Risk-Sharing in the Syndicated Loan Market: Evidence from Lehman Brothers’ Collapse

Hanh Le ∗ JOB MARKET PAPER†

March 4, 2013

Abstract

I examine the impact of banks’ liquidity risk on their risk-sharing arrangements 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 adjusting syndicate structures might alleviate the negative consequences of a shock to lending supply.

∗Send correspondence to Stern School of Business, New York University, 44 West Fourth Street, Suite 9-193, New York, NY 10012. 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, Dirk Burghardt, Gabriela Coiculescu, Matteo Crosignani, Jason Levine, Anthony Lynch, Rustom Irani, Oliver Randall, Or Shachar, Hyun Song Shin, Stoyan Stoyanov, David Yermack, Shaojun Zhang and seminar 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 paper 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.

1 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 company’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 liquidity 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.

3 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 borrowing 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 levels 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 variables in all regressions. Furthermore, compared to exposed banks, non-exposed banks participate much less frequently in the syndicated loan market. In light of the significant 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-

4 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

5 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 diversification 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 important 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 arrangements 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

6 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 lending 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 focus so far has been primarily on how asymmetric information between borrowers and lenders, and that among syndicate participants, shapes syndicate ownership arrangements. The literature builds upon the basic theoretical assumption that the need for lender monitoring arises because of asymmetric information and moral hazard problems (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

7 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 empirical 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

8 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

9 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-

10 ranger” is the most important member of a syndicate, taking on the primary responsibilities 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 institutions 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 members 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 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 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 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.4 Loan sales can be structured as either assignments or participation.

3the risk that the borrower will not pay back the borrowed amount 4The 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 undrawn

11 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.5 At the time, Lehman Brothers 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,6 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- commitments and $3m in drawn commitments, the buyer will have to pay 0.8*$3m for the drawn amount, but will receive a credit of (1-0.8)*$1m. This credit is to allow for the fact that if the borrower decide to draw on the revolver in the future, the buyer will have to fund 100 cents to the dollar. 5Loan Syndications and Trading Associations,“Examining the Legal and Business Reality of Syn- dicated Leveraged Loan”, WilmerHale, Boston, July 15, 2009. 6See Model Credit Agreement Provisions: Administrative Agent ’s Clawback, §a (2005).

12 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 problem, 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 revolver. If Lehman did not default, Alcoa would demand $100 million, in which case 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,

13 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 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”. 7

Using Dealscan’s information on lender names, geographic location and operation

7In extra tests, I also examine term loans, where liquidity risk-sharing is not a concern. I code the following loan types as term loans if Dealscan’s LoanType contains one of the following: “Delay Draw Term Loan”, “Term Loan”, “Term Loan A” - “Term Loan I”.

14 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. 8

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 exclude 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-

8I thank Sudheer Chava and Michael Roberts for providing the linking ﬁle (see Chava and Roberts (2008)).

15 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”.9 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)).

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.10 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

9This 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. 10Lin and Paravisini (2011) ﬁnd that facilities with complete lender share information tend to be larger in terms of both facility amount and number of participants.

16 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 + i,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;

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.11 11Main coefficient estimates (with t-statistics reported in parentheses) from estimating (1) are as follows:

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.

17 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 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

18 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 loans extended to firms belonging to industry j, over the total dollar amount of loans

12 PJ the bank arranges as a lead lender. Note that j=1 wi,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:

12where a syndicate consists of more than one lead arranger, I divide the facility amounts equally among the lead banks.

19 v u J uX 2 dm,n,t = t (wm,j,t − wn,j,t) (2) j=1

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:

PN−1 n=1 dbn,memn,t D = (3) b,l,t N − 1

th where dbn,memn,t denotes the distance between the n pair of bank b and syndicate member memn, and bn 6= memn.

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

20 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 characteristics. 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 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

21 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 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 exposure 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,

22 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 significance 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 examine 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:

0 ∆Lendingb = α + βExposure LEHb + ρ ∆Xb + b (4)

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 before 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 included in Xb are: Log assetsb, Core Depositsb, ROAb, Loan lossb, and Capital Ratiob.

23 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 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

24 regression speciﬁcation is as follows:

Termsb,f,l,t =αb,f + δSIC + β1Postt + β2Exposure LEHb,t

0 0 + β3Exposure LEHb,t ∗ Postt + γ Xb,t + ρ Zf,t + b,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. P ostt 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.

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

25 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 revolver 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 intensive 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 addresses (i) by excluding firms that do not borrow following the event. In addition, it 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

26 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.

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 exclusively 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

27 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

0 0 + β3 ∗ Exposure LEHb,t ∗ Postt + γ Xb,t + ρ Zf,t + b,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, 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

28 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 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

29 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,

13 β3, to be indistinguishable from zero.

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 commitments 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.

4.2.2 Risky Borrowers

The previous section shows that more exposed banks, in an eﬀort to reduce liquidity 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

13Results from this test are unreported in this paper, but are available upon request.

30 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 compare 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.

A similar pattern emerges when examining a bank’s decision to be an important 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.

31 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 specification (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)

0 0 + β7 ∗ Leadb,f,l,t ∗ Exposureb,t ∗ Postt + γ Xb,t + ρ Zf,t + b,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

32 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 arranger 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 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 statistically significant. This suggests that more exposed lead banks form more distant syndicates following the shock. The effect is highly economically significant: for example, 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 concern is the greatest. Arguably, compared to participants, co-leads and co-agents are

33 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 important 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 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

34 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 economic magnitude and statistical significance. Section 3.3 also shows that conditioning 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 P ostt in all regressions and confirm that the main results remain qualitatively unchanged.

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 exposure 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, 2008.14 If the same

14The decision about the “placebo” date and sample period is to ensure I have the same number of months pre- and post-event as in the original tests.

35 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.

5.4 Liquidity or Reputation?

I have so far attributed changes in syndicate structures to banks’ risk management when they are exposed to a liquidity shock. Another plausible explanation, based on my empirical strategy, relates to a reputational story. For example, banks that cosyndicated more revolvers with Lehman might have suﬀered reputational damage (due to the failure to monitor syndicate members) and hence were given less important roles by syndicate lead arrangers following Lehman’s collapse. In order to separate liquidity from reputation explanations, I repeat my main tests using the bailout of Bear Stearns on March 14, 2008 as a shock to cosyndicating banks’ reputation. Arguably, while Lehman’s failure might pose both liquidity and reputation problems for cosyndicating banks, the event of Bear Stearns would represent a pure reputation story as the company were later acquired by JP Morgan, which assumed Bear Stearns’ existing commitments.

In Panel D of 9, I confirm the absence of a reputational story using the Bear Stearns placebo. I do not find banks which cosyndicated more revolvers with Bear Stearns adjusted either the importance of their roles or syndicate diversification in any statistical significant way, following the company’s March rescue. In results not reported here, I

36 move the Bear Stearns event date to June 22, 2007, the first date the market learned about problems at its hedge fund legs. Again, I do not find any significant changes in syndicate structures for exposed banks. The results reinforce the claim that banks actively manage their liquidity risk exposure in syndicated loans when hit with a negative liquidity shock.

6 Concluding Remarks

I provide evidence that banks actively manage their risk-sharing arrangements in syndicated 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 examine how risk-sharing arrangements alter among banks with different levels of exposure 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.

37 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 adjustments 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.

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41 Table 1: Summary Statistics

Pre Post Diﬀerence (Post - Pre) t-stats Panel A: Banks Assets ($ bil) 741.28 948.52 207.24*** 7.48 ROA 0.0032 0.0003 -0.0029*** -25.52 Core Deposits 0.4064 0.4364 0.0301*** 4.07 Subordinated debt 0.0173 0.0167 -0.0006 -1.36 Capital 0.1275 0.1091 -0.0184*** -4.58 Loan loss 0.00128 0.00318 0.0019*** 31.45 Exposure LEH (#) 0.0419 0.0353 -0.0067*** 6.76 Exposure LEH ($) 0.0595 0.0511 -0.0084 -6.05 Panel B: Firms Sales ($ mil) 5095.1 3936.4 -1158.7*** -3.23 Leverage 0.2933 0.3213 0.0280*** 4.2 Interest Coverage 33.715 5.61 -28.105** -2.4 Net Working Capital 26.174 5.604 - 20.571** 2.39 Tangibles 0.512 0.513 0.0017 0.22 Investment Grade 0.5886 0.2882 -0.3004*** -17.84 Panel C: Revolvers All-in-drawn Spread 90.4 296.2 205.8*** 52.88 Amount ($ mil) 1118.6 922.67 -195.91*** -3.03 Maturity 44.805 29.702 -5.103*** -22.58 Distance (SIC 1-digit) 0.373 0.38 0.019 1.62 Distance (SIC 2-digit) 0.391 0.397 0.02 1.37 Distance (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 characteristics in 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 significance at the 1%, 5%, and 10%, respectively. Exposure LEH is the fraction of revolvers outstanding 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 measured using: (i) the number (Exposure LEH (#)) and (ii) the dollar amount (Exposure LEH ($)) of revolvers. Distance is the syndicate diversification measure, defined in (3). This measure is calculated based on (i) borrower 1-digit SIC code, (ii) borrower 2-digit SIC code, and (iii) borrower 49 industry classification in Fama and French (1994). All other variables are defined in Table B-2.

42 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* 1.85 ROA 0.003 0.003 0 0.37 0.003 0.003 0 -0.02 Core Deposits 0.37 0.594 -0.226*** -4.15 0.322 0.422 -0.1 -1.08 Subordinated debt 0.019 0.008 0.011*** 2.9 0.018 0.02 -0.002 -0.18 Capital 0.178 0.098 0.08 1.62 0.184 0.17 0.014 0.14 Loan 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*** 6.73 Leverage 0.294 0.289 0.005 0.89 0.295 0.289 0.006 0.67 Interest Coverage 30.6 100.7 -70.1** -2.11 29.193 35.312 -6.119 -0.4 Net Working Capital 26.872 10.762 16.11 0.5 28.348 21.932 6.416 0.39 43 Tangibles 0.494 0.642 -0.148*** 6.51 0.482 0.538 -0.057*** -4.85 Firm 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*** -5.11 Amount ($ mil) 1151.4 461.2 690.2*** 3.42 1319 599 720*** 6.83 Maturity 44.825 44.398 0.427 0.2 44.56 45.7 -1.14 -1.06 Distance (SIC 1-digit) 0.358 0.715 -0.358*** 26.3 0.334 0.431 -0.093*** -16.33 Distance (SIC 2-digit) 0.377 0.744 -0.365*** -24.55 0.351 0.453 -0.098 -15.68 Distance (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’s revolving 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 Lehman bankruptcy period. High Exp (III)/ Low Exp (IV) denotes the sample of exposed banks that are above/below the median of the post Lehman empirical exposure distribution. All variables appearing in this table are defined in Table 1 and Table B-2. Values in columns (I), (II), (III) and (IV) are simple averages. T-statistics pertain to the two-sided (pooled) t-test for differences in means across groups. For banks’ characteristics, each observation is the time series average of a bank’s characteristics. For borrowers’ and loans’ characteristics, each observation is a loan-firm-bank triple. ***, **, and * denote statistical significance at the 1%, 5%, and 10%, respectively. 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 66 R2 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 change in 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 with Lehman where both the bank and Lehman are important lenders, and which is outstanding on 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 coefficient estimates. ***, **, and * denote significance at the 1%, 5% and 10% level, respectively.

44 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 3052 R2 0.5877 0.8865 0.7867

This table reports coefficient estimates from regressions relating the terms of newly formed syndicates to the banks’ existing exposure to syndicated lending with Lehman via revolving lines 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 of facility amount), respectively. Exposure LEH is the ratio of the dollar amount of revolvers outstanding at the time of loan origination that the bank co-syndicates with Lehman, where both the bank and Lehman are important lenders. Panel A employs a measure of Exposure LEH where the number of revolvers are used in the calculation. Post is a 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 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 by bank 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.

45 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 3052 R2 0.8287 0.8041 0.7689

This table presents results upon examining the effect of banks’ exposure to syndicated lending with Lehman on the significance of their role in a syndicate. The sample includes revolvers originated from June 1, 2005 to December 31, 2009. The dependent variable, the significance of 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, and lead arranger, respectively. Lead is a dummy 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 or a co-agent, and 0 otherwise. Exposure LEH is measured as the ratio of the dollar amount of revolvers outstanding at the time of loan origination that the bank co-syndicates with Lehman, where both the bank and Lehman are important lenders. Post is a 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. All control variables are defined in Table B-2. Standard errors 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 in parentheses under the coefficient estimates. ***, **, and * denote significance at the 1%, 5% and 10% level, respectively.

46 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 3052 R2 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 lending with Lehman on the significance of their role in a syndicate. The sample includes revolvers originated 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 a dummy 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 or a co-agent, and 0 otherwise. IG, Non-IG are the subsamples pertaining to investment grade borrowers, 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 the time of loan origination that the bank co-syndicates with Lehman, where both the bank and Lehman are important lenders. Post is a 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. All control variables are defined in Table B-2. Standard errors 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 in parentheses under the coefficient estimates. ***, **, and * denote significance at the 1%, 5% and 10% level, respectively.

47 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 2967 R2 0.8183 0.8567 0.8541 0.8199 0.8577 0.8552

This table reports coefficient estimates from regressions relating the distance between the banks and their newly formed syndicate members to the banks’ existing exposure to syndicated lending with Lehman via revolving lines of credit. The sample period is from June 1, 2005 to December 31, 2009. Exposure LEH is measured as the ratio of the dollar amount of revolvers outstanding at the time of loan origination that the bank co-syndicates with Lehman, where both the bank and Lehman are important lenders. Post is a dummy variable equal to 1 if the loan is issued post September 15, 2008, and 0 otherwise. SIC 1-digit/SIC 2-digit/FF 49 ind refers 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 regressions include bank controls, firm controls, bank-firm fixed effects and borrower 2-digit SIC fixed effects. Control variables are defined in Table B-2. Standard errors 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 in parentheses under the coefficient estimates. ***, **, and * denote significance at the 1%, 5% and 10% level, respectively.

48 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 2850 R2 0.8458 0.8862 0.8791 0.8489 0.8879 0.8810

49 Panel B: Syndicate Diversiﬁcation: With Participants Only Sic 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 2647 R2 0.7628 0.7896 0.79 0.7634 0.7903 0.7906

This table reports coefficient estimates from regressions relating the distance of newly formed syndicates to the banks’ existing exposure to syndicated lending with Lehman via revolving lines of credit. The sample period is from June 1, 2005 to December 31, 2009. In Panel A, Syndicate diversification is measured as the average distance between 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 the lead 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 outstanding at the time of loan origination that the bank co-syndicates with Lehman, where both the bank and Lehman are important lenders. Post is a dummy variable equal to 1 if the loan is issued post September 15, 2008, and 0 otherwise. SIC 1-digit/SIC 2-digit/FF 49 ind refers 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 regressions include bank controls, firm controls, bank-firm fixed effects and borrower 2-digit SIC fixed effects. Control variables are defined in Table B-2. Standard errors 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 in parentheses under the coefficient estimates. ***, **, and * denote significance at the 1%, 5% and 10% level, respectively.

50 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 3052 P seudoR2 0.8689 0.9012 0.8787

Panel B: Excluding Banks with Zero Exposure Role Lead Important Maturity Sic Sic FF Depth 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 2849 R2 0.8221 0.7992 0.7604 0.5832 0.7759 0.8259 0.8212

51 Panel C: Placebo Tests - Pre-existing Trend Role Lead Important Maturity Sic Sic FF Depth 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 6459 R2 0.8361 0.8250 0.4959 0.8340 0.8632 0.8586

52 Panel D: Placebo Tests - Bear Stearns Role Lead Important Maturity Sic Sic FF Depth 1-digit 2-digit 49 ind Post 0.005 0.003 0.002 -1.737 -0.006 0.001 -0.003 (0.08) (0.06) (0.06) (-0.74) (-0.27) (0.04) (-0.15) Exposure BEAR -2.139 -2.066 -0.073 129.477 0.927 0.127 -0.305 (-0.35) (-0.59) (-0.02) (0.9) (0.56) (0.09) (-0.24) Exposure BEAR -2.18 1.435 -3.615 -206.812** 2.713** 1.769 1.986* * Post (-0.44) (0.46) (-1.57) (-2.33) (2.19) (1.5) (1.77) Lead -0.009 -0.014 -0.016 (-0.43) (-0.65) (-0.93) Lead * Post 0.022 0.001 -0.001 (0.9) (0.02) (-0.05) Exposure BEAR 0.108 0.286 0.435 *Lead (0.07) (0.18) (0.31) Exposure BEAR -1.252 0.163 0.12 * Lead * Post (-0.71) (0.1) (0.07) N 3999 3999 3999 3967 3799 3799 3799 R2 0.8309 0.8101 0.7762 0.5848 0.8469 0.8735 0.8720

This table reports results from robustness tests. Panel A: Presents results from estimating (6) using conditional ordinal logit regression for Lead and Important Column (I), and from conditional logit regressions for Role Depth(columns (II) and (III)); Wald statistics are in parentheses under coefficient estimates. Panels B and C report robustness for the main tests regarding the importance of syndicate roles and syndicate diversification. Role Depth takes the value of 1, 2, and 3 if the bank acts as a participant, coagent, and lead arranger, respectively. Lead is a dummy 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 or a co-agent, and 0 otherwise. SIC 1-digit/SIC 2-digit/FF 49 ind refers 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. Panel B: Excluding zero-exposure banks from the sample; Panel C: the placebo period from March 1, 2004 to September 15, 2008, with the placebo event date being June 15, 2007; Panel D: the placebo event is the rescue of Bear Stearns on March 14, 2008, and all exposure are measured with respect to revolver cosyndication activities with Bear Stearns. All regressions include bank controls, 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 the bank co-syndicates with Lehman, where both the bank and Lehman are important lenders. Control variables are defined in Table B-2. Standard errors are clustered by bank and also by firm.

53 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 Dealscan loan facility ID 181374.

Table A-1: Distance Between Two Lenders This table describes how to calculate distance between Bank of America (BOA) and Wells Fargo (WF). SIC 1-digit denotes the borrower 1-digit SIC code. wBOA and wWF are the weights BOA and WF invest in an industry, respectively.

2 SIC 1-digit wBOA wWF (BOA − WF ) 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%

54 Appendix B Additional Tables

Table B-2: Deﬁnition of Variables

Variable Name Deﬁnition Banks (Source:Call Reports) Assets RCFD2170 ROA RIAD4340/RCFD2170 Core Deposits (RCON2702+RCON2215)/RCFD2170 Subordinated Debt RCFD3200/RCFD2170 Capital RCFD3210/RCFD2170 Loan Loss (RIAD4635-RIAD4605)/RCFD2170 Firms (Source: Compustat) Sales Saleq Leverage (Dlttq+dlcq)/atq Interest Coverage Oibdpq/xintq Net Working Capital (atq-lctq)/(Dlttq+dlcq) Tangibles (ppentq+invtq)/atq Firm Age Year of facility ﬁrst year in Compustat Loans (Source: Dealscan) Al-in-drawn Spread AllInDrawn Amount Facilityamt Maturity Maturity Investment Grade Market Segment

All income variables from Call Reports (RIAD4340, RIAD4635, and RIAD4605) are adjusted to be quarterly values from the reported year-to-date figures. Definition of item 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; RCON2215 is the amount of transaction accounts; RCFD3200 is the amount of subordinated notes and debentures; RCFD3210 is the total amount of equity capital; RIAD4635 is the amount of charge-offs on allowance for loan and lease losses. RIAD4605 is the amount of recoveries on allowance for loan and lease losses; Dlttq/dlcq is total value of long term 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 current liabilities; ppentq is the value of property, plant, and equipment; invtq is the total value of inventories.

55 Table B-3: Eﬀect on Depth of Role and Syndicate Diversiﬁcation: Term Loans

Role Lead Important SIC 1 SIC 2 FF 49 Depth (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 0.680 Lead * Post 0.71 (0.38) (0.62) N 603 603 603 573 573 573 R2 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 revolvers with Lehman on the significance of their role in a syndicate (columns (I)-(III)), and syndicate diversification (columns (IV)-(VI)). The sample consists of term loans originated from June 1, 2005 to December 31, 2009. The significance of a bank 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, and lead arranger, respectively. Lead is a dummy variable equal to 1 if the bank acts as the lead arranger of the loan syndicate, and 0 otherwise. Important is a dummy variable equal to 1 if the bank is either a lead arranger or a co-agent of the loan syndicate, and 0 otherwise. SIC 1-digit/SIC 2-digit/FF 49 ind refers to specifications where the dependent variable, distance, is measured based on 1-digit/2-digit borrower SIC codes/Fama and French’s 49 industry definition. Exposure LEH is the ratio of the dollar amount of revolvers outstanding at the time of loan origination that the bank co-syndicate with Lehman, where both the bank and Lehman are important lenders. Post is a 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, Interest Coverage, 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 (SIC codes 1520-1600) and financial companies (SIC codes 6000-6799) are excluded. T-statistics are reported in parentheses under the coefficient estimates. ***, **, and * denote significance at the 1%, 5% and 10% level, respectively.