacity (e.g. the borrower does not have a public
bond rating, is a private firm, or is non-investment-grade). Ball et al. (2008) proxy
for asymmetric information by the debt-contracting value (DCV) of borrowers, which
captures the ability of firms’ accounting numbers to detect credit quality deterioration
in a timely manner. They find that a higher DCV (i.e. a lower level of information
asymmetry) is associated with a smaller loan share retained by the lead arranger.
In addition to affecting the lead arranger’s share, asymmetric information is also
found to shape other aspects of syndicate structure. Lee and Donald (2004) and Sufi
(2007) find that when there is little information about the borrower, syndicates are
smaller and more concentrated. Lin et al. (2012) argue that firms whose largest ultimate
owner possesses control rights which exceed cash flow rights tend to suffer from moral
hazard problems on the part of such an owner. Consequently, these firms should require
more intense monitoring and due diligence from the lenders, and their lending syndicates
should be formed in a way that facilitates better monitoring. Consistent with this
hypothesis, Lin et al. (2012) find that where the cash flow-control rights divergence is
greater, syndicates are more concentrated and consist of lenders who are close to the
borrower and have lending expertise in the borrower’s industry.
As described above, much work in the area explores how borrower characteristics
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affect syndicate ownership structure. This study, on the other hand, examines how
lender characteristics shape syndicate structure. In this respect, the study is closest to
Gatev and Strahan (2009), which shows how liquidity risk managements affect syndicate
membership. They find that commercial banks, which hold an advantage relative to
other institution types in providing products exposing lenders to systematic liquidity
risk, dominate the market for revolvers. In addition, commercial banks with a higher
capacity to absorb liquidity risk (as measured by transaction deposits over total assets)
expose themselves to higher liquidity risk via syndicated lines of credit. Findings in
Gatev and Strahan (2009) are consistent with theoretical predictions of Kashyap et al.
(2002), which explain banks’ combination of transaction deposits and credit lines as a
risk management motive. In particular, so long as the demand of depositors and credit
line borrowers are not highly correlated, there exists a benefit in providing liquidity
services to both types of customers. In this study, I focus on novel aspects of syndicate
structure and show that liquidity management does not only manifest in the share of
loan a bank owns, but also in the role in which it is willing to play and the lead bank’s
choice of syndicate members for diversification purposes.
My study is also tangential to a burgeoning strand of literature examining bank
lending during the financial crisis. Ivashina and Scharfstein (2010) is the first attempt
in such a strand to provide evidence of a possible supply-driven lending contraction.
Using Dealscan, a comprehensive database of syndicated loans, they find that banks
that are more susceptible to runs on its short-term debt or syndicated revolvers are more
likely to reduce lending during the crisis. Cornett et al. (2011) extend the cross section
of Ivashina and Scharfstein (2010) using CALL report data and identify one mechanism
that might lead to credit contraction. They show that banks that are more exposed to
unused commitments (i.e. liquidity risks) attempt to build up balance sheet liquidity,
hence reducing their overall lending. Irani (2012) finds that a bank’s health affects its
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corporate liquidity provision capacity. Using the collapse of the asset backed commercial
paper(ABCP) market, beginning August 2007, Irani (2012) finds that banks that are
more exposed to ABCP make fewer revolving lines of credit after the ABCP collapse,
at the same time imposing worse contract terms for those revolvers they roll over. My
results, however, suggest that the ability of banks to reduce their exposure to liquidity
risks through risk-sharing adjustments might alleviate the negative consequences of a
shock to lending supply.
The rest of the paper is organized as follows. In Section 2, I present institutional
details about the syndicated loan market, and analyze liquidity implications of Lehman
Brothers’ collapse for the company’s revolver cosyndicators. Section 3 describes the
data. Section 4 presents the empirical methodology, and main results. Section 5 dis-
cusses results from robustness tests. Finally, Section 6 concludes.
2 Institutional Background
2.1 The Syndicated Loan Market
The syndicated loan market first came into existence during the 1980’s amidst the
leveraged buyout wave, as an efficient way to fund large loans. By the end of 2007, it
had become a dominant venue for US issuers to obtain funding from banks and other
institutional capital providers, with outstanding syndicated loans amounting to 11.38
trillion US dollars. The syndication process begins with one or more lead arrangers
signing a preliminary loan agreement called a “mandate” with a borrowing firm, speci-
fying the loan amount, covenants, fees, an interest rate range, and collateral. The lead
arranger usually retains part of the loan and turns to potential participants to fund the
rest of it. Once the loan agreement is signed by all participating lenders, each lender is
responsible for their share of the loan and is subject to identical terms.
Members of a syndicate typically fall into one of three categories. The “lead ar-
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ranger” is the most important member of a syndicate, taking on the primary respon-
sibilities of screening and monitoring the borrower. Next are “co-agents” whose titles
are awarded either in exchange for large commitments, or in cases where these institu-
tions actually play a role in the syndication or administering of the loan. Lastly, other
participants play no other role than committing to funding part of the loan. Unlike the
lead arranger who establishes relationships with the borrower, other syndicate mem-
bers usually maintain an arm’s length relationship with the borrower through the lead
arranger. Commitments in lead arranger and co-agent roles are included in calculations
of “league tables”, which identify large players in the syndicated loan market.
Term loans and revolving lines of credit are two major types of loans in the syn-
dicated lending market. Term loans work like bonds: the borrower receives the entire
amount of the loan at the start and pays off the principal and interest by the matu-
rity date. Revolvers, on the other hand, operate like credit cards: the lenders commit
to fund on demand up to a contracted amount over the life of the loan. While both
term loans and revolvers expose the lenders to borrowers’ credit risk3, revolvers subject
lenders to an additional risk - the liquidity risk associated with future commitments
arising from borrower withdrawal demand. All syndicate members receive the same
interest rate for the borrowed/drawn amount (“front-end fee”) and additionally in the
case of revolvers, for unused commitments (“back-end fee”). The borrower also pays
an upfront fee at the start of the loan, which is often tiered among syndicate members;
the larger amount of this fee goes to the lead arranger, with the rest usually being pro-
portional to each participant’s commitments. Term loans and revolving lines of credit
are two major types of loans in the syndicated lending market. Term loans work like
bonds: the borrower receives the entire amount of the loan at the start and pays off
the principal and interest by the maturity date. Revolvers, on the other hand, operate
like credit cards: the lenders commit to fund on demand up to a contracted amount
3the risk that the borrower will not pay back the borrowed amount
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over the life of the loan. While both term loans and revolvers expose the lenders to
borrowers’ credit risk4, revolvers subject lenders to an additional risk - the liquidity
risk associated with future commitments arising from borrower withdrawal demand.
All syndicate members receive the same interest rate for the borrowed/drawn amount
(“front-end fee”) and additionally in the case of revolvers, for unused commitments
(“back-end fee”). The borrower also pays an upfront fee at the start of the loan, which
is often tiered among syndicate members. The larger amount of this fee goes to the lead
arranger, with the rest usually being proportional to each participant’s commitments.
Once a loan is allocated, investors are free to trade their share of the loan in the
secondary market.5 Loan sales can be structured as either assignments or participation.
An assignment is effectively a primary sale, in which the assignee replaces the original
lender and becomes a direct signatory to the loan. A participation contract, on the
other hand, is an agreement between an existing lender and a participant, where the
former remains the official holder of the loan. Ivashina and Sun (2011) shows that while
such significant lenders as lead arrangers and co-agent are not likely to sell their loans,
half of other participants do so in the two years following loan origination.
2.2 Lehman Brothers’ Collapse and Banks’ Liquidity Prob-lems
Upon its bankruptcy filing on September 15, 2008, it is estimated that Lehman Brothers
had $30 billion of undrawn revolving commitments.6 At the time, Lehman Brothers
4the risk that the borrower will not pay back the borrowed amount5The pricing of revolvers in the secondary market works as follows. The buyer of a revolver pays
a price for the funded part of the revolver, but receive credit for the unfunded amount. For example,assume a $4m revolver is sold at a price of 80 cents per dollar. If the revolver has $1m in undrawncommitments and $3m in drawn commitments, the buyer will have to pay 0.8*$3m for the drawnamount, but will receive a credit of (1-0.8)*$1m. This credit is to allow for the fact that if theborrower decide to draw on the revolver in the future, the buyer will have to fund 100 cents to thedollar.
6Loan Syndications and Trading Associations,“Examining the Legal and Business Reality of Syn-dicated Leveraged Loan”, WilmerHale, Boston, July 15, 2009.
12
were participants in 930 outstanding revolvers, whose total facility size amounts to $794
billion. Moreover, 566 out of these 930 facilities ($416 billion) had Lehman acting as
either a lead arranger or a co-agent. A natural question to ask is what implications
Lehman’s bankrupty may have had on the liquidity of its revolvers’ co-syndicators.
Lenders’ obligations under a syndicated credit agreement are not joint. According to
the Model Credit Agreement Provisions,7 once the performing lenders have fully funded
their commitments, the borrower will be unable to replace the defaulting lender’s share
by demanding increases in the amount of loans from these lenders. The co-syndicators’
liquidity problem arises, not because they have to stand in for the defaulting lenders,
but are consequences of the borrower managing their liquidity risk, as discussed below.
Syndicated credit agreements usually include a “yank-a-bank” clause, which grants
the borrower the option to force the defaulting lender to assign its commitments to an-
other willing financial institution at par. Nevertheless, this remedy is largely ineffective
during the financial crisis. The lack of liquidity in the syndicated loan market made it
impossible to locate a replacement lender or to convince an existing lender to purchase
the loan commitment at par from the defaulting lender. To manage this liquidity prob-
lem, the borrower may decide to increase borrowing requests by an amount necessary
to cover the shortfall created by the defaulting lender. As a result, the amount of funds
withdrawn from performing lenders could be higher under the presence of a defaulting
lender.
To illustrate how Lehman’s collapse could place funding pressure on performing
lenders, let’s look at one hypothetical example. Suppose that Lehman Brothers and
Bank One syndicate a revolver of $200 million to Alcoa, under which each bank makes
equal contributions. Suppose that Alcoa decides to borrow $100 million from the re-
volver. If Lehman did not default, Alcoa would demand $100 million, in which case
7See Model Credit Agreement Provisions: Administrative Agent ’s Clawback, §a (2005).
13
Lehman Brothers and Bank One were responsible for funding $50 million each. How-
ever, when Lehman defaulted, Alcoa would have to demand $200 million, expecting
to receive $0 from Lehman and $100 million from Bank One. The $50 difference in
the amount Bank One has to fund in the two cases represents the unexpected revolver
draw-down arising from exposure to Lehman revolver co-syndication. Of course, if Al-
coa decides to draw down more than $100 million, Bank One is liable to fund only up
to its contractual amount of $100 million.
In addition to the liquidity shock arising from a larger revolver draw-down rate on
Lehman co-syndicated revolvers described above, banks co-syndicating revolvers with
Lehman may face additional funding pressure from runs on their other revolvers, similar
to Diamond and Dybvig (1983)’s depositor runs argument. Specifically, in the above
example, other firms that rely on revolvers funded by Bank One (but which do not
involve Lehman) may worry that such a liquidity shock would drain BOA’s capital,
making it unable or unwilling to fund commitments extended to these firms. In a
market where liquidity is dried up and finding alternative funding sources is difficult,
these firms may draw down on revolvers for precautionary purposes even when they
have no intermediate need for funds. Consistent with this expectation, Ivashina and
Scharfstein (2010) collect data on draw-downs from SEC filings by a sample of selected
manufacturing firms and find that banks with more exposure to revolvers cosyndicated
with Lehman experienced greater draw-downs during the crisis.
3 Data
3.1 Sample Selection
I collect syndicated loan information, i.e. loan terms and syndicate structure from
the Loan Pricing Corporation (LPC)’s Dealscan database. Dealscan is the largest
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and most comprehensive syndicated loan database used in academic research to date.
According to LPC, Dealscan covers most loans made to large publicly traded companies.
Information on lending to small and middle-sized firms are, however, very limited.
I start with a sample of all term loans and revolvers originated or outstanding over
the period from June 2005 to December 2009, where the borrower is a US firm. Infor-
mation on loan originations are used to conduct analyses at the loan level. Information
on outstanding loans are used to construct the liquidity exposure measure, and the
syndicate diversification measures. I classify a loan as a revolver if Dealscan reports its
loan type as one of the following: “Revolver/Line < 1 Yr.”, “Revolver/Line >= 1 Yr,
“Revolver/Term Loan”, “364-Day Facility”, “Demand Loan”, “Limited Line”. 8
Using Dealscan’s information on lender names, geographic location and operation
dates, I then hand-match each lender to an institution in the National Information
Center (NIC) database. This process yields a unique identity (“RSSD ID”) for each
lender, together with the dates it was acquired and became part of another institution,
if any. I control for mergers in my sample in the following way. The acquiring firms
inherit all the acquired firm’s syndicated lending relationships with both borrowers and
other lenders. In addition, all loans unexpired at the merger date are transfered to the
acquiror’s record. I aggregate the lenders to their top holding company level, which I
identify using top-holding company IDs (item RSSD9348 from reports of conditions and
income (CALL Reports), which could be found on the Chicago Federal Reserve Bank
website) and the RSSD IDs from NIC. Lenders’ characteristics (at the top-holding
company level) are obtained from CALL reports. For each loan origination, I use the
borrower GVKEY’s to match the borrowing firm to COMPUSTAT in order to obtain
borrower financial information. 9
8In extra tests, I also examine term loans, where liquidity risk-sharing is not a concern. I code thefollowing loan types as term loans if Dealscan’s LoanType contains one of the following: “Delay DrawTerm Loan”, “Term Loan”, “Term Loan A” - “Term Loan I”.
9I thank Sudheer Chava and Michael Roberts for providing the linking file (see Chava and Roberts
15
My empirical strategy makes use of repeated relationships, that is, loans extended
to the same firm by the same lender prior to and following Lehman Brothers’ collapse.
To alleviate concern that the results obtained are merely demand side effects, I ex-
clude firms that belong to the financial and real estate industries, which are at the
heart of the 2007-2009 financial crisis. Finally, I retain only US bank lenders whose
information can be consistently found in CALL reports. These filters leaves a final
sample of 1130 revolvers, extended by 50 banks and bank holding companies to 311
firms over the period from June 1, 2005 to December 31, 2009. The unit of observation
is a bank-firm-loan triple. Due to the multiple-lender nature of syndication, a loan
may appear multiple times in the sample. For example, a revolver with facility ID
252245, extended to MEMC ELECTRONIC MATRIALS INC on December 23, 2009
by a syndicate consisting of Fifth Third Bank and PNC Bank will result in two obser-
vations (facility 252245 -Fifth Third Bank- MEMC ELECTRONIC MATRIALS INC,
and facility 252245 -PNC Bank- MEMC ELECTRONIC MATRIALS INC).
3.2 Definition of Main Variables
Exposure to Liquidity Shocks from co-syndicating revolvers with Lehman
(Exposure LEH )
Similar to Ivashina and Scharfstein (2010), I first measure a bank’s exposure using the
fraction of outstanding revolvers it cosyndicates with Lehman over the total number
of its outstanding revolvers. For the numerator, I only consider revolvers where both
the bank and Lehman play an important role of “lead arranger” or “co-agent”.10 This
restriction is put in place to take into account the fact that syndicate participants would
be likely to sell the loan prior to maturity (Ivashina and Sun (2011)).
(2008)).10This is a slight deviation from Ivashina and Scharfstein (2010) who place a restriction on Lehman
to be a lead arranger or coagent but do not require the bank to also play a significant role.
16
Note that this exposure measure is crude, for three reasons. Firstly, we do not know
when and by how much the borrower draws down on each revolver. Secondly, borrowers
can and do sometimes refinance a loan prior to maturity. Thirdly, we rarely know the
actual allocation of each bank in a syndicate. The first point is admittedly a weakness
of my study, as well as others of a similar nature (Ivashina and Scharfstein (2010), Lin
and Paravisini (2011)). For the period I examine, lender allocation is complete in only
25.76% of the loan facilities.11 I address the second point by computing the exposure
measure at time t using only outstanding loans that are issued in the three years leading
up to t. The results, which are unreported here for brevity, confirm the robustness of
my analysis.
To address the third point, I construct another measure of exposure based on the
dollar amount, instead of the number, of revolvers. For all tests, I report results under
this second measure. Results are robust throughout when I use first measure of exposure
but are not reported for brevity. The dollar amount a bank contributes to a loan is
calculated as the bank’s percentage share in a loan times the total loan facility amount.
As noted earlier, lender shares are not observable in many cases. To predict a bank’s
percentage share of a loan in those cases, I employ the following procedure. Based on
observations with information on lender shares, I estimate a censored regression model
as follows:
Sharei,l = α + βyear + γ1Leadi,l + γ2Coagenti,l + γ3NLendersl + γ4Sizel + γ5Maturityl + εi,l
(1)
The dependent variable, Sharei,l, is the fraction of loan l contributed by lender i.
Independent variables are those expected to influence lender shares. In particular,
Leadi,l is a dummy variable equal to 1 if the bank is a lead arranger, and 0 otherwise;
11Lin and Paravisini (2011) find that facilities with complete lender share information tend to belarger in terms of both facility amount and number of participants.
17
Coagenti,l is a dummy variable equal to 1 if the bank is a lead arranger, and 0 otherwise;
βyear is the year fixed effects; NLendersl is the number of lenders in facility l ; Sizel is the
log of the loan amount; and Maturityl is the loan’s maturity (the number of months
between the loan contractual start date and end date). I expect lenders with more
important roles to contribute a larger share, and lenders in larger syndicates (with a
higher number of members of a larger facility amount) to contribute a smaller share.
Finally, I expect lender shares to be smaller for longer maturity loans which tend to
involve higher risks. I then use the estimated coefficients from (1) to predict the lender
shares in cases where such information is not observed in Dealscan.12
Depth of Lender Roles
From the discussion in Section 2.1, I argue that more important (“deeper”) lender
roles are associated with higher future liquidity risk, the risk that the bank cannot
sell their share of the loan when they want to. To construct the Depth of Lender
Roles variables, I first assign lender roles based on information provided in Dealscan’s
LeadArrangerCredit and LenderRole fields. I identify a lender as a lead arranger if 1.
LeadArrangerCredit field indicates “Yes”; or, 2. The LeadArrangerCredit field indicates
“No” but “LenderRole” is one of the following: administrative agent, agent, arranger,
bookrunner, coordinating agent, lead arranger, lead bank, lead manager, mandated
arranger, and mandated lead arranger. I assign a lender the “co-agent” title if she
is not a lead arranger as determined from the above procedure and her “LenderRole”
is one of the following: co-agent, co-arranger, co-lead arranger, documentation agent,
managing agent, senior arranger, and syndication agent. A lender is also classified as a
co-agent if the LeadArrangerCredit field indicates “No” but her “LenderRole” falls in
12Main coefficient estimates (with t-statistics reported in parentheses) from estimating (1) are asfollows:
Sharei,l ≈ 99.8(205.75) + 15.767(211.73)Leadi,l + 3.02(38.80)Coagenti,l
+−0.281(−97.37)NLendersl +−4.42(−174.11)Sizel +−0.086(−69.56)Maturityl
All coefficients are as expected and significant at the 1% level.
18
the list of titles held by banks awarded a “Yes” in the LeadArrangerCredit field.
I employ three variables to proxy for the depth of lender roles. The first variable is
Role Depth, which is an ordinal variable taking the value of 1 if bank b is a participant
in loan l made to firm f , 2 if the bank is a co-agent, and 3 if it is a lead arranger (or
co-lead). Second is Lead, a dummy variable, which equals 1 if the bank is the lead (or
co-lead), and 0 otherwise. Finally, Important, is a dummy variable taking the value
of 1 if the bank is either a lead arranger (or co-lead) or a co-agent, and 0 otherwise.
Higher values for these variables indicate deeper lender roles.
Lender Distance
I hypothesize that post Lehman’s bankruptcy, banks that are more exposed to liquidity
problems via co-syndicating revolvers with Lehman should form more stable syndicates.
I argue that one way to do so is by diversifying lenders in terms of their borrower
pools. By choosing to syndicate with banks whose borrower pools are distant or less
correlated with their own, an exposed bank subject itself to a smaller risk that its
syndicate members may experience liquidity problems when the bank is also in trouble.
Reducing this risk is important, as banks do not want to face a greater draw-down rate
arising from the potential impairment of other syndicate members at a time when they
are experiencing liquidity problems. I adapt the “distance” variable proposed by Cai
et al. (2011) as a measure of lender diversification.
I define a bank’s measure of diversification in a syndicate as the average distance in
loan portfolios between the bank and other syndicate members. Calculating the distance
in loan portfolio holdings between two lenders involves the following steps. First, I need
to compute each lender’s portfolio weights in each industry category. For each lender
i - loan l combination, I search for all loans arranged by lender i in a lead arranger
role over the three-year period leading up to date t. The portfolio weight of lender i in
industry j at time t, denoted as wi,j,t is determined by dividing the dollar amount of
19
loans extended to firms belonging to industry j, over the total dollar amount of loans
the bank arranges as a lead lender.13 Note that∑J
j=1wi,j,t = 1, where J is the total
number of industries the lender can invest in. For the purpose of classifying borrower
industries, I follow Cai et al. (2011) and employ Standard Industry Classification (SIC)
1-digit and SIC 2-digit systems. In addition, to be consistent with the traditional asset
pricing literature, I adopt Fama and French (1994)’s 49 industry classification, which
is published on Kenneth French’s website.
The distance between the two lenders m and n in a syndicate formed at time t,
dm,n,t, is then simply the Euclidean distance in their weights in a J -dimensional space:
dm,n,t =
√√√√ J∑j=1
(wm,j,t − wn,j,t)2 (2)
which captures how similar syndicated loan portfolios are between two lenders.
Appendix A provides an example of how to calculate distance between two lenders.
Suppose that there are N syndicate members contributing to loan l, formed on
date t. Bank b’s lender diversification in syndicate l, denoted Db,l,t is computed as the
average distance between bank b and other syndicate members, calculated using all
loans originated by these lenders in lead arranger roles in the three-year period leading
up to date t :
Db,l,t =
(∑N−1n=1 dbn,memn,t
)N − 1
(3)
where dbn,memn,t denotes the distance between the nth pair of bank b and syndicate
member memn, and bn 6= memn.
13where a syndicate consists of more than one lead arranger, I divide the facility amounts equallyamong the lead banks.
20
3.3 Summary Statistics
In this section, I describe my main sample, which includes revolvers originated by
bank-firm pairs that exist both before and after Lehman’s collapse. In Table 1, I
partition this sample into two sub-samples, corresponding the pre-event (June 1, 2005
to September 14, 2008) and post-event (September 15, 2008 to December 31, 2009)
periods. The average bank has $741 million in assets pre-Lehman, which increases to
$948 million post-Lehman. This increase reflects the many mergers that happened over
the sample period. As expected, banks’ performance deteriorated in the post-Lehman
period, evidenced by a decrease in ROA and capital ratios and an increase in loan loss
ratios. The ratio of core deposits over total assets, however, increases, reflecting either
a flight to quality or banks’ efforts to adjust their balance sheets (see Acharya and
Mora (2012)). My measure of exposure to Lehman revolvers decreases post-Lehman,
reflecting the maturity of certain Lehman-cosyndicated revolvers.
Borrowing firms also experience performance deterioration in the post-event period.
Their sales, interest coverage ratios and net working capital drop and their leverage
increases. The fraction of tangible assets, however, remains unchanged over the sample
period. With liquidity being dried up and the poor performance of banks during the
crisis, the terms of revolvers’ contracts became worse as expected. As can be seen from
Panel C of table 1, the average all-in-drawn spread increases, and the facility amount
and maturity both decrease post-Lehman.
Are banks with high exposure to Lehman revolvers different from those with low
exposure? Cosyndicating with Lehman is clearly a bank’s choice variable. As a result,
it is important to understand how exposure may correlate with observable bank charac-
teristics. In the first two columns of Table 2, I break out my sample in the pre-Lehman
period (June 1, 2005 to September 14, 2008) into two groups: banks with no exposure
to Lehman (23 banks) and those with positive exposure (27 banks). It is noticeable that
21
exposed banks are much larger and more risky compared to their non-exposed coun-
terparts: they have fewer core deposits and are funded by more subordinated debt.
Nonetheless, they do not differ in terms of various performance measures such as ROA,
capital and loan loss ratios. Exposed banks also lend to larger, older firms with lower
interest coverage ratios. They also participate in closer/less distant syndicates. Finally,
exposed banks lend on better terms with lower all-in-drawn spreads and larger facility
sizes.
I then focus on exposed banks only and examine differences in bank characteristics,
borrower pools, and loan characteristics between the high-exposure and low-exposure
groups. The high-exposure group consists of the fourteen top exposure banks, and
the low-exposure group is made up of the remaining thirteen banks. Here the high
and low exposure groups do not differ very much. High-exposure banks, on average,
are still larger. But the statistical significance is only at the 10% level. Furthermore,
bank performance and risk measures (ROA, core deposits, subordinated debt, capital,
and loan loss ratios) are economically and not statistically different among the two
groups. On the other hand, they are still different in terms of borrower pool and loan
characteristics, with high-exposure banks extending less expensive and larger loans to
larger, more older firms with fewer tangibles. However, the syndicates formed by high
and low exposure banks are not different in the measure of facility distance.
Given differences among banks with different exposure to Lehman revolvers, one is
concerned about a classic endogeneity issue. That is, the collapse of Lehman Brothers
may affect risk-sharing incentives of banks via characteristics that are correlated with
exposure. If this were the case, any effect found on exposure could merely be correlation,
and not causation. I address this concern in two ways. First, I control for bank and
borrower characteristics in all my regressions. Second, as noted above, most differences
in lender characteristics are found between exposed and non-exposed banks, rather than
22
between high-exposure and low-exposure banks. Therefore, in one of the robustness
tests, I exclude the 23 banks that do not have any exposure during the sample period.
I argue that exclusion of these banks does not cause serious sample selection bias, as
they are far less frequent players in the syndicated loan market when compared to
exposed banks. In fact, the 23 non-exposed banks participate in only 78 revolvers in
the pre-event period, compared to 931 co-syndicated by the 27 exposed banks.
Differences in borrower characteristics for loans made by banks with different ex-
posure levels raise a further concern that a credit demand shock post-Lehman that is
correlated with bank exposure might have led to the observed outcome. Specifically,
if more exposed banks lend to worse quality borrowers, who suddenly became much
riskier following Lehman’s collapse, then findings that exposed banks reduce the signif-
icance of their role and form more diversified syndicates might be driven by a demand
side effect. Panel B and C of Table 2 shows that this is not the case. If anything, more
exposed banks lend to safer and more established firms: their customers are larger,
older and have fewer tangibles.
4 Empirical Methodology and Results
4.1 Bank Lending Channel
In this section, I revisit the “bank lending channel” hypothesis. In particular, I exam-
ine how bank exposure to liquidity risk affects (i) lending activity and (ii) other loan
contract terms. To investigate (i), I start with the following specification:
∆Lendingb = α + βExposure LEHb + ρ′∆Xb + εb (4)
23
The dependent variable, ∆Lendingb, is the change in the logs of bank lending activity in
the pre- and post- Lehman’s bankruptcy periods, which correspond to the 365 days be-
fore and after September 15, 2008. I measure lending activity for a bank in each of these
two periods as the total number of loan facilities in which the bank participates. The
main explanatory variable of interest, Exposure LEHb, is bank b’s exposure to Lehman
co-syndicated revolvers, as defined in Section 3.2, and measured as of September 15,
2008. The coefficient of interest, β, measures the impact of this exposure on the change
in bank lending activity. ∆Xb is a vector of changes in bank control variables, where
changes are measured by the quarterly average of these variables in the post-Lehman
period, minus the corresponding value in the pre-Lehman period. Bank controls in-
cluded in Xb are: Log assetsb, Core Depositsb, ROAb, Loan lossb, and Capital Ratiob.
I expect larger banks to be better diversified and less risky. Hence they can afford to
pass on more favorable terms to the borrowers. A similar argument goes for banks with
a high ratio of core deposits, which are usually considered to have a more stable source
of funding. Finally, better performing banks, those with higher ROA, higher capital
ratios and smaller loan losses are expected to be more healthy and can lend on better
terms.
Column 1 of Table 3 presents the results estimating regression (4). We can see that
on average, banks reduced their lending activity in half following Lehman’s collapse.
Consistent with the result in Ivashina and Scharfstein (2010), banks that are more
exposed to liquidity risk via co-syndicating revolvers with Lehman reduced their lending
by more than less exposed banks. The effect is both statistically and economically
significant. A one standard deviation (3.944%) increase in Exposure LEH is associated
with a 16.17% decrease in new loan originations. When I break out lending activity
into that related to revolvers and term loans (Columns 2 and 3 of Table 3), the effect
is concentrated in the revolver sample only. In the term loan sample, β is still negative
24
but is no longer statistically significant. This result is consistent with Irani (2012), who
finds that a negative shock to bank health affects its lending, but that this effect is more
pronounced in banks’ liquidity provision via revolvers relative to their credit provision
via term loans.
The disadvantage of estimating (4) is that it ignores borrower characteristics and
therefore does not take into account the possible effect of a credit demand shock. This is
a concern if (i) there are differences in characteristics between firms that did and did not
take out loans following Lehman’s collapse, and (ii) these characteristics are correlated
with the Exposure LEH variable in the cross section. To alleviate this concern, I focus
on loan level analysis in the rest of the paper, and only examine loans that are taken
out by the same firms from the same banks before and after Lehman’s collapse. The
regression specification is as follows:
Termsb,f,l,t =αb,f + δSIC + β1Postt + β2Exposure LEHb,t
+ β3Exposure LEHb,t ∗ Postt + γ′Xb,t + ρ′Zf,t + εb,f,l,t (5)
The coefficient of interest is β3, which measures the differential effect of the change in
exposure on lending terms in the post Lehman period, between high- and low-exposure
banks. Loan contractual terms I consider, Termsb,f,l,t, include maturity, all-in-drawn-
spread, and (the log of) facility size attached to revolver l, made to firm f by bank b at
time t. αb,f denotes bank-firm fixed effects. δSIC denotes industry fixed effects. Postt
is a dummy variable equal to 1 if the loan is made in the post-Lehman period; and
0 otherwise. Exposure LEHb,t is bank b’s exposure to Lehman co-syndicated revolvers
measured at time t, as defined in Section 3.2. Xb,t and Zf,t are, respectively, vectors of
bank and firm control variables, which are defined in Table B-2. All control variables
are measured in the quarter immediately prior to the loan origination date t.
25
Bank controls, Xb,t, are previously defined. Firm controls, Zf,t, include Log Salesf,t,
Leveragef,t, Interest Coveragef,t, Net Working Capitalf,t, Tangiblesf,t, Firm Agef,t and
Investment Gradef,t. I expect larger and older firms to be more established and less
risky, and thus are able to obtain more favorable terms. Firms with high leverage and
low interest coverage are riskier and hence are expected to borrow on worse terms.
Firms with less net working capital and more tangibles tend to lose more value in
default, thus have higher default risk and command worse borrowing terms. Finally,
investment grade firms are better credits and thus should command more favorable
terms.
Including time-varying observable and unobservable bank and firm control variables
is important in explaining loans’ contract terms. Nonetheless, I continue to worry about
unobservables that may drive (i) the sample selection of borrowing firms in the post-
event period; and (ii) the matching between banks and firms. In particular, (i) relates
to the concern that there may be unobservable differences between firms that decide
to borrow in the post-Lehman period and firms that do not. On the other hand, (ii)
relates to the concern that firms with certain characteristics are likely to borrow from
banks with characteristics that are correlated with the exposure variable.
To address these issues, I only include in my sample firm-bank pairs that have re-
volver contracts with each other both in the pre- and post-Lehman periods, and employ
bank-firm fixed effects in the regression. This way, β3 is identified only from the inten-
sive margin of lending. In other words, it is identified from changes in the dependent
variable within relationship, one that is established by the same firm borrowing from
the same bank both before and after the event. This approach closely follows previous
work in the literature examining syndicated lending (see, for example, Glenn Hubbard
and Palia (2002), Lin and Paravisini (2011), Irani (2012), and Santos (2011)). It ad-
dresses (i) by excluding firms that do not borrow following the event. In addition, it
26
addresses (ii) by taking away the cross-sectional mean of characteristics that influence
firm-bank matching, leaving the effect of exposure to be identified from between-bank
variation at a given point in time.
Note that although estimators from within-relationship estimation are consistent,
results cannot be extrapolated to the extensive margin of lending. That is, we do not
know whether or how banks’ and firms’ exiting relationships continue to lend and borrow
after the shock. This is a common limitation applying to within-relationship estimators
(see Lin and Paravisini (2011), Khwaja and Mian (2005), and Schnabl (2012)). Finally,
I follow Petersen (2009) and cluster standard errors by both firm and bank. This
allows for the fact that the error components of lending policies in regression (5) may
be correlated across banks lending to the same firm, and across firms for loans made by
the same bank. Clustering by both the bank and firm dimensions are important, for two
reasons. First, because the shock happens at the bank level, changes in lending policies
may be correlated among loans originated by the same bank. Second, a firm receives
multiple loans over the sample period, changes in contractual terms from different banks
may be correlated within the same firm.
Table 4 shows the results estimating regression (5) on maturity, all-in-drawn spread
and facility size for my sample of revolvers. All else equal, an average bank in the sample
does not alter their maturity and facility size following Lehman’s collapse. However,
they do charge larger spreads, reflecting the overall liquidity crisis. Interestingly, more
exposed banks reduce the maturity of revolvers they syndicate following Lehman’s
collapse, relative to less exposed banks. The estimated coefficient of interest, β3, is
negative and statistically significant. A one standard deviation difference in exposure
translates to a difference in the pre-Lehman - post-Lehman change in maturity of 3.9
months. There is no statistical difference in changes in spread and facility size between
exposed and non-exposed banks.
27
How does this result reconcile with results in Table 3 and in Ivashina and Scharf-
stein (2010) who find that banks that are more exposed to liquidity problems lend less
during the 2007-2009 financial crisis? Note that in testing specification (5), I look ex-
clusively at the intensive lending margin. It is entirely possible that on aggregate, more
exposed banks lend less post-Lehman, but for those borrowers to whom they continue
to lend, they do not reduce the facility size. This is indeed what Irani (2012) finds while
examining the impact of bank health on corporate liquidity provision.
I now turn to examine the main question of this study. If exposed banks do not
worsen the terms of lending, do they structure their syndicates in a way that reduce
future risks? In particular, do they participate in less important roles following the
shock? If they remain as the lead arranger (or co-lead), do they choose to form more
stable syndicates?
4.2 Change in Depth of Syndicate Roles
4.2.1 Exposed Banks Reduce the Depth of Their Roles
In this section, I examine how exposure to liquidity risk leads to banks changing the
depth of their syndicate roles. The regression specification is the same as (5), except
that the dependent variable is now Role Importanceb,f,l,t, the importance of the role
played by bank b, in revolver l made to firm f at time t:
Role Importanceb,f,l,t =αb,f + δSIC + β1Postt + β2Exposure LEHb,t
+ β3 ∗ Exposure LEHb,t ∗ Postt + γ′Xb,t + ρ′Zf,t + εb,f,l,t (6)
Three variables measuring the Role Importance are used: (1) Role Depthb,f,l,t, an
ordinal variable taking the value of 1 if bank b is a participant in loan l made to firm
f , 2 if the bank is a co-agent, and 3 if it is a lead arranger (or co-lead); (2) Leadb,f,l,t,
28
a dummy variable, which equals 1 if the bank is the lead (or co-lead), and 0 otherwise;
and (3) Importantb,f,l,t, a dummy variable taking the value of 1 if the bank is either a
lead arranger (or co-lead) or a co-agent, and 0 otherwise. All other variables are defined
in section 4.1. Again, the unit of observation here is a bank-firm-loan triple.
Results of testing this change in banks’ syndicate roles are reported in the first three
columns of Table 5. Column (1) suggests that more exposed banks are more likely to
reduce the depth of their role following Lehman’s bankruptcy. The coefficient on the
interaction term, Exposure LEHb,t ∗ Postt is negative and statistically significant. The
magnitude of the coefficient suggests that a one standard deviation increase in exposure
leads to a decrease of -0.149 units of the depth of role. Given that banks rarely change
the depth of their role for the same borrower (the average and median bank level
change in the depth of role across all revolvers in the post-Lehman period is 0.015 and
0 respectively), this effect is economically significant.
Given that more exposed banks are more likely to play a less important role in a
syndicate in the post-Lehman period, I next examine which type of role they find it
easier to switch away from. In column (2), I examine the impact of exposure on a
bank’s decision to participate as a lead arranger. Again, I find that banks with higher
exposure to Lehman revolvers are less likely to participate as a lead arranger following
Lehman’s collapse. However, while the estimated β3 is negative, it is not statistically
significant. As relationship lending is formed at the lead arranger level, these results
suggest that lending relationship is sticky, i.e., lead arrangers who continue to fund a
relationship borrower’s loan after the shock do not forgo their lead-arranging role.
On the other hand, when the depth of syndicate role is proxied for by whether the
bank is a key lender (either lead arranger or co-agent), I continue to find the risk-
sharing result. That is, more exposed banks are more likely to join the syndicate as a
participant following Lehman’s collapse. Column (3) shows a negative and statistically
29
significant estimate for β3. In terms of economic significance, a one standard deviation
increase in exposure results in an 11.5% decrease in the probability that the bank joins a
syndicate as a key lender in the post-Lehman period. This result is consistent with the
hypothesis that higher exposure leads to banks willing to share less risk in a syndicate.
Specifically, they are more likely to be in participant roles, such that they can more
easily sell off the loan when illiquidity becomes imminent.
One could argue that a decrease in role depth has nothing to do with risk-sharing.
But rather, the same bank lending channel as in Ivashina and Scharfstein (2010) could
be at work. In other words, more exposed banks could merely reduce their allocation
to a syndicate following Lehman’s collapse, and thus were given less important roles.
I address this concern in two ways. Firstly, I show in Section 4.1 that more exposure
does not lead to a decrease in the size of the facility. This test is, however, crude as it
looks at the effect of exposure on the total facility size rather than banks’ individual
commitments. Therefore, I restrict my sample to only bank-firm-loan observations
where information on bank allocation is not missing. Using this sample to analyze the
effect of exposure on individual banks’ contribution, I find the coefficient of interest,
β3, to be indistinguishable from zero. 14
Second, I show that the same syndicate role adjustments do not happen for term
loans. I expect that risk-sharing adjustments in anticipation of liquidity shocks would
arise more in revolvers than in term loans. While the latter represents up-front com-
mitments and involves only credit risk from the borrower, the former entails both credit
risk and liquidity risk. Consistent with this argument, columns (I)-(III) of Table B-3
show that the coefficient of interest is not statistically significant under any definition
of syndicate role depth. Moreover, it turns positive for specifications where Lead or
Role Depth are dependent variables.
14Results from this test are unreported in this paper, but are available upon request.
30
4.2.2 Risky Borrowers
The previous section shows that more exposed banks, in an effort to reduce liquid-
ity risks associated with revolver commitments, take on less important roles following
Lehman’s collapse. A natural question then arises: do these adjustments depend on
borrower characteristics? In other words, do banks actively manage their risk-sharing
arrangements more for borrowers who pose a greater liquidity concern?
I define borrowers that pose a greater liquidity concern as those having a non-
investment grade credit rating. Non-investment grade borrowers are considered worse
credits, who are less likely to have access to alternative sources of funding when their
relationship lender runs into trouble. As a result, they are more likely to draw down on
banks’ credit lines for precautionary purposes. As such, I hypothesize that incentives for
risk-sharing adjustments should be more intense for non-investment grade borrowers,
relative to investment-grade ones.
To test this hypothesis, I employ Dealscan’s investment-grade classification and
break out the main sample into two subsamples: investment-grade and non-investment
grade borrowers. I rerun regression (6) separately on these two subsamples, and com-
pare the coefficient on Exposure LEHb,t ∗ Postt between them. For all regressions, I
employ both measures of exposure based on the number (Panel A), and dollar amount
(Panel B) of revolver exposure to Lehman respectively, and confirm that the conclu-
sions are the same under both measures. The results, presented in Table 6, show that
risk-sharing adjustments by exposed banks are primarily concentrated in the sample of
risky borrowers. In particular, the first two columns (I and II), which seek to explain
a bank’s depth of role, reveals a negative coefficient on Exposure LEHb,t ∗ Postt for
both sub samples. However, the magnitude of such a coefficient is five times larger for
non investment-grade borrowers. Furthermore, while the effect is strongly statistically
significant for these borrowers, it is no longer significant for investment-grade borrowers.
31
A similar pattern emerges when examining a bank’s decision to be an impor-
tant lender (“Important”) as we see in columns IV and V. Here, the coefficient on
Exposure LEHb,t ∗Postt is ten times greater in magnitude for the non-investment grade
subsample, compared to the investment-grade one. As a robustness test, I estimate a
pooled regression for the entire sample, and interact Exposure LEHb,t ∗ Postt with the
Investment Grade dummy variable (columns III and VI). As can be seen, the coefficient
on the triple-interaction variable is positive, and highly significant in the case where
Important is the dependent variable. Overall, the results suggest that banks do actively
manage their risk-sharing capacity via adjusting their roles. Moreover, they are more
likely to do so when the borrowers pose greater liquidity risks.
4.3 Syndicate Diversification
4.3.1 Exposed Lead Banks Choose More Distant Members
Results in Section 4.2 indicate that lead arranging roles are not affected by banks’
exposure in a significant way. If relationships are sticky and lead banks cannot offload
liquidity risk by reducing the depth of their role, do they structure their syndicates in a
way that reduces their exposure to future liquidity risk? Here I argue that exposed lead
banks are likely to form more diversified syndicates by choosing more distant co-lenders
following the shock. Choosing more distant co-syndicators benefits the exposed lead
arranger, as she is less likely to take on additional liquidity risk arising from the default
of other syndicate members when she is also in trouble.
To examine this diversification hypothesis, I employ a modified version of specifica-
32
tion (5), as follows:
Db,f,l,t =αb,f + SICf + β1Postt + β2Exposureb,t + β3 ∗ Exposureb,t ∗ Postt
+ β4 ∗ Leadb,f,l,t + β5 ∗ Leadb,f,l,t ∗ Postt + β6 ∗ Leadb,f,l,t ∗ Exposureb,t (7)
+ β7 ∗ Leadb,f,l,t ∗ Exposureb,t ∗ Postt + γ′Xb,t + ρ′Zf,t + εb,f,l,t
The dependent variable, Db,f,l,t, is a measure of distance between bank b and other
syndicate members (“syndicate diversification”), defined in Section 3.2. The coefficient
of interest is now that on the triple interaction variable, β7, which measures the effect
of the lead arranger’s exposure on syndicate diversification following the shock.
The results examining this hypothesis is presented in Table 7. Distance is measured
based on borrower SIC 1-digit (i), SIC 2-digit (ii), and Fama and French’s 49 industry
classification (iii). The first three columns estimate the average effect of exposure on
distance for all syndicate participants, regardless of what role they take. The estimated
coefficient of interest is positive, consistent with the hypothesis that more exposed
banks forms more distant syndicates. The effect, however, is not statistically robust
across measures of distance. While it is significant at the 5% level for measure (i), it
is only weakly significant at the 10% level for measure (iii) and no longer retains its
significance for measure (ii).
This is not surprising since not all syndicate members’s exposure levels are expected
to affect their incentives to diversify with respect to co-syndicators. It is the lead ar-
ranger whose diversification concern is the biggest. First, as shown in Section 4.2,
compared to coagents, exposed lead arrangers do not have much option to limit their
risk by taking on less important roles and hence would find lender diversification as
a possible alternative. Second, lead arrangers usually contribute a significant amount
to a syndicated loan, thereby subjecting themselves to a significant liquidity shock
when other members are unable to honor their commitments. Therefore, I estimate
33
specification (7), focusing on the coefficient of interest, β7. The results are provided
in columns (4) and (5), and (6) of Table 7. As can be seen, β7 is positive and sta-
tistically significant. This suggests that more exposed lead banks form more distant
syndicates following the shock. The effect is highly economically significant: for exam-
ple, when syndicate distance is measured based on borrower 1-digit SIC classification, a
one standard deviation increase in the lead bank’s exposure leads to 0.07 units increase
in lender distance (which is equal to about 17% of the sample average). Again, this
result is not obtained with the term loan sample, where the estimated coefficient of
interest is indistinguishable from zero (See columns (IV), (V), and (VI) of Table B-3).
4.3.2 Syndicate Diversification: What Type of Lenders Matters?
Given that more exposed lead banks want to form syndicates with more “distant”
members, I now examine with which members the lead arranger’s diversification con-
cern is the greatest. Arguably, compared to participants, co-leads and co-agents are
more important members who usually contribute a bigger share to a loan and thereby
causing greater liquidity problems for the lead arranger should they default. As such, I
expect the lead bank’s diversification concern with respect to these important lenders
should dominate that with respect to members with participant roles. To examine this
hypothesis, I reconstruct a bank’s distance measure (i) as the average distance between
the bank and other important members; and (ii) as the average distance between the
bank and participants only. I rerun regression (7) for distance measures (i) and (ii) and
report the results in Panels A and B of Table 8, respectively.
Panel A shows that more exposed lead banks form syndicates with more distant im-
portant lenders, following Lehman collapse. The coefficient on Leadb,f,l,t ∗Exposureb,t ∗
Postt is positive and highly statistically significant. A positive coefficient of interest
is also found in Panel B, where the distance measure is calculated between the bank
34
and syndicate participants. However, it is no longer statistically significant, even at the
10% level. The results suggest that an exposed lead bank has diversification concern
in mind when choosing important syndicate members but not when choosing members
with participant roles.
5 Robustness
5.1 Logit Regressions
So far, regressions involving measures of the significance of syndicate roles as dependent
variables are estimated using linear probability models. While being consistent in the
presence of fixed effects, these models produce estimates that imply probabilities outside
the [0,1] range. In Panel A of Table 9, I repeat these analyses using conditional logit
models for Lead and Important, and conditional ordinal logit model for Role Depth,
with the same set of control variables and fixed effects as in (5). As can be seen, the
results are qualitatively similar to those under linear probability models. Noticeably,
the estimated coefficient on Exposure LEHb,t ∗ Postt in the Lead regression is now
statistically significant. As logit regressions with many fixed effects may suffer from
the incidental parameter problem (e.g. Greene (2004)), all inferences in this paper are
made with reference to results from linear probability models.
5.2 Removing Banks with Zero Exposure
As discussed in Section 3.3, non-exposed banks are remarkably different from exposed
banks along several dimensions. In Panel B of Table 9, I show that the main risk-
sharing results are robust to the exclusion of non-exposed banks. In particular, the
coefficient estimates are essentially the same as those from original tests, both in eco-
nomic magnitude and statistical significance. Section 3.3 also shows that conditioning
35
on being exposed, banks with high and low exposure levels do not differ from each other
except in banks’ asset size. In unreported results, I interact bank size with Postt in all
regressions and confirm that the main results remain the same qualitatively.
5.3 Pre-exsiting Trend
One important concern with my empirical strategy is that the results might purely pick
up a trend in the data that is unrelated to banks’ liquidity risk arising from revolver
draw-down post Lehman bankruptcy. For example, there might be some time-invariant
characteristics of the data which induce more exposed banks to decrease the depth of
their role over time, relative to less exposed ones. In order to address this concern, I
perform a set of “placebo” tests, where I move the event date to June 15, 2007, and the
sample period from March 15, 2004 to September 14, 2008.15 If the same results in the
original tests are found in this “placebo” sample, my results would be unconvincing.
Panel C of table 9 reports the results of these tests, which confirms absence of a
pre-existing trend. In particular, the coefficient on Exposure LEHb,t ∗ Postt switches
its sign and becomes positive in tests on changes in lender roles. The coefficient on
Leadb,f,l,t ∗Exposureb,t ∗Postt turns negative, indicating that more exposed lead banks
are more likely to keep closer syndicates in the “post-event” period. All coefficients of
interest are, however, now statistically indistinguishable from zero.
6 Concluding Remarks
I provide evidence that banks actively manage their risk-sharing arrangements in syndi-
cated revolving lines of credit when facing liquidity risk. Using the collapse of Lehman
Brothers as a shock to bank liquidity risk exposure arising from undrawn revolvers, I
15The decision about the “placebo” date and sample period is to ensure I have the same number ofmonths pre- and post-event as in the original tests.
36
examine how risk-sharing arrangements alter among banks with different levels of ex-
posure to the shock. The syndicate structure of multiple participants provides a useful
setting to identify this exposure, as different banks have different direct exposure via
their level of co-syndication with Lehman.
I exclusively examine this question at the intensive lending margin, i.e., I look only
at firms that borrow from the same banks both before and after the crisis. Looking
within-relationship allows me to control for factors that influence the lender-borrower
match, and help me abstract from confounding factors that might affect the relationship
exit decisions. I find that exposed banks that continue to lend to the same firm do not
worsen the rates charged or reduce the amount of commitment relative to other banks,
following the shock. However, the results suggest that they were able to do so thanks
to active management of risk-sharing arrangements. More exposed banks that join
revolver syndicates do so in less important roles. Banks that stay as lead arrangers to
the same firm form more diversified syndicates. These risk-sharing adjustments would
help lenders limit their exposure to future liquidity shocks.
In this paper, I have focused on US commercial banks only. It would be inter-
esting to extend this study to other types of financial institutions that participate in
the syndicated loan market. Some preliminary analyses reveal that risk-sharing ad-
justments following Lehman’s collapse are more concentrated in non-commercial-bank
institutions. This could be further evidence suggesting the advantage of commercial
banks in providing liquidity.
37
References
Acharya, V. V. and Mora, N. (2012). Are banks passive liquidity backstops? deposit
rates and flows during the 2007-2009 financial crisis. NYU Working Paper.
Ball, R., Bushman, R. M., and Vasvari, F. P. (2008). The debt-contracting value of
accounting information and loan syndicate structure. Journal of Accounting Research,
46(2):247–287.
Cai, J., Saunders, A., and Steffen, S. (2011). Syndication, interconectedness, and
systemic risk. Working paper, NYU Stern School of Business, (5).
Chava, S. and Roberts, M. R. (2008). How does financing impact investment? the role
of debt covenants. The Journal of Finance, 63(5):2085–2121.
Cornett, M. M., McNutt, J. J., Strahan, P. E., and Tehranian, H. (2011). Liquidity risk
management and credit supply in the financial crisis. Journal of Financial Economics,
101(2):297 – 312.
Dennis, S. A. and Mullineaux, D. J. (2000). Syndicated loans. Journal of Financial
Intermediation, 9(4):404 – 426.
Diamond, D. W. (1984). Financial intermediation and delegated monitoring. The
Review of Economic Studies, 51(3):393–414.
Diamond, D. W. and Dybvig, P. H. (1983). Bank runs, deposit insurance, and liquidity.
Journal of Political Economy, 91(3):401–419.
Esty, B. C. and Megginson, W. L. (2003). Creditor rights, enforcement, and debt
ownership structure: Evidence from the global syndicated loan market. Journal of
Financial and Quantitative Analysis, 38(1):37–60.
38
Fama, E. and French, K. (1994). Industry cost-of-equity capital. Working Paper,
University of Chicago and Yale University.
Gatev, E. and Strahan, P. E. (2009). Liquidity risk and syndicate structure. Journal
of Financial Economics, 93(3):490 – 504.
Glenn Hubbard, K. K. and Palia, D. (2002). Are there bank effects in borrowers’
costs of funds? evidence from a matched sample of borrowers and banks. Journal of
Business, 75(4):559–581.
Greene, W. (2004). Fixed effects and bias due to the incidental parameters problem in
the tobit model. Econometric Reviews, 23(2):125–147.
Holmstrom, B. (1979). Moral hazard and observability. The Bell Journal of Economics,
10(1):pp. 74–91.
Holmstrom, B. and Tirole, J. (1997). Financial intermediation, loanable funds, and the
real sector. The Quarterly Journal of Economics, 112(3):663–691.
Irani, R. (2012). Bank health and corporate liquidity provision. Working Paper, Uni-
versity of Illinois at Urbana Champagne.
Ivashina, V. (2009). Asymmetric information effects on loan spreads. Journal of Fi-
nancial Economics, 92(2):300 – 319.
Ivashina, V. and Scharfstein, D. (2010). Bank lending during the financial crisis of
2008. Journal of Financial Economics, 97(3):319 – 338.
Ivashina, V. and Sun, Z. (2011). Institutional stock trading on loan market information.
Journal of Financial Economics, 100(2):284 – 303.
Jones, J. D., Lang, W. W., and Nigro, P. J. (2005). Agent bank behavior in bank loan
syndications. Journal of Financial Research, 28(3):385–402.
39
Kashyap, A. K., Rajan, R., and Stein, J. C. (2002). Banks as liquidity providers:
An explanation for the coexistence of lending and deposit-taking. The Journal of
Finance, 57(1):33–73.
Khwaja, A. I. and Mian, A. (2005). Do lenders favor politically connected firms? rent
provision in an emerging financial market. The Quarterly Journal of Economics,
120(4):1371–1411.
Lee, S. W. and Donald (2004). Monitoring, financial distress, and the structure of
lending syndicates. Financial Management, 33(3):107–130.
Leland, H. E. and Pyle, D. H. (1977). Informational asymmetries, financial structure,
and financial intermediation. The Journal of Finance, 32(2):371–387.
Lin, C., Ma, Y., Malatesta, P., and Xuan, Y. (2012). Corporate ownership structure
and bank loan syndicate structure. Journal of Financial Economics, 104(1):1 – 22.
Lin, H. and Paravisini, D. (2011). What’s bank reputation worth? the effect of fraud
on financial contracts and investments. Columbia Working Paper.
Petersen, M. A. (2009). Estimating standard errors in finance panel data sets: Com-
paring approaches. Review of Financial Studies, 22(1):435–480.
Santos, J. (2011). Bank corporate loan pricing following the subprime crisis. Review of
Financial Studies, 24(6):1916–1943.
Schnabl, P. (2012). The international transmission of bank liquidity shocks: Evidence
from an emerging market. The Journal of Finance, 67(3):897–932.
Sufi, A. (2007). Information asymmetry and financing arrangements: Evidence from
syndicated loans. The Journal of Finance, 62(2):629–668.
40
Table 1: Summary Statistics
Pre Post Difference (Post - Pre) t-statsPanel A: Banks
Assets ($ bil) 741.28 948.52 207.24*** 7.48ROA 0.0032 0.0003 -0.0029*** -25.52Core Deposits 0.4064 0.4364 0.0301*** 4.07Subordinated debt 0.0173 0.0167 -0.0006 -1.36Capital 0.1275 0.1091 -0.0184*** -4.58Loan loss 0.00128 0.00318 0.0019*** 31.45Exposure LEH (#) 0.0419 0.0353 -0.0067*** 6.76Exposure LEH ($) 0.0595 0.0511 -0.0084 -6.05
Panel B: FirmsSales ($ mil) 5095.1 3936.4 -1158.7*** -3.23Leverage 0.2933 0.3213 0.0280*** 4.2Interest Coverage 33.715 5.61 -28.105** -2.4Net Working Capital 26.174 5.604 - 20.571** 2.39Tangibles 0.512 0.513 0.0017 0.22Investment Grade 0.5886 0.2882 -0.3004*** -17.84
Panel C: RevolversAll-in-drawn Spread 90.4 296.2 205.8*** 52.88Amount ($ mil) 1118.6 922.67 -195.91*** -3.03Maturity 44.805 29.702 -5.103*** -22.58Distance (SIC 1-digit) 0.373 0.38 0.019 1.62Distance (SIC 2-digit) 0.391 0.397 0.02 1.37Distance (FF 49 ind) 0.393 0.401 0.022 1.85*# Banks 50 50# Firms 311 311# Lines 725 405# Bank-firm-loan triples 2186 1277
Panels A, B, and C compare banks’, borrowers’ and revolvers’ average characteristicsin the pre- and post- Lehman periods for all observations included in the sample. Pre:June 1, 2005 to September 14, 2008; Post : September 15, 2008 to December 31, 2009.T-stats are pooled for differences across means. ***, **, and * represent significanceat the 1%, 5%, and 10%, respectively. Exposure LEH is the fraction of revolversoutstanding at the time of loan origination that the bank co-syndicate with Lehman,where both the bank and Lehman are important lenders. Exposure LEH is measuredusing: (i) the number (Exposure LEH (#)) and (ii) the dollar amount (Exposure LEH($)) of revolvers. Distance is the syndicate diversification measure, defined in (3). Thismeasure is calculated based on (i) borrower 1-digit SIC code, (ii) borrower 2-digit SICcode, and (iii) borrower 49 industry classification in Fama and French (1994). All othervariables are defined in Table B-2.
41
Table 2: Exposure and Pre-Lehman Sample Characteristics
Exp (I) Non-Exp (II) (I)-(II) t-stat High Exp (III) Low Exp(IV) (III)-(IV) t-stat
Pabel A: Banks
Assets ($ bil) 274.44 24.05 250.39** -2.58 438.74 96.45 342.29* 1.85ROA 0.003 0.003 0 0.37 0.003 0.003 0 -0.02Core Deposits 0.37 0.594 -0.226*** -4.15 0.322 0.422 -0.1 -1.08Subordinated debt 0.019 0.008 0.011*** 2.9 0.018 0.02 -0.002 -0.18Capital 0.178 0.098 0.08 1.62 0.184 0.17 0.014 0.14Loan loss 0.0014 0.0009 0.0005 0.99 0.0017 0.0011 0.0006 0.63
Panel B: Firms
Sales ($ mil) 5302.5 935.7 4366.8*** 3.94 6208.9 2318.2 3890.7*** 6.73Leverage 0.294 0.289 0.005 0.89 0.295 0.289 0.006 0.67Interest Coverage 30.6 100.7 -70.1** -2.11 29.193 35.312 -6.119 -0.4Net Working Capital 26.872 10.762 16.11 0.5 28.348 21.932 6.416 0.39Tangibles 0.494 0.642 -0.148*** 6.51 0.482 0.538 -0.057*** -4.85Firm Age 25.725 20.654 5.071*** 3.41 26.107 24.467 1.65** 2.13
Panel C: Revolvers
All-in-drawn Spread 88.328 129 -40.682*** -4.81 82.891 105.5 -22.563*** -5.11Amount ($ mil) 1151.4 461.2 690.2*** 3.42 1319 599 720*** 6.83Maturity 44.825 44.398 0.427 0.2 44.56 45.7 -1.14 -1.06Distance (SIC 1-digit) 0.358 0.715 -0.358*** 26.3 0.334 0.431 -0.093*** -16.33Distance (SIC 2-digit) 0.377 0.744 -0.365*** -24.55 0.351 0.453 -0.098 -15.68Distance (FF 49 ind) 0.377 0.75 -0.37 -25.16 0.352 0.455 -0.1 -16.14
# Banks 27 23 14 13# Firms 347 47 322 149# Facilities 931 78 871 292
This table reports pre-Lehman banks’, borrowers’ and loans’ characteristics for banks with different exposure to Lehman’srevolving lines of credit. The sample includes revolvers originated over the period from June 1, 2005 to September 14, 2008.Exp (I)/Non-Exp (II) denotes the sample of banks that have positive/zero exposure to Lehman revolvers in the post Lehmanbankruptcy period. High Exp (III)/ Low Exp (IV) denotes the sample of exposed banks that are above/below the medianof the post Lehman empirical exposure distribution. All variables appearing in this table are defined in Table 1 and TableB-2. Values in columns (I), (II), (III) and (IV) are simple averages. T-statistics pertain to the two-sided (pooled) t-testfor differences in means across groups. For banks’ characteristics, each observation is the time series average of a bank’scharacteristics. For borrowers’ and loans’ characteristics, each observation is a loan-firm-bank triple. ***, **, and * denotestatistical significance at the 1%, 5%, and 10%, respectively.
42
Table 3: Effect on Aggregate Lending
∆ All ∆ Revolvers ∆ Term Loans
Constant -0.509 -0.503 -0.604(-4.07)*** (-4.09)*** (-3.80)***
Exposure LEH -3.021 -3.104 -1.874(-2.37)** (-2.87)*** (-0.50)
Bank controls Yes Yes Yes
N 96 87 66R2 0.1249 0.1264 0.0608
The dependent variable is the 09/15/2007- 09/14/2008 to 09/15/2008 to 09/15/2009 changein logs of the number of loans originated. The main explanatory variable of interest, Expo-sure LEH, is the fraction of the dollar amount of revolvers that the bank co-syndicates withLehman where both the bank and Lehman are important lenders, and which is outstandingon September 15, 2008. Bank controls include changes in log assets, ROA, loan loss ratios,and equity ratios, which are defined in Table B-2. Loans to real estate (SIC codes 1520-1600)and financial companies (SIC codes 6000-6799) are excluded. Standard errors are White-heteroskedasticity consistent. T-statistics are reported in parentheses under the coefficientestimates. ***, **, and * denote significance at the 1%, 5% and 10% level, respectively.
43
Table 4: Effect on Revolvers’ Contract Terms
All Contract Terms - Revolvers
Maturity (I) Spread (II) Amount (III)
Post -0.556 101.332*** -0.137(-0.16) (7.37) (-1.21)
Exposure LEH($) 67.824 45.19 0.119(1.31) (0.27) (0.09)
Exposure LEH($) * Post -66.27** -73.548 -0.529(-2.37) (-0.51) (-0.59)
N 3039 2704 3052R2 0.5877 0.8865 0.7867
This table reports coefficient estimates from regressions relating the terms of newly formedsyndicates to the banks’ existing exposure to syndicated lending with Lehman via revolvinglines of credit. The sample includes facilities originated from June 1, 2005 to December 31,2009. Dependent variables are Maturity (in months), Spread (in bps), and Amount (log offacility amount), respectively. Exposure LEH is the ratio of the dollar amount of revolversoutstanding at the time of loan origination that the bank co-syndicates with Lehman, whereboth the bank and Lehman are important lenders. Panel A employs a measure of ExposureLEH where the number of revolvers are used in the calculation. Post is a dummy variableequal to 1 if the loan is issued post September 15, 2008, and 0 otherwise. All regressionsinclude bank controls, firm controls, bank-firm fixed effects and borrower 2-digit SIC fixedeffects. Control variables are defined in Table B-2. Loans to real estate (SIC codes 1520-1600)and financial companies (SIC codes 6000-6799) are excluded. Standard errors are clustered bybank and also by firm. T-statistics are reported in parentheses under the coefficient estimates.***, **, and * denote significance at the 1%, 5% and 10% level, respectively.
44
Table 5: Effect on Depth of Role
Role Depth Lead Important
Post 0.104* 0.006 0.099**(1.76) (0.17) (2.36)
Exposure LEH -0.183 -0.844 0.661(-0.09) (-0.69) (0.78)
Exposure LEH -2.424** -0.446 -1.979**** Post (-2.21) (-0.70) (-2.91)
N 3052 3052 3052R2 0.8287 0.8041 0.7689
This table presents results upon examining the effect of banks’ exposure to syndicated lendingwith Lehman on the significance of their role in a syndicate. The sample includes revolversoriginated from June 1, 2005 to December 31, 2009. The dependent variable, the significanceof a bank’s role in a syndicate, is proxied by three variables, Role Depth, Lead and Important.Role Depth takes the value of 1, 2, and 3 if the bank acts as a participant, coagent, andlead arranger, respectively. Lead is a dummy variable equal to 1 if the bank acts as the leadarranger for the loan, and 0 otherwise. Important is a dummy variable taking the value of 1 ifthe bank is either a lead arranger or a co-agent, and 0 otherwise. Exposure LEH is measuredas the ratio of the dollar amount of revolvers outstanding at the time of loan originationthat the bank co-syndicates with Lehman, where both the bank and Lehman are importantlenders. Post is a dummy variable equal to 1 if the loan is issued post September 15, 2008, and0 otherwise. All regressions include bank controls, firm controls, bank-firm fixed effects andborrower 2-digit SIC fixed effects. All control variables are defined in Table B-2. Standarderrors are clustered by bank and also by firm. Loans to real estate (SIC codes 1520-1600)and financial companies (SIC codes 6000-6799) are excluded. T-statistics are reported inparentheses under the coefficient estimates. ***, **, and * denote significance at the 1%, 5%and 10% level, respectively.
45
Table 6: Risky Borrowers
Importance level Important
IG (I) Non-IG (II) All (III) IG (IV) Non-IG (V) All (VI)
Post 0.015 0.204*** 0.16* 0.011 0.16*** 0.159***(0.13) (3.62) (1.86) (0.14) (3.63) (3.2)
Exposure LEH -1.697 1.312 -0.237 -0.054 1.211 0.926(-0.96) (0.24) (-0.10) (-0.06) (0.44) (0.93)
Exposure LEH * Post -0.733 -4.047** -2.964** -0.288 -3.112*** -2.81***(-1.21) (-2.34) (-1.99) (-0.73) (-2.76) (-3.17)
Investment Grade -0.065 -0.027(-0.74) (-0.59)
Investment Grade -0.071 -0.474* Exposure LEH (-0.06) (-0.56)Investment Grade -0.167 -0.168**** Post (-1.46) (-2.71)Investment Grade 1.857 2.312**** Exposure LEH * Post (1.22) (2.59)
N 1501 1551 3052 1501 1551 3052R2 0.8826 0.8505 0.8292 0.8462 0.7973 0.7704
This table presents results upon examining the effect of banks’ exposure to syndicated lendingwith Lehman on the significance of their role in a syndicate. The sample includes revolversoriginated from June 1, 2005 to December 31, 2009. Role Depth takes the value of 1, 2,and 3 if the bank acts as a participant, coagent, and lead arranger, respectively. Lead is adummy variable equal to 1 if the bank acts as the lead arranger for the loan, and 0 otherwise.Important is a dummy variable taking the value of 1 if the bank is either a lead arranger ora co-agent, and 0 otherwise. IG, Non-IG are the subsamples pertaining to investment gradeborrowers, and non-investment grade borrowers, respectively. All refers to the full sample.Exposure LEH is measured as the ratio of the dollar amount of revolvers outstanding at thetime of loan origination that the bank co-syndicates with Lehman, where both the bank andLehman are important lenders. Post is a dummy variable equal to 1 if the loan is issued postSeptember 15, 2008, and 0 otherwise. All regressions include bank controls, firm controls,bank-firm fixed effects and borrower 2-digit SIC fixed effects. All control variables are definedin Table B-2. Standard errors are clustered by bank and also by firm. Loans to real estate (SICcodes 1520-1600) and financial companies (SIC codes 6000-6799) are excluded. T-statisticsare reported in parentheses under the coefficient estimates. ***, **, and * denote significanceat the 1%, 5% and 10% level, respectively.
46
Table 7: Syndicate Diversification
SIC 1-digit SIC 2-digit FF 49 ind SIC 1-digit SIC 2-digit FF 49 ind
Post -0.018 0.009 0.005 -0.016 0.009 .006(-0.69) (0.36) (0.22) (-0.62) (0.38) (0.25)
Exposure LEH 0.44 -0.215 -0.215 0.503 -0.17 -0.167(0.63) (-1.44) (-1.02) (0.79) (-1.19) (-0.96)
Exposure LEH 0.75** 0.408 0.456* 0.6** 0.271 0.319* Post (2.49) (1.58) (1.82) (2.04) (1.15) (1.38)Lead 0.049* 0.029* 0.034*
(1.85) (1.73) (1.93)Lead*Post -0.041* -0.03* -0.033**
(-1.89) (-1.68) (-2)Lead*Exposure LEH -0.906** -0.662*** -0.729***
(-2.36) (-2.58) (2.86)Exposure LEH 0.9*** 0.722*** 0.76***Lead*Post (2.96) (2.58) (2.89)
N 2967 2967 2967 2967 2967 2967R2 0.8183 0.8567 0.8541 0.8199 0.8577 0.8552
This table reports coefficient estimates from regressions relating the distance between thebanks and their newly formed syndicate members to the banks’ existing exposure to syndicatedlending with Lehman via revolving lines of credit. The sample period is from June 1, 2005 toDecember 31, 2009. Exposure LEH is measured as the ratio of the dollar amount of revolversoutstanding at the time of loan origination that the bank co-syndicates with Lehman, whereboth the bank and Lehman are important lenders. Post is a dummy variable equal to 1 if theloan is issued post September 15, 2008, and 0 otherwise. SIC 1-digit/SIC 2-digit/FF 49 indrefers to specifications where the dependent variable, Distance, is measured based on 1-digit/2-digit borrower SIC codes/Fama and French (1994)’s 49 industry definition. All regressionsinclude bank controls, firm controls, bank-firm fixed effects and borrower 2-digit SIC fixedeffects. Control variables are defined in Table B-2. Standard errors are clustered by bankand also by firm. Loans to real estate (SIC codes 1520-1600) and financial companies (SICcodes 6000-6799) are excluded. T-statistics are reported in parentheses under the coefficientestimates. ***, **, and * denote significance at the 1%, 5% and 10% level, respectively.
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Table 8: Syndicate Diversification: Important Lenders Versus Participants
Panel A: Syndicate Diversification: With Important Lenders Only
Sic 1-digit Sic 2-digit FF 49 ind Sic 1-digit Sic 2-digit FF 49 ind
Post -0.043* -0.016 -0.017 -0.043* -0.016 -0.016(-1.68) (-0.77) (-0.76) (-1.72) (-0.73) (-0.71)
Exposure LEH 0.498 -0.202 -0.184 0.593 -0.126 -1.103(0.59) (-1) (-0.64) (0.79) (-0.68) (-0.45)
Exposure LEH * Post 0.85** 0.423 0.456* 0.697** 0.3 0.321(2.27) (1.62) (1.72) (1.99) (1.31) (1.36)
Lead 0.093** 0.076*** 0.08***(2.3) (2.92) (2.83)
Lead*Post -0.039 -0.036 -0.04(-1.11) (-1.23) (-1.31)
Lead*Exposure LEH -1.579*** -1.3*** -1.369***(-2.84) (-3.52) (-3.64)
Lead*Exposure LEH 0.929** 0.796** 0.892***Post (2.22) (2.17) (2.32)
N 2850 2850 2850 2850 2850 2850R2 0.8458 0.8862 0.8791 0.8489 0.8879 0.8810
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Panel B: Syndicate Diversification: With Participants OnlySic 1-digit Sic 2-digit FF 49 ind Sic 1-digit Sic 2-digit FF 49 ind
Post 0.032 0.055* 0.049 0.034 0.056* 0.05(1.03) (1.70) (1.58) (1.06) 1.67 (1.55)
Exposure LEH 0.406 -0.355* -0.318 0.42 -0.353* -0.319(0.65) (-1.68) (-1.33) (0.7) (-1.72) (-1.41)
Exposure LEH * Post 0.648** 0.423 0.463 0.522* 0.278 0.344(2.31) 1.38 (1.59) (1.82) 0.94 (1.2)
Lead 0.005 -0.026 -0.026(0.14) (-0.75) (-0.72)
Lead*Post -0.039 -0.023 -0.02(-1.25) (-0.59) (-0.5)
Lead*Exposure LEH -0.268 0.146 0.12(-0.51) (0.3) (0.24)
Lead*Exposure LEH 0.812 0.668 0.553*Post (1.49) (1.11) (0.92)N 2647 2647 2647 2647 2647 2647R2 0.7628 0.7896 0.79 0.7634 0.7903 0.7906
This table reports coefficient estimates from regressions relating the distance of newlyformed syndicates to the banks’ existing exposure to syndicated lending with Lehmanvia revolving lines of credit. The sample period is from June 1, 2005 to December31, 2009. In Panel A, Syndicate diversification is measured as the average distancebetween the lead arranger(s) and other important lenders (co-leads and co-agents) only.In Panel B, Syndicate diversification is measured as the average distance between thelead arranger(s) and syndicate participants only (co-leads and co-agents are excluded).Exposure LEH is measured as the ratio of the dollar amount of revolvers outstandingat the time of loan origination that the bank co-syndicates with Lehman, where boththe bank and Lehman are important lenders. Post is a dummy variable equal to 1 if theloan is issued post September 15, 2008, and 0 otherwise. SIC 1-digit/SIC 2-digit/FF 49ind refers to specifications where the dependent variable, distance, is measured basedon 1-digit/2-digit borrower SIC codes/Fama and French (1994)’s 49 industry definition.All regressions include bank controls, firm controls, bank-firm fixed effects and borrower2-digit SIC fixed effects. Control variables are defined in Table B-2. Standard errorsare clustered by bank and also by firm. Loans to real estate (SIC codes 1520-1600) andfinancial companies (SIC codes 6000-6799) are excluded. T-statistics are reported inparentheses under the coefficient estimates. ***, **, and * denote significance at the1%, 5% and 10% level, respectively.
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Table 9: Robustness Tests
Panel A: Logit Regressions
Role (I) Lead (II) Important (III)
Post 1.268*** 1.606* 1.805***(10.78) (4.031) (12)
Exposure LEH 6.186 -30.236 15.68(0.74) (2.18) 2.39
Exposure LEH -22.47*** -39.168*** -32.356**** Post 23.61 (10.86) (27.28)
N 3052 3052 3052PseudoR2 0.8689 0.9012 0.8787
Panel B: Excluding Banks with Zero Exposure
Role Lead Important Maturity Sic Sic FFDepth 1-digit 2-digit 49 ind
Post 0.106 0.004 0.101** -1.677 -0.002 0.023 0.021( 1.47) (0.12) (2.17) (-0.51) (-0.09) (1.01) (1.02)
Exposure LEH -0.132 -0.817 0.684 68.38 0.523 -0.142 -0.136-0.07 (-0.67) ( 0.79) (1.34) (0.8) (-0.96) (-0.72)
Exposure LEH -2.448** -0.477 -1.972*** -55.787** 0.448 0.118 0.146* Post (-2.02) 0.71 (-2.66) (-2.20) ( 1.62) (0.56) (0.77)Lead 0.053** 0.033* 0.038**
(2.01) (1.92) (2.14)Lead*post -0.048** -0.037** -0.041***
(-2.18) (-2.17) (-2.63)Lead* Exposure LEH -0.959** -0.69*** -0.765***
(-2.46) (2.7) (-2.99)Exposure LEH 0.972*** 0.798*** 0.845****Post * Lead (3.19) (2.97) (3.40)
N 2907 2907 2907 2895 2849 2849 2849R2 0.8221 0.7992 0.7604 0.5832 0.7759 0.8259 0.8212
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Panel C: Placebo Tests
Role Lead Important Maturity Sic Sic FFDepth 1-digit 2-digit 49 ind
Post -0.057 -0.028 -0.029 -4.144** -0.026 -0.013 -0.001(-1.36) (-1.58) (-0.86) (-2.04) (-0.86) (-0.48) (-0.04)
Exposure LEH 1.127 -0.278 1.405*** 37.575 0.791 0.307 0.195(1.63) (-0.61) (2.98) (1.22) (1.05) (0.53) (0.29)
Exposure LEH 0.643 0.261 0.382 8.198 0.989* 0.835* 0.611* Post (1.09) (0.79) (0.89) (0.31) (1.94) (1.86) (1.4)Lead 0.004 -0.014 -0.01
(0.14) (-0.65) (-0.46)Lead * Post 0.086 * 0.077** 0.06*
(1.92) (2.11) (1.7)Exposure LEH -0.225 0.042 -0.028*Lead (-0.65) (0.14) (-0.09)Exposure LEH -1.137 -1.026* -0.763* Lead * Post (-1.63) (-1.86) (-1.37)
N 6359 6359 6359 6312 6359 6359 6459R2 0.8361 0.8250 0.4959 0.8340 0.8632 0.8586
This table reports results from robustness tests. Panel A: Presents results from esti-mating (6) using conditional ordinal logit regression for Lead and Important Column(I), and from conditional logit regressions for Role Depth(columns (II) and (III)); Waldstatistics are in parentheses under coefficient estimates. Panels B and C report ro-bustness for the main tests regarding the importance of syndicate roles and syndicatediversification. Role Depth takes the value of 1, 2, and 3 if the bank acts as a partici-pant, coagent, and lead arranger, respectively. Lead is a dummy variable equal to 1 ifthe bank acts as the lead arranger for the loan, and 0 otherwise. Important is a dummyvariable taking the value of 1 if the bank is either a lead arranger or a co-agent, and 0otherwise. SIC 1-digit/SIC 2-digit/FF 49 ind refers to specifications where the depen-dent variable, distance, is measured based on 1-digit/2-digit borrower SIC codes/Famaand French (1994)’s 49 industry definition. Panel B: Excluding zero-exposure banksfrom the sample; Panel C: the placebo period from March 1, 2004 to September 15,2008, with the placebo event date being June 15, 2007. All regressions include bankcontrols, firm controls, bank-firm fixed effects and borrower 2-digit SIC fixed effects.Exposure LEH is the fraction of the dollar amount of outstanding revolvers that thebank co-syndicates with Lehman, where both the bank and Lehman are importantlenders. Control variables are defined in Table B-2. Standard errors are clustered bybank and also by firm.
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Appendix A Distance Between Two Lenders: An
Example
Table A-1 provides an example of how to calculate “distance” between two lenders,Wells Fargo and Bank of America, on June 20, 2005 when they syndicated Dealscanloan facility ID 181374.
Table A-1: Distance Between Two LendersThis table describes how to calculate distance between Bank of America (BOA) and WellsFargo (WF). SIC 1-digit denotes the borrower 1-digit SIC code. wBOA and wWF are theweights BOA and WF invest in an industry, respectively.
SIC 1-digit wBOA wWF (BOA−WF )2
0 0.31377% 0.24908% 0.00004%1 7.76837% 12.94392% 0.26786%2 9.68612% 4.40934% 0.27844%3 11.69250% 11.39405% 0.00089%4 13.26726% 6.23275% 0.49484%5 10.74354% 15.24984% 0.20307%6 30.70943% 27.28561% 0.11723%7 10.61945% 17.71217% 0.50307%8 5.18005% 3.87564% 0.01701%9 0.01950% 0.64760% 0.00395%sum 100.00000% 100.00000% 1.88640%Distance 13.73464%
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Appendix B Additional Tables
Table B-2: Definition of Variables
Variable Name Definition
Banks (Source:Call Reports)
Assets RCFD2170ROA RIAD4340/RCFD2170Core Deposits (RCON2702+RCON2215)/RCFD2170Subordinated Debt RCFD3200/RCFD2170Capital RCFD3210/RCFD2170Loan Loss (RIAD4635-RIAD4605)/RCFD2170
Firms (Source: Compustat)
Sales SaleqLeverage (Dlttq+dlcq)/atqInterest Coverage Oibdpq/xintqNet Working Capital (atq-lctq)/(Dlttq+dlcq)Tangibles (ppentq+invtq)/atqFirm Age Year of facility first year in Compustat
Loans (Source: Dealscan)
Al-in-drawn Spread AllInDrawnAmount FacilityamtMaturity MaturityInvestment Grade Market Segment
All income variables from Call Reports (RIAD4340, RIAD4635, and RIAD4605) areadjusted to be quarterly values from the reported year-to-date figures. Definition ofitem abbreviations are as follows. RCFD2170 is total assets; RIAD4340 is net income(loss); RCON2702 is the amount of deposits accounts of $100,000 or less; RCON2215is the amount of transaction accounts; RCFD3200 is the amount of subordinated notesand debentures; RCFD3210 is the total amount of equity capital; RIAD4635 is theamount of charge-offs on allowance for loan and lease losses. RIAD4605 is the amountof recoveries on allowance for loan and lease losses; Dlttq/dlcq is total value of longterm debt/debt in current liabilities; Oibdpq is Operating Income Before Depreciation;xintq is the amount of interest and related expense; lctq is the amount of currentliabilities; ppentq is the value of property, plant, and equipment; invtq is the totalvalue of inventories.
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Table B-3: Effect on Depth of Role and Syndicate Diversification: Term Loans
Role Lead Important SIC 1 SIC 2 FF 49Depth (I) (II) (III) -digit (IV) -digit (V) ind (VI)
Post -0.12 -0.087 -0.034 -0.047 -0.04 -0.042(-0.38) (-0.54) (-0.16) (-1.01) (-0.81) (-0.82)
Exposure LEH 4.346 3.031 1.315 -1.354 -0.988 -1.043(1.3) (0.79) (0.39) (-0.99) (-0.74) (-0.83)
Exposure LEH 2.032 2.407 -0.375 1.203 1.043 1.145* Post (0.45) (1.2) (-0.12) (1.39) (1.1) (1.21)Lead 0.067 0.059 0.065
(1.05) (0.83) (0.92)Lead * Post -0.073 -0.056 -0.081
(-1.18) (-0.8) (-1.05)Lead*Exposure LEH -0.915 -0.803 -0.922
(-0.95) (-0.74) (-0.83)Exposure LEH 0.63 0.397 0.680Lead * Post 0.71 (0.38) (0.62)
N 603 603 603 573 573 573R2 0.7568 0.7231 0.6933 0.8036 0.8325 0.8301
This table presents results upon examining the effect of banks’ exposure to syndicated re-volvers with Lehman on the significance of their role in a syndicate (columns (I)-(III)), andsyndicate diversification (columns (IV)-(VI)). The sample consists of term loans originatedfrom June 1, 2005 to December 31, 2009. The significance of a bank in a syndicate, is proxiedby three variables, Role Depth, Lead, and Important. Role Depth takes the value of 1, 2,and 3 if the bank acts as a participant, coagent, and lead arranger, respectively. Lead is adummy variable equal to 1 if the bank acts as the lead arranger of the loan syndicate, and 0otherwise. Important is a dummy variable equal to 1 if the bank is either a lead arranger ora co-agent of the loan syndicate, and 0 otherwise. SIC 1-digit/SIC 2-digit/FF 49 ind refersto specifications where the dependent variable, distance, is measured based on 1-digit/2-digitborrower SIC codes/Fama and French’s 49 industry definition. Exposure LEH is the ratioof the dollar amount of revolvers outstanding at the time of loan origination that the bankco-syndicate with Lehman, where both the bank and Lehman are important lenders. Post isa dummy variable equal to 1 if the loan is issued post September 15, 2008, and 0 otherwise.All regressions include bank controls, firm controls, bank-firm fixed effects and borrower 2-digit SIC fixed effects. Control variables related to lenders (Log assets, ROA, Core Deposits,Subordinated Debt, Capital and Loan Loss) and borrowers (Log Sales, Leverage, InterestCoverage, Net Working Capital, Tangibles, Firm Age, Investment Grade) are defined in Ta-ble B-2. Standard errors are clustered by bank and also by firm. Loans to real estate (SICcodes 1520-1600) and financial companies (SIC codes 6000-6799) are excluded. T-statisticsare reported in parentheses under the coefficient estimates. ***, **, and * denote significanceat the 1%, 5% and 10% level, respectively.
54