did structured credit fuel the lbo...

THE JOURNAL OF FINANCE • VOL. LXVI, NO. 4 • AUGUST 2011

Did Structured Credit Fuel the LBO Boom?

ANIL SHIVDASANI and YIHUI WANG∗

ABSTRACT

The leveraged buyout (LBO) boom of 2004 to 2007 was fueled by growth in collat-eralized debt obligations (CDOs) and other forms of securitization. Banks active instructured credit underwriting lent more for LBOs, indicating that bank lending poli-cies linked LBO and CDO markets. LBO loans originated by large CDO underwriterswere associated with lower spreads, weaker covenants, and greater use of bank debtin deal financing. Loans financed through structured credit markets did not lead toworse LBOs, overpayment, or riskier deal structures. Securitization markets alteredbanks’ access to capital, affected their lending policies, and fueled the recent LBOboom.

FROM 2004 TO 2007, $535 billion of leveraged buyouts (LBOs) were completed,more than 10 times the $50 billion of volume over the previous 8 years from1996 to 2003 (see Figure 1), vastly eclipsing the $227 billion (in 2007 dollars)of volume during the prior 1986 to 1989 LBO boom. The LBO boom of 2004 to2007 collapsed as dramatically as it rose, with LBO volume dropping by 94%in the fourth quarter of 2007 from the prior-year level.

Most explanations for LBOs are based on trade-off theories of capital struc-ture whereby LBOs create value from interest tax shields, lower agency costs,and operational improvements. While variation in these benefits may havecontributed to the large changes in LBO volumes, the 2004 to 2007 LBO boomalso coincided with important developments in structured credit markets thatpotentially altered the supply of credit. In a Modigliani and Miller (1958) worldin which the supply of capital is perfectly elastic, leverage ratios and the inci-dence of highly leveraged transactions are determined only by the demand for

∗Shivdasani is at Kenan-Flagler Business School, University of North Carolina at Chapel Hill.Wang is at the Department of Finance, Chinese University of Hong Kong. We thank two anonymousreferees, an associate editor, Greg Brown, Sudipto Dasgupta, John Graham (Editor), Cam Harvey,Matthias Kahl, Wayne Landsman, Micah Officer, Paige Ouimet, Chris Parsons, Adam Reed, JayRitter, Steffen Sascha, Ed Van Wesep, and seminar participants at the 2010 American FinanceAssociation meeting in Atlanta, the 2010 China International Conference in Finance meetingin Beijing, the 2010 European Finance Association meeting in Frankfurt, American University,Australia National University Summer Finance Camp 2009, Chinese University of Hong Kong,City University of Hong Kong, Hong Kong Baptist University, Nanyang Technological University,Southern Methodist University, Texas Christian University, Tsinghua University, University ofFlorida, University of North Carolina, University of Oregon, and University of Wisconsin at Madi-son for helpful comments. We thank Chris Flanagan and Kedran Panageas of JP Morgan andMarc Auerbach and William Chew of S&P for helpful discussions. We thank Asset-Backed Alertfor sharing their data and updating the sample to include recent deals. An earlier version of thispaper was titled “Does Credit Supply Drive the LBO Market?.”

1291

1292 The Journal of Finance R©

0

30

60

90

120

150

180

0

20

40

60

80

100

120T

otal

CD

O I

ssua

nce

Vol

ume

($bn

)

Tot

al L

BO

Vol

ume

($bn

)

Total LBO Volume (Right) Total CDO Issuance (Left)

Figure 1. LBO volume and CDO issuance volume (1996–2009). Total LBO volume is theaggregate transaction value of deals announced in each quarter between 1996 and 2009 for a totalof 369 LBO deals from SDC satisfying the following criteria: The deal is completed by January 2010;the target is a U.S. company and publicly traded; transaction value is greater than $10 million; atleast 50% of common shares are acquired in the deal; and the acquirers own 100% of the sharesafter. Transaction value of LBO is the total value of consideration paid by the acquirer, excludingfees and expenses. The CDO issuance volume aggregates the total CDOs issued worldwide. TheCDO sample, from ABS Database, includes CDO issues rated by at least one major rating agencyand under the control of a trustee. LBO is leveraged buyout; CDO is collateralized debt obligation.

corporate debt. Under this framework, structural changes affecting the sup-ply of credit should not affect the volume and financing of LBO transactions.Evidence that such structural changes do in fact matter would thus indicatethat capital market frictions affecting the availability of credit are an impor-tant element missing from the Modigliani and Miller framework. Faulkenderand Petersen (2006), Sufi (2009), Lemmon and Roberts (2010), Leary (2009),and Becker and Ivashina (2010) explore the importance of such supply-sideeffects in capital structure decisions. We extend this work by studying whethersupply-side effects contributed to the large boom in the LBO market and howthey affected the quality of LBO deals.

A hallmark of the 2004 to 2007 period was the expansion of the marketfor collateralized debt obligations (CDOs). The growth was particularly sharpfor CDOs backed by corporate loans, also referred to as collateralized loanobligations (CLOs). Bank loans used to finance LBOs were often placed inthese CLO vehicles. Hence, the CDO channel potentially increased the supplyof credit by allowing a wide range of institutional investors such as hedge

Did Structured Credit Fuel the LBO Boom? 1293

funds, insurance companies, pension funds, and other institutional investorsthat invest in CDOs to indirectly invest in LBO loans. In addition, placing LBOloans into CLOs may have allowed banks to lend more because they becameless constrained by their balance sheets since they no longer needed to meetcapital requirements when the loans were sold to CLOs. To satisfy investordemand for CDOs, CDO issuers also needed collateral assets, providing banksthat underwrote CDOs an incentive to originate loans so as to fund LBOs thatcould be placed in CDOs. We argue that the growth in the CDO market providedan important source of funding to finance LBOs.

We document a high correlation between the growth of the LBO and CDOmarkets and show that CDO issuance exploded concurrently with the LBOboom of 2004 to 2007. Aggregate CDO issuance rose to $1.3 trillion over 2004to 2007, twice the total issuance volume over the previous 8 years (see Figure 1).CDO issuance then dropped sharply in the second half of 2007 when the LBOboom came to an end. This is consistent with the view that increased supplyof credit from the CDO market fueled LBO transactions. Alternatively, anincrease in the demand for LBO transactions could generate increased demandfor bank loans, leading banks to create CLO vehicles to distribute these loansto investors. In addition, it is possible that both the LBO and CDO marketsgrew simultaneously because of some omitted factors during the boom period.

To disentangle supply- and demand-side factors, we study securitizationssuch as structured product CDOs that are not directly affected by the demandfor LBOs. Structured product CDOs hold securitized assets1 but do not investin corporate loans or bonds generated by LBOs. We also study asset-backedsecurities (ABS), the collateral assets of which include home equity loans,credit card loans, auto loans, and student loans, none of which are driven by thedemand for LBOs. Though neither structured product CDOs nor ABS includeLBO loans as collateral, their issuance volumes are also highly correlated withLBO volume. Thus, the link between the LBO and CDO markets cannot beexplained by increased demand for LBO financing.

One of our primary results is that in a bank fixed effects model, we find apositive within-bank correlation between a bank’s LBO lending and its accessto CDO capital through underwriting capabilities. In the years when a bankunderwrote more structured CDOs (which exclude LBO loans or bonds), it alsooriginated a larger volume of loans to finance LBOs. Since this result holds ina bank fixed effects framework, it cannot be attributed to a bank-level time-invariant omitted factor. In addition, we do not find that a similar relation holdsfor investment-grade lending, indicating that the increased credit supply is ob-served only in the leveraged loan market, where structured credit vehicles wereactive. We also show that a bank’s experience in CDO underwriting prior to theLBO boom is strongly associated with its LBO lending during the boom years.This helps establish that a bank’s LBO lending during the boom is related to itsexpertise in securitization rather than some factor correlated with the boom.

1 Such assets include residential mortgage-backed securities, commercial mortgage-backed se-curities, collateralized mortgage obligations, asset-backed securities, and other securitized assets.


It is harder to rule out a link between the LBO and CDO markets thatarises from an omitted factor that varies over time. For example, it is possiblethat both markets grew due to a decrease in the perception of risk. While wecannot eliminate this possibility, we provide several pieces of evidence thatindicate a direct link between the two markets. We show that the institutionaltranches, which represent the allocation of LBO loans to institutional investorssuch as CLOs, were considerably higher during the LBO boom years than inthe preboom years. In addition, we find a positive association between theCDO underwriting activity of the lead banks and the allocation of the LBOloan to institutional investors. This suggests that CDOs were more importantinvestors in LBO loans during the boom years and that better access to CDOinvestors allowed banks to raise more capital from these investors to financetheir LBO loan commitments.

A bank’s underwriting in CDO markets was also strongly associated withthe cost of LBO loans for its borrowers. We show that banks with large CDOunderwriting businesses offered cheaper credit and looser covenant protectionand LBO transactions funded by these banks employed bank loans more ag-gressively. Thus, a bank’s CDO underwriting activity was linked to easier creditterms for LBO financing.

We examine the implications of the lower cost of credit for LBO financingassociated with the CDO channel. Lower financing costs should make morefirms desirable LBO candidates by making previously marginal LBO targetsmore attractive. It is also possible that the ability of banks to sell LBO loansto CDOs instead of holding the loans on their balance sheets reduced theirscreening incentives and led banks to finance lower quality transactions. Inthe context of the subprime mortgage market, Keys et al. (2010) argue thatsecuritization adversely affected the screening incentives of mortgage lenders.

To explore these issues, we examine whether banks’ access to structuredcredit markets was associated with changes in the quality of LBO transactions.Based on the characteristics of the LBOs, we do not find that bank fundingfrom structured credit was associated with weaker screening incentives in LBOlending. Target firms in CDO-driven deals generated more free cash flows, paidmore taxes, and were less risky. This finding is surprising if lower financingcosts allowed previously marginal LBO deals to become worthwhile duringthe boom period. One possible explanation is that CDO-driven LBOs weremuch larger firms—on average four times the size of non-CDO-led LBOs. Thus,a primary impact of the CDO channel was to facilitate much larger LBOsthan historically possible, potentially because it helped relax balance sheetconstraints that banks faced in financing large LBOs. This is consistent withdevelopments in structured credit markets allowing banks to finance largeLBOs rather than facilitating worse deals.

When we consider LBO pricing, we do not find evidence indicating over-payment in CDO-driven deals. In fact, the trajectory of credit ratings fol-lowing LBOs indicates better ex post performance of CDO-driven deals thannon-CDO-driven deals. We also do not find that CDO-driven deals during theboom period were financed with lower equity contributions compared to other


LBOs. Overall, these results suggest that banks retained their incentives toscreen borrowers when originating LBO loans even when they sold the loansto CLOs, possibly because of incentives to preserve their reputation as diligentunderwriters.

Our paper contributes to the emerging literature on how securitization af-fects the behavior of credit borrowers and lenders. Mian and Sufi (2009) findthat securitization contributed to the growth of the subprime mortgage market.Loutskina and Strahan (2009) show that mortgage securitization increased thewillingness of banks to increase their mortgage lending. Nini (2008) shows thatthe growth of institutional investors increased the supply of credit to firms withspeculative-grade ratings. We find a similar pattern in the context of LBO lend-ing by demonstrating an association between the growth of the securitizationmarket and LBOs. Though some have argued that deteriorating loan qualityassociated with securitization of subprime mortgages contributed to the recentfinancial crisis (Brunnermeier (2009)), we do not find evidence of lower qualityfor corporate lending in the setting of LBO transactions.

Our results suggest that financing from structured credit markets is notnecessarily associated with lower quality transactions. Kaplan and Stein (1993)show that junk bond investors contributed to overheating of the LBO marketin the late 1980s. Unlike high-yield bonds, loans sold to CDOs were originatedby banks, which may have resulted in stronger incentives for banks to screenborrower quality. Our evidence complements other studies of the recent LBOboom. Demiroglu and James (2010) and Ivashina and Kovner (2011) examinethe reputation of private equity groups and their relationship with lenders.Other studies exploring related issues include Acharya and Johnson (2010),Axelson et al. (2010), Boone and Mulherin (2009), Guo, Hotchkiss, and Song(2011), Kaplan and Stromberg (2009), Metrick and Yasuda (2010), and Officer,Ozbas, and Sensoy (2010).

The paper proceeds as follows. Section I describes the markets for CDOsand leveraged loans in more detail. Section II describes the sample and thedata. Section III presents our main results. Section IV examines the effects ofCDO lending on the structure, pricing, and quality of recent LBO transactions.Section V concludes.

I. Background: CDOs and Leveraged Loans

A. Collateralized Debt Obligations

CDOs are notes issued by a special purpose vehicle (also referred to as aCDO vehicle) that are collateralized by a portfolio of assets. Depending on theunderlying assets, CDOs are classified as CLOs, collateralized bond obliga-tions (CBOs), or structured product CDOs that invest in structured productsand notes. Unlike traditional pass-through securitization involving most ABSand mortgage-backed securities (MBS), CDO notes are usually divided intotranches with different seniority. The senior tranches have investment-graderatings, with the majority at AAA even though the collateral assets might be


rated much lower.2 Before the financial crisis of 2007 and 2008, almost 60% ofthe structured finance notes received an AAA rating (Fitch Ratings (2007)).

The higher rating of senior tranches allowed CDOs to be sold to a broaduniverse of investors such as banks, insurance companies, pension funds, andcertain asset managers that might not have otherwise invested in high-yield in-struments. Hence, the tranching process allowed speculative-grade assets un-derlying the CDOs to be financed using the much larger capacity of investment-grade markets. CLOs, which primarily held leveraged loans that could be usedto finance LBOs, were a very important part of the CDO market and attractedmany of the same investors that invested in CDOs.3 For example, about halfof the CDOs that insurance companies purchased were CLOs, according toCiti Credit Research (2007). Thus, CLOs helped bring new investors to theleveraged loan market, investors who had historically participated in securiti-zation markets primarily through traditional channels such as ABS and MBS.4

Figure 2 depicts how CDO markets were used in the financing of LBO trans-actions.

A potentially important driver behind the growth of CDOs was the changein banks’ incentives regarding tranching in securitization resulting from theBasel II Accord, published in June 2004.5 The CDO market allowed banks tosell risky assets with high capital requirements (such as leveraged loans) toCDO investors, while at the same time investing in the senior CDO tranches,which required less capital.6 The incentive of banks to hold senior trancheswas a critical element in the expansion of the CDO market because most ofthe other major CDO investors (e.g., hedge funds) were more interested inthe higher risk junior tranches. In addition, Coval, Jurek, and Stafford (2009a)and Brennan, Hein, and Poon (2009) document pricing errors for CDO tranches,

2 Coval, Jureck, and Stafford (2009b) discuss how high ratings were achieved given the muchriskier collateral assets in CDOs. Benmelech and Dlugosz (2009) and Griffin and Tang (2010) arguethat CDO notes were overrated before the crisis.

3 According to the Securities Industry and Financial Markets Association (SIFMA), all of theCLOs issued between 2005 and 2007 were backed by leveraged loans.

4 Though systematic data on investor participation in various securitization markets are un-available, discussions with industry professionals suggest a high overlap between investors inCDO, CLO, and ABS markets. Such overlap also exists between the CLO and MBS markets,though some credit and corporate funds tend to invest in CLOs but not in mortgage and realestate assets. On the other hand, some dedicated mortgage funds tend to invest in MBS and realestate–related equities but do not invest in CLOs. In subsequent tests, we focus more on the ABSmarkets than the MBS markets due to their higher potential overlap in investor base with CLOs.However, our results are qualitatively similar if we use variables based on MBS markets.

5 Though Basel II had not been uniformly implemented in the United States during our sampleperiod, JP Morgan (2007) argues that the effects of its implementation were clearly anticipatedin market prices. During this period, most large “internationally active” banks moved toward arisk-based capital approach both for their internal capital management and for communicatingtheir capital positions to analysts and investors.

6 Under Basel II, the weight on some AAA investments in securitized assets is only 7%. With an8% capital requirement, an investment in an AAA security requires banks to put up only 0.56%(7% x 8%) capital for the invested asset, an implicit leverage of 178 (1/0.56%). JP Morgan (2007,p. 118) notes that “the Basel II capital framework for securitization was a key driver of the excessiveleverage applied to ABS securities via structured finance CDOs.”


Figure 2. Illustration of LBO financing and the structure of securitization markets.LBO is leveraged buyout; CLO is collateralized loan obligation; CDO is collateralized debt obliga-tion; ABS is asset-backed securities; MBS is mortgaged-backed securities; CBO is collateralizedbond obligation; SIV is structured investment vehicle.

offering another potential explanation for the rapid growth in the CDO marketduring this period.

B. Leveraged Loans

Leveraged loans are bank loans issued to borrowers with speculative-graderatings, most of which are syndicated. In the syndicated loan market, investorscan be classified into pro rata investors and institutional investors. Accordingto S&P (2006), commercial banks and finance companies typically invest inrevolvers and term loan A (or amortizing term loan) tranches, also referredto as pro rata tranches. CLO vehicles, prime funds, hedge funds, and insur-ance companies typically comprise the bulk of institutional investors. Theseinvestors invest in the term loan B, C, and D tranches, also referred to as insti-tutional tranches. Term loan A tranches have an amortization structure whileinstitutional term loan tranches repay toward the end of the term. Hence, in-stitutional tranches tend to be riskier and are priced at higher spreads thanpro rata tranches.7 Since CLO vehicles invest in institutional tranches, our

7 After 2001, spreads on an increasing number of institutional tranches were priced closer topro rata tranches, and in some cases were even lower according to S&P (2006), which attributesthis pattern to the higher demand for leveraged loans from institutional investors.


0

10

20

30

40

50

60

70

0

20

40

60

80

100

120

140

160

$Bil

lion

$Bil

lion

Pro Rata Volume (Left) Institutional Volume (Left) CLO Issuance (Right)

Figure 3. Leveraged loan allocation and CLO issuance volume. The figure shows quar-terly volume of newly issued leveraged loans broken down by pro rata and institutional marketallocation, and the issuance volume of collateralized loan obligations (CLOs). The leveraged loanvolume is from S&P’s LCD and CLO volume is calculated using the ABS Database.

tests focus on the pricing of these tranches, which is referred to as the insti-tutional spread and measured as the spread on the term loan B tranche, themost common institutional tranche.

CLO vehicles were one of the most important investors in the institutionalmarket, representing 60% of the institutional investor volume in primary mar-kets of leveraged loans by 2006 (S&P (2006)). Thus, during the LBO boom, asubstantial part of the financing of leveraged loans came from CLO vehicles.Consistent with the active participation of CLOs in institutional tranches,Figure 3 displays a close linkage between CLO issuance volumes and the allo-cation of leveraged loans to institutional tranches during the LBO boom. Thefraction of institutional tranches in leveraged loans was 70% to 80% duringthe peak of the LBO boom but dropped to 40% by the end of 2008 as CLO is-suance declined sharply at the onset of the financial crisis. Figure 3 also showsthat CLO issuance volumes were not closely linked to the pro rata allocations,consistent with the observation that CLOs rarely invest in pro rata tranches.

II. Data and Sample Description

A. The LBO Sample

Our sample consists of 345 LBOs from SDC Platinum satisfying the followingcriteria: The transaction was announced between 1996 and the second quarterof 2008 and completed by July 2008; the target is a publicly traded U.S. com-pany; the transaction value is at least $10 million; at least 50% of the common


shares were acquired; and the buyers owned 100% of the shares upon comple-tion. Our minimum deal value of $10 million is lower than that in some otherstudies, such as Kaplan (1989b) and Guo, Hotchkiss, and Song (2011) and ischosen to avoid biasing against earlier time periods when smaller deals weremore common.

Figure 1 shows that LBO volume rose in the second quarter of 2004, when$5.6 billion of LBOs were announced. The pace of LBOs picked up dramatically,reaching $20 billion in the last quarter of 2005. At $255 billion of announceddeal volume, 2006 was a record year for LBOs. With the onset of the creditturmoil, LBO volume dropped to $32 billion in the third quarter of 2007 and tounder $5 billion in the first quarter of 2008. The number of deals also increasedduring 2004 and 2007 but less dramatically because much larger LBO deal sizeswere a hallmark of the boom period.

We match the LBO targets with LBO loans from Loan Pricing Corporation’s(LPC) DealScan and collect data on tranche types and amounts, lead arrangers,spreads, maturity, and other terms. We also manually collect data from proxyfilings as well as from schedules 14A, TO-T, S-4, and 13E3 when these filingsare available in Edgar. We check these filings for two reasons. First, someLBO loans, particularly for smaller deals, are not in DealScan. For these loans,we collect information on the loan contract whenever available. Second, sinceDealScan does not identify which loans are asset-backed, we use information inproxy filings to remove tranches that can be identified as asset-backed financ-ing.8 Asset-backed loans are excluded to avoid potential bias resulting fromsecuritized financing other than CDOs.9

We are able to collect data on lead arrangers and borrowing amounts for275 loans financing 241 deals (70% of the sample). We lose some observationsbecause several small deals were funded by cash, mezzanine finance, or equityand did not arrange bank loans to finance the deals. Other deals were notconditioned on the availability of financing and thus lack disclosure on loans.In addition, we exclude a few asset-backed loans, and, for some firms, filingsare unavailable on Edgar.

The volume of bank loans in each quarter is shown in the Internet Ap-pendix.10 The pattern mirrors the volume of LBO transactions, with heavyvolume between the second quarter of 2004 and the first half of 2007. To-tal bank borrowing peaked in the second quarter of 2007, while LBO volumepeaked at the end of 2006.11 On average, bank loans comprise 53% of LBO

8 For example, commercial mortgage-backed financings were used in the buyouts of UICI, LaQuinta, and Station Casinos, but these are included in DealScan and flagged as “other loan”tranches, which are not distinguishable from other loans.

9 When DealScan and proxy filings differ, we retain the DealScan information since termsspecified in proxy filings are sometimes adjusted after the filing and hence may not be the finalterms.

10 The Internet Appendix is available on The Journal of Finance website at http://www.afajof.org/supplements.asp.

11 The reason for LBO volume and LBO loan volume to peak at different times is because theloan sample excludes commercial mortgage financing for a few large deals announced in the fourth


volume, indicating that they represented the most important form of financingfor LBOs during our sample period.

We also identify the financing structure for 235 (68.1%) of the sample deals.We collect information on nonbank financing from proxy filings, supplementedwith SDC’s data on high-yield bond issuance. We record the total fundingneeded to complete the deal and the amounts of total equity financing frompost- and pre-LBO investors, asset-backed financing, high-yield bonds/notes,and mezzanine finance when available.12

B. Data on CDO and Other Securitized Issues

Data on CDOs and other securitized issues are from Asset-Backed Alert’sABS Database, which provides the initial terms of all rated issues of ABS,MBS, and CDOs worldwide.13 The first full year of CDO coverage begins in1996 and hence we use this as the starting point for our sample.

From 1996 through the second quarter of 2008, 4,542 CDOs with a total valueof $1.9 trillion were issued. Figure 1 shows the virtually identical trajectoriesin the LBO and CDO markets, with both markets rising and falling at the sametime. The correlation between the natural log of quarterly LBO volumes andof CDO issuance volumes is quite strong at 0.68.

Table I summarizes the CDO market. We have detailed data on the under-lying collateral for CDOs after 2001. Hence, we compare CDO issuance during2001 and 2003 to the post-2004 period. The CDO market witnessed explosivegrowth during this period, rising from aggregate issuance volume of $255 bil-lion to over $1.3 trillion. The growth was particularly pronounced for structuredproduct CDOs, whose issuance volume rose from $63.9 billion over the 2001 to2003 period to $556 billion in the post-2004 period. CLOs also experienced verystrong growth, with issuance volume rising from $88.5 billion to $506.7 billion.

CLOs are either balance sheet or arbitrage CLOs, depending on the purposeof issuance. A balance sheet CLO is typically originated by a bank that seeks toremove its existing loans (or their risk) from its balance sheet. Since the loansin a balance sheet CLO already exist on a bank’s loan book, these CLOs do notgenerate much additional demand for leveraged loans. In contrast, arbitrage

quarter of 2006, including the $41 billion LBO of Equity Office Properties and the $28 billionbuyout of Harrah’s Entertainment.

12 Total funding needs include cash needed to pay off equity, option, and warrant holders; retireexisting debt; and pay for fees and expenses related to the deal. The amount of bank financing isfrom the bank loan data set. When a deal cannot be matched with a bank loan in the loan sample,and the proxy filings indicate no bank financing, bank financing is assumed to be zero. Bridge loansor other bridge financing are recorded as high-yield bond/note and mezzanine finance. The amountof high-yield bond issues is supplemented by bond issuance data in SDC for 40 deals for whichthe hand-collected amounts are different from proceeds recorded in SDC. Equity contributions andasset-backed financing are from proxy filings.

13 To be included in the ABS Database, an issue must be rated by at least one major rating agency,under control of a trustee, and collateralized on some assets. The data set excludes commercialmortgage-backed issues, agency-sponsored MBS, issues by municipalities, tax-exempt issues, andasset-backed commercial paper issues.


Table ISummary of CDO Issuance

This table presents the volume of CDO issuance based on collateral assets over the 2001 to 2003 and2004 to June 2008 subperiods. The data on CDO issues are from the ABS Database, which covers allrated securitized issues and provides information on CDO collateral since 2001. Structured productCDOs are CDOs backed by a portfolio of structured products, including mortgage-backed securities,asset-backed securities, and other securitized assets. Arbitrage CLOs are CLOs created in anattempt to capture a mismatch between the yield of collateral assets and the financing cost of CLOtranches. Balance Sheet CLOs are CLOs issued for the purpose of removing existing assets (or theirrisk) from the balance sheet of the seller. High-Yield CBOs are CDOs collateralized on high-yieldbonds and Investment-Grade CBOs are CDOs collateralized on investment-grade corporate bonds.CMBS/Real Estate refers to CDOs backed by commercial mortgage-backed securities and realestate. “Others” include small business loans, hedge fund shares, preferred stock, trust-preferredsecurities, etc. as collateral assets.

2001–2003 2004–June 2008

Issuance IssuanceNumber of Volume % of Number Volume % of

Issues ($bn) Total of Issues ($bn) Total

Structured product CDOs 252 63.9 29.6 1,243 556.0 43.8Corporate loans (CLOs)Arbitrage CLOs 153 46.9 18.0 891 431.1 31.4Balance sheet CLOs 90 41.6 10.6 76 75.6 2.7

Corporate bonds (CBOs)High-yield CBOs 72 24.0 8.5 37 12.6 1.3Investment-grade CBOs 128 24.8 15.0 213 30.3 7.5

CMBS/Real estate 29 16.0 3.4 179 89.4 6.3Others 54 19.8 6.3 197 126.5 6.9Unknown 74 17.8 8.7 19 1.0 0.7Total CDO issuance 852 254.9 100 2,855 1,322.4 100

CLOs are usually originated by asset managers or hedge funds who do not havethe loans and need to purchase them in the marketplace. Thus, arbitrage CLOissuance creates incremental demand for leveraged loans that may in turnencourage the supply of additional credit to leveraged borrowers.14 Table Ishows that the growth of CLOs was driven primarily by arbitrage CLOs, whichgrew almost 10-fold from $46.9 billion during the 2001 to 2003 period to $431.1billion in the post-2004 period. In contrast, balance sheet CLO issuance rosemore modestly from $41.6 billion to $75.6 billion. Thus, the growth of theCLO market occurred primarily in the segment of the market that had higherpotential for creating incremental supply of credit. During this time, the marketfor high-yield CBOs actually contracted from $24 billion to $12.6 billion in the

14 It is worth noting that demand for collateral assets arises not just at the creation of the CDObut can extend beyond the initial CDO issuance. According to Barclays (2002), underlying assetscan be purchased in multiple stages, starting before the issue and continuing up to 6-monthsafter. In addition, once a CDO is created, cash flows from principal repayments resulting fromamortization, maturity, prepayment, or asset sales are usually reinvested, generating continualdemand for collateral, including LBO loans.


post-2004 period, indicating that CBOs were not an important channel throughwhich structured credit helped finance LBOs.

In the Internet Appendix, we summarize the size of the entire structuredcredit market. The total volume of securitized issues rose from $4.76 trillionover 1996 to 2003 to $8.07 trillion over the next four and half years. The bulk ofthe growth arises from expansion of the CDO market and of structured creditlinked to U.S. and non-U.S. residential real estate. Total CDO issuance morethan doubled during this period, and issuance of U.S. MBS and home equityloan ABS along with non-U.S. residential MBS/ABS also doubled.

III. Results

A. Correlation between LBO and CDO Markets

LBO and CDO volumes can be positively correlated due to factors affect-ing either the demand or the supply of LBO loans. Increased supply of capitalthrough structured credit may drive LBO activities. However, a higher demandto complete LBOs also results in new bank loans and high-yield bonds that maybe sold to CLOs and CBOs. As a first step in understanding the link betweenthese two markets, we examine the correlation of institutional spreads on lever-aged loans with LBO volumes and LBO loan volumes. While an outward shiftin LBO loan demand is expected to increase loan spreads, an outward shift inthe supply of credit should lower spreads. Hence, evidence of a negative corre-lation between changes in institutional spreads and changes in LBO volumeand LBO loan volume would be consistent with a shift in credit supply fromthe institutional market. For comparison, we also consider spreads on pro ratatranches. Since CDOs do not participate in pro rata tranches, examining prorata spreads serves as a useful control to isolate the effect of CDOs on thepricing of bank credit.

We find that quarterly changes in spreads on institutional tranches ratedBB and B display correlation of −0.32 and −0.39 with changes in loan volumesand of −0.28 and −0.30 with changes in LBO volumes. However, we do not finda meaningful correlation between changes in LBO volumes or loan volumeswith changes in pro rata spreads. While this does not mean that LBO demanddid not change during this period, it does indicate an outward shift in creditsupply in the institutional market, where CDOs were active investors duringthe boom.15

If credit expansion from the CDO market allowed banks to make more LBOloans, LBO lending should be correlated with all types of CDOs, not just theCLOs that hold leveraged loans or the CBOs that hold high-yield bonds. Ac-cordingly, we consider the volume of all CDOs excluding CLOs and CBOs. Wehenceforth refer to this group as structured CDOs since structured product

15 Arguably, LBO demand increased substantially before the third quarter of 2007. We also findevidence of a negative correlation between LBO loan volumes and institutional spreads in theperiod before the third quarter of 2007, suggesting that shifts in the supply of credit were notoffset by shifts in LBO demand during this period.


CDOs comprise the vast majority of these instruments. Structured CDO vol-ume is also highly correlated with LBO volume with a correlation of the naturallog amounts of 0.64. Thus, the link between LBO and CDO markets is not amechanical result of LBO loans and high-yield bonds being placed in CLO andCBO vehicles.

We also consider the more traditional securitization market of ABS. Thecollateral assets in ABS include home equity, auto, credit card, and studentloans. Since LBOs do not create these types of consumer loans, ABS issuanceis not affected by the demand for LBOs. Yet as an ancestor of CDOs, the ABSmarket shares a similar investor base to that of the CDO market. We find thatABS issuance volumes are also highly correlated with LBO volume, with acorrelation of the log amounts of 0.52.

B. Multivariate Evidence on LBO and CDO Markets

We next examine the link between the CDO and LBO markets in a multi-variate framework where we control for factors affecting the demand for LBOloans, macroeconomic conditions, and other potential sources of credit supply.The control variables include GDP growth, the high-yield spread calculated asthe Bank of America–Merrill Lynch High-Yield Index over 6-month LIBOR,the difference between prime and fed funds rates, and the equity market riskpremium calculated as the equity market return over the 90-day Treasury billreturn.

Table II shows three specifications linking overall LBO volume to the size ofthe CDO market, structured CDO market, and ABS market. Model (1) confirmsthe positive correlation between LBO volumes and CDO issuance volumes. Thecoefficient of 1.73 implies a partial correlation of 0.62 between the log of LBOand CDO volumes. Regression (2) shows that LBO and structured CDO volumesare also positively related, indicating that the relation between LBO and CDOvolumes is not driven by LBO loans being placed into CDO vehicles. The partialcorrelation between the log of LBO volumes and the log of Structured CDO is0.51. Regression (3) shows that LBO volumes are also positively linked to ABSissuance volumes. As in Axelson et al. (2010), this specification shows thatLBO volumes are negatively related to the high-yield spread, suggesting thatfinancing costs in high-yield bond markets affect LBO activity.

We estimate corresponding regressions for aggregate quarterly LBO loanvolumes and obtain similar results. Models (4) to (6) show that LBO loanvolumes are also positively related to issuance volumes in CDO, structuredCDO, and ABS markets. Among the control variables, GDP Growth tends tohave a positive effect on LBO volumes and loan volumes. The high-yield spreadhas a negative effect on LBO loan volumes, consistent with Axelson et al. (2010).

The last four columns link spreads on institutional tranches of leveragedloans to issuance volumes in the CDO market. A negative correlation betweeninstitutional spreads and CDO issuance volumes would be consistent with anexpansion in credit supply from the CDO market. Model (7) displays such anegative correlation. Point estimates indicate that a one-standard-deviation

1304 The Journal of Finance R©T

able

IIR

egre

ssio

ns

ofL

BO

Vol

um

eon

Siz

eof

CD

Oan

dS

ecu

riti

zati

onM

ark

ets

Th

ista

ble

pres

ents

the

regr

essi

ons

ofL

BO

volu

mes

,lo

anvo

lum

es,

and

spre

ads

onth

esi

zeof

CD

Oan

dse

curi

tiza

tion

mar

kets

.T

he

LB

Ovo

lum

ein

mod

els

(1)

to(3

)is

the

aggr

egat

equ

arte

rly

volu

me

ofal

lL

BO

deal

sfr

omS

DC

sati

sfyi

ng

the

foll

owin

gcr

iter

ia:

the

deal

isan

nou

nce

dbe

twee

n19

96an

dth

ese

con

dqu

arte

rof

2008

and

com

plet

edby

July

2008

;th

eta

rget

isa

U.S

.co

mpa

ny

and

ispu

blic

lytr

aded

;tr

ansa

ctio

nva

lue

isgr

eate

rth

an$1

0m

illi

on;a

tle

ast

50%

ofco

mm

onsh

ares

are

acqu

ired

inth

ede

al;a

nd

the

acqu

irer

sow

n10

0%of

the

shar

esaf

ter.

LB

OL

oan

Vol

um

eis

the

aggr

egat

eof

lon

g-te

rmba

nk

loan

borr

owin

gam

oun

tfo

rth

e24

1sa

mpl

ede

als

wh

ere

info

rmat

ion

onth

eam

oun

tsof

ban

klo

ans

can

beid

enti

fied

.S

prea

dsof

Lev

erag

edL

oan

sis

the

quar

terl

ysp

read

ofth

ein

stit

uti

onal

tran

ches

inle

vera

ged

loan

sra

ted

BB

orB

,pr

ovid

edby

LP

Cba

sed

onn

ewis

sues

ofle

vera

ged

loan

s.L

og(C

DO

)is

log

ofth

equ

arte

rly

issu

ance

volu

me

ofal

lty

pes

ofC

DO

s;L

og(S

tru

ctu

red

CD

O)

excl

ude

sC

DO

sba

cked

onco

rpor

ate

loan

s(C

LO

s)or

bon

ds(C

BO

s);

Log

(AB

S)

incl

ude

sis

suan

cevo

lum

esof

AB

S.

Pri

me

Ove

rF

edF

un

dR

ate

isca

lcu

late

das

the

prim

era

tem

inu

sth

efe

dera

lfu

nds

rate

.Hig

h-Y

ield

Spr

ead

isth

esp

read

ofth

eB

ank

ofA

mer

ica–

Mer

rill

Lyn

chH

igh

-Yie

ldIn

dex

over

the

6-m

onth

LIB

OR

rate

.G

DP

Gro

wth

isth

ese

ason

ally

adju

sted

perc

enta

gech

ange

inG

DP

over

aye

area

rlie

r.R

isk

Pre

miu

mis

calc

ula

ted

asth

ean

nu

aliz

edva

lue-

wei

ghte

dm

arke

tre

turn

min

us

retu

rnon

90-d

aybi

lls.

Th

efi

rst

row

for

each

inde

pen

den

tva

riab

lere

port

sth

ees

tim

ated

coef

fici

ent.

Th

ese

con

dro

wre

port

sab

solu

teva

lues

ofth

et-

stat

isti

cu

sin

gN

ewey

–Wes

tst

anda

rder

rors

adju

stin

gfo

rse

rial

corr

elat

ion

.∗∗∗

,∗∗ ,

and

∗in

dica

tes

stat

isti

cal

sign

ifica

nce

atth

e1%

,5%

,an

d10

%le

vel,

resp

ecti

vely

.

Log

(In

stit

uti

onal

Spr

eads

ofL

ever

aged

Loa

ns)

Log

(LB

OV

olu

me)

Log

(LB

OL

oan

Vol

um

e)B

BR

ated

BR

ated

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

Log

(CD

O)

1.73

1.58

−0.2

0−0

.14

5.16

∗∗∗

4.79

∗∗∗

4.82

∗∗∗

3.35

∗∗∗

Log

(Str

uct

ure

dC

DO

)1.

501.

41−0

.20

−0.1

35.

64∗∗

∗5.

02∗∗

∗3.

26∗∗

∗2.

74∗∗

∗

Log

(AB

S)

2.59

2.40

4.67

∗∗∗

4.24

∗∗∗

Pri

me

Ove

rF

edF

un

ds

Rat

e4.

593.

6613

.86.

255.

2914

.72.

402.

471.

681.

740.

870.

672.

28∗∗

0.83

0.77

2.34

∗∗2.

85∗∗

∗3.

22∗∗

∗2.

22∗∗

2.47

∗∗

Hig

h-Y

ield

Spr

ead

−0.2

1−0

.23

−0.4

9−0

.25

−0.2

6−0

.51

0.01

0.01

0.02

0.02

1.60

1.60

3.90

∗∗∗

1.79

∗1.

76∗

3.71

∗∗∗

0.67

0.34

1.42

1.17

GD

PG

row

th0.

320.

10−0

.08

0.57

0.38

0.21

−0.0

4−0

.02

−0.0

4−0

.02

1.73

∗0.

500.

391.

91∗

1.32

0.62

1.48

0.75

1.26

0.80

Ris

kP

rem

ium

−0.7

2−1

.04

−0.3

4−1

.52

−1.8

3−1

.17

0.01

0.00

−0.0

10.

001.

041.

470.

511.

271.

501.

090.

110.

670.

150.

32C

onst

ant

−24.

4−1

7.1

−60.

0−2

9.7

−23.

2−6

2.9

0.59

0.14

2.21

1.86

1.29

0.96

2.95

∗∗∗

1.19

1.05

2.89

∗∗∗

0.23

0.06

0.91

0.85

Obs

erva

tion

s49

4949

4949

4941

4141

41A

dj.R

20.

520.

420.

430.

480.

420.

430.

590.

600.

610.

61


increase in the log of CDO issuance volume is associated with a 33 basis pointdecrease in the institutional spread of the BB-rated tranches. Model (8) showsthat this result holds if we use the volume of structured CDO underwriting.We also find a similar relation using ABS volume. Models (9) and (10) confirmthat the relation between CDO volume and leveraged loan spreads holds usingspreads of B-rated institutional loans.16 Interestingly, in all models, institu-tional spreads on leveraged loans are not significantly related to the high-yieldspread. This suggests that financing costs in the leveraged loan and high-yieldbond markets are determined by different factors and that the effect of CDO is-suance volume on institutional spreads on leveraged loans cannot be explainedby contemporaneous changes in high-yield spreads.

Overall, the aggregate-level evidence indicates a clear link between the CDOand LBO markets that cannot be explained by LBO loan demand fueling CDOissuance. However, it is possible that the link between the two markets is theoutcome of an omitted factor. We next turn to bank-level and loan-level analysesto address the possibility that omitted factors may be responsible for the linkbetween the LBO and CDO markets.

C. Results from Bank-Level Analysis

We test whether a bank’s activities in the CDO markets affected its lendingbehavior. If an increased supply of credit through the CDO market made abank a more aggressive LBO lender, we expect that a bank with substantialCDO underwriting activities would originate more LBO loans. The presence ofan established distribution channel to structured credit investors should allowa bank with CDO underwriting capabilities to distribute its LBO loans to CLOvehicles. To test the effect of a bank’s CDO underwriting on its LBO lending, weconstruct a panel data set to estimate the following bank fixed effects model:

LBOLendingit =K∑

i=1

αi + β × CDOit + γ ′ × Xit + δ′ × Zt + εit, (1)

where LBOLendingit is the total volume of LBO loans that bank i arranges inyear t, αi is the bank fixed effect, Xit and Zt are proxies for bank characteristicsand macroeconomic conditions, respectively. The term CDOit is the volume ofCDOs underwritten by bank i in year t. We also use our two non-LBO-relatedmeasures of the structured credit market to proxy for CDOit. If the supply ofcapital through structured credit led a bank to be a more aggressive lender, weexpect β > 0.

16 As an additional test of the credit supply view, we examine the difference between institutionaland pro rata spreads. The supply of credit from CLOs is expected to lower institutional spreads butshould have little impact on pro rata spreads. Hence, the difference in institutional and pro rataspreads should also be negatively correlated with CDO volumes. Results presented in the InternetAppendix display such a pattern. For both BB- and B-rated loans, we find that the differencebetween institutional and pro rata spreads is negatively related to CDO and structured CDOunderwriting volumes.


Using a fixed effects approach allows us to control for bank-level fixed omit-ted variables. This helps address the possibility that an unobserved factorsimultaneously drove both a bank’s decision to originate LBO loans as well asthe growth in its CDO underwriting. Bank-level time-invariant omitted fac-tors cannot explain a correlation between a bank’s LBO lending and it’s CDOunderwriting in this setting.

To construct the bank-level panel data set on banks’ annual LBO lendingvolumes and their CDO underwriting activity, we start with all the lead banksin the LBO loan sample. For each bank, we calculate the total volume of LBOloans it originates and the total volume of CDOs it underwrites in each year.For sole-led loans, we assign full lending credit to the lead bank.17 For co-led loans, we divide the loan amount equally among all the lead banks. Asin Sufi (2007), the banks are consolidated with the parent holding companyin the loan allocation algorithm. When banks merge, loan allocations of theacquired bank are aggregated with those of the acquirer as of the effective dateof the merger. Allocation of CDO underwriting volumes across lead banks isperformed similarly. Only 8.3% of the CDO issues in our sample had multipleunderwriters.

In the Internet Appendix, we show the structured credit underwriting activ-ities for the top LBO lenders. In the post-2004 period, the top 10 LBO lendersoriginated $225 billion in LBO loans, with a 94% market share in LBO lending.During this time, these top LBO lenders were all major CDO underwriters, andeach ranked among the top 13 CDO underwriters with a collective 55% marketshare in CDO underwriting. However, this tight correspondence between LBOlending and CDO underwriting activity did not exist pre-2004. Before 2004,the top 10 LBO lenders had a 79% market share in LBO loan origination butonly a 25.5% market share in CDO underwriting. Four of the top LBO lendershad no CDO underwriting activity. Similar patterns hold using the ranking ofthe top LBO lenders in structured CDO and ABS markets. The contrast in theoverlap between LBO lending and structured credit underwriting before andafter 2004 suggests these two activities were much more closely linked duringthe LBO boom than was historically the case.

For each bank-year, we calculate the bank’s underwriting activity in struc-tured credit markets using variables that correspond to our aggregate mea-sures of market size: Bank CDO is the aggregate volume of CDOs under-written by a bank in a given year, Bank Structured CDO measures a bank’snon-CLO and non-CBO underwriting, and Bank ABS represents the volume ofABS underwritten by the bank in a given year. The models include Bank Size(measured as log of total bank assets), Capital Ratio (the ratio of total equityto total assets), Deposits (total customer deposits over total assets), and FedFunds Rate as controls.

17 As described by Sufi (2007), lead banks are primary negotiators of the loans. They are re-sponsible for collecting information and negotiating loan terms, they typically hold a larger shareof the loan, and they charge up-front fees. Other syndicate participants maintain an arm’s-lengthrelationship with the borrower by interacting with lead banks. Our allocation of loan amounts isalso consistent with how league tables for banking transactions are computed.


One concern is that a number of banks merged during our sample periodand in some cases might have gained CDO underwriting capabilities throughacquisitions. To account for bank mergers in the panel data regressions, wemerge the lending and underwriting activity as well as balance sheet data forthe acquired bank with that of the acquirer throughout the sample period. Withthis process, we treat an acquired and acquiring bank as a single bank in theregressions. Our LBO loan panel data contain 36 banks and 368 bank-yearobservations.

Results from the bank fixed effects models shown in Table III indicate that abank’s activities in CDO underwriting and its LBO lending are related. Model(1) shows that the volume of LBO lending in a given year is positively re-lated to the bank’s CDO underwriting volume in that year at the 10% level ofsignificance. Model (2) shows that this relation also holds if we only considerunderwriting volumes of structured CDOs. In model (3), we use the volume ofa bank’s underwriting in the broader ABS market and find that it is also posi-tively related to its LBO lending in a given year. The coefficient on underwritingactivity is significant at the 5% level or better in both specifications.

We also consider bank lending for investment-grade loans and collect thesedata from Dealscan. If supply from the structured credit market drove LBOlending, we expect that investment-grade lending should be relatively unaf-fected by a bank’s underwriting in structured credit markets because CLOsprimarily purchase leveraged loans. This is exactly what we observe. Columns(4) to (6) present bank fixed effects models and show that a bank’s originationof investment-grade loans is not systematically related to its CDO, structuredCDO, or ABS underwriting.

We also estimate cross-sectional regressions of LBO lending during the 2004to 2008 period using CDO underwriting volumes in the preboom period of1996 to 2003. Model (7) shows that a bank’s LBO lending during the boom ispositively related to its CDO underwriting in the preboom years. Models (8)and (9) display a similar pattern using the volume of a bank’s structured CDOunderwriting and ABS underwriting volumes.

Finally, we estimate similar cross-sectional regressions for investment-gradelending as a control group that should be relatively unaffected by structuredcredit markets. Columns (10) and (11) show that investment-grade lending inthe LBO boom years was unrelated to a bank’s activities in CDO and structuredCDO markets prior to the LBO boom. We find some evidence in model (12) thatinvestment-grade lending is linked to pre-boom ABS underwriting at the 10%level of significance, but the coefficient estimate is about one-third the size forLBO lending.

These results point to a direct connection between the LBO lending policyof a bank and its underwriting activities in the CDO and structured creditmarkets. Since this result holds in a bank fixed effects framework, it cannotbe explained by the possibility of bank-level omitted variables driving both abank’s lending policies and its CDO underwriting. A potential explanation forthis result is that the supply of capital from structured credit investors allowedbanks to finance their leveraged lending. The result cannot be explained by a


Tab

leII

IB

ank

-Lev

elR

egre

ssio

ns

ofL

BO

Len

din

gon

CD

OU

nd

erw

riti

ng

Act

ivit

yT

his

tabl

epr

esen

tsre

gres

sion

sof

ban

ks’l

endi

ng

tofu

nd

LB

Os

and

thei

rin

vest

men

t-gr

ade

len

din

gon

thei

rC

DO

un

derw

riti

ng

volu

mes

for

ban

ksth

atse

rved

asle

adar

ran

gers

inL

BO

loan

s.T

he

ban

kpa

nel

cove

rs19

96to

2007

.We

mer

geta

rget

ban

ksin

toac

quir

ing

ban

ksth

rou

ghou

tth

esa

mpl

epe

riod

.Th

ispa

nel

ofba

nks

isu

sed

toru

nth

efo

llow

ing

fixe

def

fect

mod

elfo

rba

nks

’LB

Ole

ndi

ng

and

inve

stm

ent-

grad

ele

ndi

ng,

resp

ecti

vely

:

Ban

kL

end

ing i

t=

K ∑ i=1

αi+

β×

CD

Oit

+γ

′ ×X

it+

δ′ ×

Z t+

ε it.

Th

ede

pen

den

tva

riab

leis

the

log

ofba

nks

’LB

Ole

ndi

ng

inco

lum

ns

(1)t

o(3

).B

ank

i’sL

BO

loan

amou

nt

inye

arti

sca

lcu

late

das

the

sum

ofth

ecr

edit

the

ban

kre

ceiv

edas

ale

adar

ran

ger

inan

yof

the

sam

ple

LB

Olo

ans

arra

nge

din

year

t.L

BO

loan

sar

ecr

edit

sole

lyto

lead

arra

nge

rsan

deq

ual

lydi

s-tr

ibu

ted

toal

llea

dsw

hen

mu

ltip

lele

ads

pres

ent.

Inco

lum

ns

(4)t

o(6

),th

ede

pen

den

tva

riab

leis

the

log

ofin

vest

men

t-gr

ade

loan

sth

eba

nk

arra

nge

din

year

t,w

hic

his

obta

ined

from

Dea

lSca

nle

agu

eta

ble

repo

rts

and

adju

sted

for

mer

gers

.αi

isth

efi

xed

effe

ctfo

rba

nk

i.C

DO

itde

not

esu

nde

rwri

tin

gac

tivi

tyin

the

CD

Oor

broa

der

secu

riti

zed

mar

kets

for

ban

kii

nye

art.

Spe

cifi

call

y,L

og(B

ank

CD

O)i

sth

elo

gari

thm

ofth

eto

tala

mou

nt

ofC

DO

sba

nk

iun

derw

rite

sin

year

t;L

og(B

ank

Str

uct

ure

dC

DO

)exc

lude

sC

DO

sba

cked

byco

rpor

ate

loan

sor

bon

ds;L

og(B

ank

AB

S)i

sth

elo

gari

thm

ofth

eto

talv

ol-

um

eof

AB

Sth

eba

nk

un

derw

rite

sin

year

t.O

ne

isad

ded

toal

lth

ese

mea

sure

sbe

fore

taki

ng

the

log

toav

oid

zero

valu

es.X

itre

fers

toti

me-

vary

ing

ban

kch

arac

teri

stic

s.F

inan

cial

data

for

ban

ksar

eta

ken

from

Com

pust

at’s

Glo

balF

inan

cial

Ser

vice

.Bal

ance

shee

tsof

targ

etba

nks

are

mer

ged

into

acqu

ir-

ing

ban

ksth

rou

ghou

tth

esa

mpl

e.B

ank

Siz

eis

the

loga

rith

mof

the

tota

lass

ets

ofth

eba

nk.

Cap

ital

Rat

iois

tota

lequ

ity

divi

ded

byto

tala

sset

s.D

epos

itis

tota

lcu

stom

erde

posi

tsdi

vide

dby

tota

lass

ets.

Zt

refe

rsto

tim

e-va

ryin

gm

acro

vari

able

s;F

edF

un

ds

Rat

eis

the

ann

ual

aver

age

fede

ralf

un

dsra

te.

Col

um

ns

(7)

to(1

2)re

port

cros

s-se

ctio

nal

regr

essi

ons

ofba

nks

’tot

alL

BO

len

din

gan

din

vest

men

t-gr

ade

len

din

gdu

rin

g20

04to

Jun

e20

08on

thei

rC

DO

un

derw

riti

ng

volu

mes

duri

ng

1996

to20

03u

sin

gth

efo

llow

ing

mod

el:

Ban

kL

endi

ngbo

om,i

=α

+β

×C

DO

pre−

boom

,i+

γ′ ×

Xbo

om,i

+ε i

.

Th

ede

pen

den

tva

riab

leis

the

log

ofth

eto

tal

volu

me

ofL

BO

loan

sa

ban

kar

ran

ged

duri

ng

2004

toJu

ne

2008

inco

lum

ns

(7)

to(9

)an

dis

the

log

ofin

vest

men

t-gr

ade

len

din

gdu

rin

gth

esa

me

peri

odin

colu

mn

s(1

0)to

(12)

.L

og(B

ank

CD

O),

Log

(Ban

kS

tru

ctu

red

CD

O),

and

Log

(Ban

kA

BS

)ar

elo

gof

the

ban

k’s

tota

lu

nde

rwri

tin

gvo

lum

eof

all

CD

Os,

stru

ctu

red

CD

Os

(i.e

.,C

DO

sex

clu

din

gC

LO

san

dC

BO

s),

and

AB

Sdu

rin

g19

96to

2003

.O

bser

vati

ons

wit

hze

role

ndi

ng

duri

ng

the

boom

and

zero

preb

oom

CD

Ou

nde

rwri

tin

gar

eex

clu

ded.

Th

efi

nan

cial

rati

osof

the

ban

ksar

eca

lcu

late

das

the

aver

age

ofth

ean

nu

alra

tios

over

the

boom

peri

odfo

rea

chba

nk.

Abs

olu

teva

lues

oft-

stat

isti

csar

ere

port

edbe

low

coef

fici

ent

esti

mat

es.∗

∗∗,∗

∗ ,an

d∗

indi

cate

sst

atis

tica

lsig

nifi

can

ceat

the

1%,5

%,a

nd

10%

leve

l,re

spec

tive

ly.

(con

tin

ued

)


Tab

leII

I—C

onti

nu

ed

Ban

kF

ixed

-Eff

ect

Reg

ress

ion

sB

ank

Cro

ss-s

ecti

onal

Reg

ress

ion

s

Log

(LB

OL

oan

s)L

og(I

nv.

-Gra

deL

oan

s)L

og(L

BO

Loa

ns)

Log

(In

v.-G

rade

Loa

ns)

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

Log

(Ban

kC

DO

)0.

140.

100.

390.

121.

88∗

1.47

2.19

∗∗1.

34L

og(B

ank

Str

uct

ure

d0.

220.

080.

430.

13C

DO

)3.

08∗∗

∗1.

292.

35∗∗

1.28

Log

(Ban

kA

BS

)0.

140.

030.

450.

162.

00∗∗

0.46

3.22

∗∗∗

1.90

∗

Ban

kS

ize

1.11

1.06

1.20

1.04

1.07

1.13

1.73

1.51

0.84

1.53

1.51

1.31

4.89

∗∗∗

4.79

∗∗∗

5.62

∗∗∗

5.23

∗∗∗

5.49

∗∗∗

6.05

∗∗∗

1.99

∗1.

690.

952.

58∗∗

2.46

∗∗2.

22∗∗

Cap

ital

Rat

io−1

.26

−1.2

3−1

.32

−0.3

9−0

.39

−0.4

267

.564

.050

.451

.049

.645

.20.

830.

830.

880.

290.

300.

323.

44∗∗

∗3.

39∗∗

∗2.

98∗∗

∗4.

27∗∗

∗4.

16∗∗

∗3.

86∗∗

∗

Dep

osit

2.54

6.09

2.82

23.1

23.2

21.3

−8.0

4−8

.41

−4.5

512

.812

.313

.10.

330.

780.

363.

40∗∗

∗3.

37∗∗

∗3.

13∗∗

∗1.

031.

110.

632.

31∗∗

2.27

∗∗2.

58∗∗

Dep

osit

Squ

ared

−5.0

0−7

.53

−4.9

3−2

1.9

−21.

9−2

0.5

2.54

4.27

0.33

−16.

7−1

5.9

−16.

40.

681.

030.

683.

42∗∗

∗3.

39∗∗

∗3.

19∗∗

∗0.

240.

420.

034

2.22

∗∗2.

16∗∗

2.40

∗∗

Fed

Fu

nd

sR

ate

0.25

0.24

0.26

−0.0

6−0

.07

−0.0

63.

61∗∗

∗3.

51∗∗

∗3.

72∗∗

∗1.

031.

151.

07C

onst

ant

−13.

1−1

3.6

−14.

4−1

1.3

−11.

6−1

1.8

−20.

4−1

7.5

−8.9

0−1

5.8

−15.

4−1

3.0

4.13

∗∗∗

4.31

∗∗∗

4.51

∗∗∗

4.06

∗∗∗

4.18

∗∗∗

4.19

∗∗∗

1.92

∗1.

600.

822.

13∗∗

2.01

∗1.

75∗

Ban

kfi

xed-

effe

cts

Yes

Yes

Yes

Yes

Yes

Yes

Obs

erva

tion

s36

836

836

836

836

836

824

2424

3131

31N

um

ber

ofba

nks

3636

3636

3636

R2

0.14

0.15

0.14

0.15

0.15

0.15

0.76

0.77

0.81

0.78

0.78

0.79


reverse causality effect where the demand for LBOs led to greater volumes ofCDO underwriting since it also holds for structured CDOs and ABS, which donot invest in leveraged loans or high-yield bonds. In addition, the finding thata bank’s LBO lending in the boom years is related to its preboom underwritingin structured credit is reassuring in light of the high correlations of manyfinancing and underwriting activities during the boom years. Finally, consistentwith CDOs increasing the supply of capital for higher risk assets, we find thatstructured credit markets have a unique effect on lending in leveraged loanmarkets that is not observed in investment-grade loan markets.

D. Loan-Level Results on Allocation to Institutional Investors

The bank fixed effects results do not rule out the possibility of a time-varyingomitted factor. For example, it is possible that banks pursued riskier strategiesduring the LBO boom, lending more to LBOs and underwriting more CDOs,but that these activities were not directly linked to each other. While we cannotdirectly test for such time-varying omitted factors, we examine how LBO loanswere financed in the syndicated loan market. If the ability to obtain capitalfrom structured credit investors led a bank to extend LBO loans, these loansshould be heavily financed by CLO vehicles when the bank had access to theseinvestors through its underwriting capabilities.

To examine if LBO loans were financed from CLOs, ideally we would trackthe allocation of each loan to CLO vehicles but data do not permit this level ofprecision. However, we are able to identify a closely related measure, namely,the percentage of a loan that is placed with institutional investors, by calcu-lating the size of the institutional tranches of the loan. We expect this variableto reflect the allocation to CLO vehicles because they had become the mostimportant institutional investors during the LBO boom, accounting for 60% ofprimary activity in leveraged loans by 2006 (S&P (2006)).

We calculate the institutional loan allocation of each loan as the sum of theterm loan B, C, and D tranches that were sold to institutional investors andscale it by the amount of the loan. LBO loans in the post-2004 period werefinanced heavily by institutional investors (average of 60% vs. 44% in pre-2004). The term loan B, the most common institutional term loan, accountedfor 47% of LBO loans post-2004, compared to only 24% before.18 In the InternetAppendix, we provide summary statistics on LBO loans and their institutionalallocations.

Results from a Tobit model estimating the fraction of an LBO loan placedin institutional tranches are presented in Table IV. We use the total volumeof CDO underwriting (Total Lender CDO) of the lead banks in the year of theloan origination as an independent variable. To control for loan attributes,

18 The CLO market suffered after the third quarter of 2007 with issuance volume dropping to$34 billion from $48 billion in the prior quarter. As the CLO market declined, the bulk of newleveraged loans were sold to pro rata investors. In the second quarter of 2007, the institutionalallocation averaged 77%, which dropped to 45% in 2008 and to 29% in the first quarter of 2009.


Table IVTobit Regressions of Fraction of Institutional Tranches on Lenders’

CDO Underwriting ActivityThe dependent variable is the percentage of institutional tranches, calculated as all institutionalterm loans over total long-term borrowing amount of the LBO loan. Log(Total Lender CDO) is thelogarithm of the total volume of CDOs underwritten by all lead banks of the loan in the year theLBO deal is announced. Log(Total Lender Structured CDO) excludes alls CLOs and CBOs. LoanAmount is total long-term borrowing amount of the loan. LBO Deal Value is the transaction valueof the LBO deal net of fees and expenses. Loan Maturity is the value-weighted average maturityof all institutional tranches. Target EBITDA is the firm’s operating income over total assets inthe year before the deal. Std. Dev. of Target EBITDA is the standard deviation of Target EBITDAin the 5 years before the deal, and Industry Std. Dev. of EBITDA is the median of the standarddeviation of EBITDA for firms in the same Fama–French 48 industry over 5 years before the deal.Number of Banks is the number of lead banks and Bank Size is the logarithm of the average totalassets of the lead banks. The R2 from a corresponding OLS regression is reported for each model.Absolute values of t-statistics are reported in the second row for each independent variable. ∗∗∗, ∗∗,and ∗ indicates statistical significance at the 1%, 5%, and 10% level, respectively.

Percentage of Institutional Tranches

(1) (2) (3) (4) (5) (6)

Log(Total Lender CDO) 1.79 1.72 1.762.74∗∗∗ 2.73∗∗∗ 2.12∗∗

Log(Total Lender Structured CDO) 1.59 1.62 1.882.33∗∗ 2.46∗∗ 2.23∗∗

Loan Amount/LBO Deal Value −5.45 −2.28 −5.85 −5.32 −2.38 −5.923.02∗∗∗ 1.30 3.32∗∗∗ 2.92∗∗∗ 1.34 3.36∗∗∗

Loan Maturity −0.19 −0.04 −0.24 −0.18 −0.03 −0.241.76∗ 0.42 2.27∗∗ 1.70∗ 0.34 2.24∗∗

Loan Amount/EBITDA 0.55 0.24 0.57 0.52 0.21 0.582.41∗∗ 1.12 2.56∗∗ 2.28∗∗ 1.00 2.57∗∗

Log(LBO Deal Value) 3.94 2.61 3.02 4.07 2.57 3.002.59∗∗ 1.90∗ 1.86∗ 2.64∗∗∗ 1.84∗ 1.86∗

Target EBITDA −0.35 −0.25 −0.17 −0.38 −0.26 −0.191.39 1.08 0.71 1.48 1.12 0.78

Std. Dev. of Target EBITDA 0.66 0.87 0.77 0.66 0.89 0.801.10 1.62 1.31 1.09 1.65 1.37

Industry Std. Dev. of EBITDA 0.87 0.84 1.02 0.83 0.80 1.001.91∗ 2.06∗∗ 2.20∗∗ 1.81∗ 1.97∗ 2.17∗∗

Number of Banks 0.25 −3.05 12.5 0.62 −2.75 12.40.08 1.08 1.90∗ 0.20 0.98 1.88∗

Bank Size −0.14 −1.17 1.59 0.19 −1.03 1.680.08 0.71 0.85 0.11 0.62 0.91

Year dummies No Yes No No Yes NoBank dummies No No Yes No No YesConstant 40.58 26.94 24.5 37.57 30.50 23.6

1.90∗ 1.10 0.97 1.75∗ 1.21 0.94Observations 170 170 170 170 170 170OLS R2 0.32 0.47 0.31 0.32 0.47 0.31


we include the amount of the bank loan (relative to deal size and the tar-get’s operating cash flow) and its maturity, measured as the weighted averagematurity of all institutional tranches. The models control for several deal- andbank-specific variables. Deal-specific control variables include the size of theLBO deal measured by transaction value, the target’s operating cash flow, thevolatility of the target’s cash flow over the past 5 years, and the industry’scash flow volatility. Bank-specific controls include the number of lead arrang-ing banks and the size of the banks as measured by their total assets. Wealso estimate specifications with year dummies to alleviate concerns that theresults may be affected by the contemporaneous growth in other institutionalinvestors such as hedge funds during the LBO boom years. We also includebank dummies to control for bank-specific effects.

The results show a strong relation between the lead bank’s CDO underwrit-ing and the fraction of the loan funded through institutional tranches. Models(1) and (2) display a positive correlation between the institutional allocationand the total CDO underwriting volume of the lead banks. The point estimatesimply that a one-standard-deviation increase in the log of the bank’s CDO un-derwriting volume is associated with a 7% higher allocation to institutionaltranches. Model (3) shows that the relation between lead bank CDO under-writing and the fraction of institutional tranches holds with bank fixed effects.In models (4) to (6), we use the total structured CDO underwriting volumeof the lead banks (Total Lender Structured CDO) as an independent variableto alleviate concerns about reverse causality. We obtain similar results. Thus,the lead bank’s activities in structured CDO underwriting are also positivelyrelated to the fraction of the LBO loan allocated to institutional tranches andthe effect is of similar economic magnitude as Total Lender CDO.

Among the control variables, larger LBOs involve a higher proportion of loansfunded through institutional tranches, suggesting that structured credit mar-kets played a more important role for larger LBO transactions. Institutionalallocations are also larger when the LBO target is in an industry with morevariable cash flows, possibly reflecting greater risk appetite of institutional in-vestors such as CLOs than pro rata investors. Overall, the results suggest thata bank’s access to CDO capital through its underwriting activities is associatedwith a larger portion of its loan commitments being financed from CLOs.

E. CDO Access and Contractual Loan Terms

For further evidence linking a bank’s CDO underwriting activities to its LBOlending, we study the contractual terms of LBO loans. If CLOs increased thesupply of credit, we expect that banks with access to this supply through theirunderwriting activities should be able to offer lower spreads, all else equal.Financing LBO loans through CLOs might affect the covenant structure of LBOloans as well. Since banks are less likely to fund the loans from their balancesheet in such transactions, their incentive to monitor is potentially reduced.This may lead to less restrictive loan covenants if banks do not intend to monitorthe firm on an on-going basis. On the other hand, if loans financed through the


CLO channel are riskier, we may observe tighter covenants attached to theseloans to alleviate adverse selection concerns on the part of CLO investors.

E.1. Evidence on Loan Spreads

We use the spread on term loan B tranche to represent the spread on in-stitutional tranches.19 The average institutional spread dropped by almost 70basis points (or 20%), from 340 basis points to 271 basis points, from pre-2004to post-2004. In the Internet Appendix, we report the LBO loan spreads for thetwo subperiods.

We examine the effect of access to CDO capital on institutional spreads byusing the volume of CDO underwriting by the lead bank in the loan origina-tion year as a fraction of the bank’s total assets (Lender CDO Funding). Thisprovides a measure of the relative importance of the lead bank’s funding fromCDO investors versus other sources such as deposits and wholesale funding.We average this ratio across all lead banks when multiple lead arrangers werepresent. Lender CDO Funding averaged 1.1% before 2004 but rose to 1.9% inlater years (see the Internet Appendix), indicating that CDOs as a source offunding grew in importance for banks.

Table V shows that Lender CDO Funding is negatively related to institutionalspreads, implying that the cost of institutional tranches of LBO loans is lowerwhen the lead bank is active in CDO underwriting. Models (1) to (3) find thatthe coefficient on Lender CDO Funding is negative and significant at the 5%level. This effect is robust to the inclusion of GDP Growth, Fed Funds Rate, andPrime Rate as controls that might affect a bank’s lending costs. Differences inthe credit ratings of the loans cannot explain this effect, as shown in model (3).The economic magnitude of Lender CDO Funding is sizable. A one-standard-deviation increase in this measure is estimated to lower institutional spreadsby 17 to 20 basis points. We obtain similar results in model (4), which uses bankfixed effects. Thus, a bank’s access to CDO capital appears to reduce financingcosts for borrowers. A similar pattern is found by Ivashina and Sun (2011), whoshow that demand pressure from institutional investors in the syndicated loanmarket lowered spreads on institutional tranches.

The lower borrowing costs associated with structured lending are consistentwith those observed in loan sales. Parlour and Plantin (2008) develop a modelof loan sales and predict that in an active secondary loan market, firms borrowlarger quantities at lower prices. Guner (2006) shows that loans originated bybanks that engage in more loan sales are priced with lower spreads. In additionto possible supply-side effects, our result that structured credit is associatedwith lower institutional spreads might be attributable to the pricing errorsfor CDO tranches shown by Coval, Jureck, and Stafford (2009a) and Brennan,

19 We use term loan B tranches because other institutional term loans may be junior or havedifferent covenants, and the term loan B is the most common institutional term loan, accountingfor 78% of all institutional tranches after 2004. This measure of institutional spreads is also widelyused in practice.


Table VEffect of Lenders’ CDO Funding on Pricing and Covenants of

Institutional Tranches in LBO LoansThis table reports OLS regressions of institutional spreads in the first four models and marginaleffects from probit regressions of the likelihood of having a covenant-lite term loan B tranche in thelast three models. Lender CDO Funding is a bank’s CDO underwriting volume as a percentage of itsassets averaged across lead banks. Loan Maturity is value-weighted average maturity of all termloan B tranches. Log(Loan Amount) is log of the long-term loan amount. Loan Amount/EBITDAis loan amount divided by preannouncement operating income of the target. Target EBITDA ispercentage of operating income to target assets. Std. Dev. of Target EBITDA is standard deviationof Target EBITDA over the 5 years before the deal. Industry Std. Dev. of EBITDA is medianof standard deviation of EBITDA for firms in the same Fama–French 48 industry. Number ofBanks is the number of lead arrangers. Bank Size is log of average total assets of lead banks.GDP Growth is annual seasonally adjusted percentage change in GDP. Fed Funds Rate and PrimeRate are annual rates computed from the average monthly federal funds rate and the bank primerate. Rating dummies indicate if the loan is rated BB, B, or CCC. t-statistics are reported belowcoefficient estimates. ∗∗∗, ∗∗, and ∗ indicates statistical significance at the 1%, 5%, and 10% level,respectively.

Regression on Institutional Spread Probit on Covenant-Lite

(1) (2) (3) (4) (5) (6) (7)

Lender CDO Funding −15.31 −13.99 −16.51 −24.7 0.03 0.02 0.062.45∗∗ 2.28∗∗ 2.08∗∗ 2.17∗∗ 3.46∗∗∗ 3.42∗∗∗ 2.46∗∗

Log(Loan Amount) −14.03 −7.93 18.44 11.4 0.01 0.01 0.030.80 0.45 0.47 0.33 0.93 0.93 0.55

Loan Maturity −0.28 −0.13 −1.02 −0.28 0.00 0.00 0.010.65 0.30 1.45 0.44 2.52∗∗ 2.57∗∗ 1.69∗

Loan Amount/EBITDA −0.91 −0.93 −7.17 −6.61 0.00 0.00 0.010.64 0.66 1.06 1.14 0.51 0.85 0.57

Log(Target Assets) −3.29 −9.88 −28.05 −6.23 −0.01 −0.01 −0.030.20 0.60 0.75 0.19 1.07 0.96 0.47

Target EBITDA −0.49 −0.62 −1.91 −2.48 0.00 0.00 0.000.35 0.46 0.74 1.12 0.14 0.00 0.24

Std. Dev. of Target EBITDA −2.48 −1.83 −0.12 1.43 0.00 0.00 0.000.94 0.70 0.04 0.56 0.95 1.24 0.45

Industry Std. Dev. of EBITDA 2.36 2.01 2.61 4.07 0.00 0.00 0.011.28 1.11 1.14 1.90∗ 2.37∗∗ 2.43∗∗ 2.39∗∗

Number of Banks 8.47 5.52 3.52 17.5 −0.01 −0.01 −0.040.70 0.46 0.26 0.67 0.78 1.20 1.52

Bank Size −16.98 −21.28 −43.96 −33.7 0.05 0.04 0.111.52 1.91∗ 2.57∗∗ 1.54 2.71∗∗∗ 2.79∗∗∗ 2.11∗∗

GDP Growth −17.63 −4.92 6.94 −0.01 −0.021.94∗ 0.42 0.56 0.96 0.65

Fed Funds Rate −775.76 −553.46 −1,110 −0.22 −0.212.38∗∗ 1.39 2.86∗∗∗ 1.22 0.38

Prime Rate 765.16 554.68 1,120 0.22 0.192.36∗∗ 1.39 2.89∗∗∗ 1.20 0.35

Rating dummy No No Yes Yes No No YesBank dummy No No No Yes No No NoConstant 676.07 −1,427.55 −542.03 −2,623

4.63∗∗∗ 1.46 0.45 2.23∗∗

Observations 131 131 99 99 136 136 101R2 0.22 0.27 0.30 0.49


Hein, and Poon (2009). The lower spreads may also reflect a smaller monitor-ing premium charged by banks because banks may invest fewer resources inmonitoring borrowers when they sell the loans to CLOs.

E.2. Evidence on Covenant Structure

To examine the covenant structure of LBO loans, we consider the useof covenant-lite loans, a recent phenomenon in the leveraged loan market.Covenant-lite loans have only incurrence covenants (which are met at the ini-tiation of the loan), but none of the traditional maintenance covenants thatrequire borrowers to maintain financial ratios at prespecified levels on an on-going basis. Hence, covenant-lite loans impose much weaker financial require-ments on borrowers than traditional syndicated bank loans.20 According toS&P’s Leveraged Commentary & Data (LCD) (2007), outstanding covenant-liteloans accounted for less than 1% of leveraged loans before 2005 and increasedto 18% by the first half of 2007.

We identify covenant-lite tranches for each LBO loan using data from S&P’sLCD. In the post-2004 period, 21% of the LBO loans in our sample havecovenant-lite tranches but covenant-lite loans were absent prior to 2004 (seethe Internet Appendix). The last three columns in Table V report the marginaleffects from probit regressions for the probability that a term loan B has acovenant-lite tranche. In all specifications, Lender CDO Funding is associatedwith a higher likelihood that a loan has covenant-lite tranches. A one-standard-deviation increase in Lender CDO Funding is associated with a 5% to 9% in-crease in the probability of the loan being covenant-lite, a substantial effectconsidering that the unconditional probability of a covenant-lite loan is only11%. Among control variables, Bank Size has a significant effect, indicatingthat larger banks were more likely to extend covenant-lite loans.

The weaker covenant protection in CDO-driven loans suggests that loan fi-nancing from structured credit markets was associated with less ongoing mon-itoring. This is in sharp contrast to the more restrictive covenants Drucker andPuri (2008) document for loans sold in the secondary market. This differencein covenant protection highlights a key distinction between structured lendingand loan sales markets. In loan sales, loans are traded among banks and in-stitutions. Since the buyer bears the risk, it has the incentive to monitor theborrower and enforce covenants. Hence, the seller (and originator) of loans hasincentives to design tight covenants at origination to reduce information asym-metry in future sales. However, in the structured lending approach, loans areoriginated to be sold to a diverse set of investors in the CDO market instead ofan individual buyer. These investors, well diversified by holding only a slice of

20 While revolvers may still have a covenant package when the institutional tranche is covenant-lite, the existence of revolver covenants does not imply that the covenants on the institutionaltranche are immaterial. A firm may never draw upon the revolver and sometimes the revolver mayneed to be repaid in the event of a covenant breach. In such cases, the revolver covenants do notprotect the institutional investors.


a loan in a bigger collateral pool, lack the incentive and expertise to monitorand enforce contracts. This reduces the originating banks’ incentives to includerestrictive covenants.

Overall, our results highlight changes in the nature of bank lending thatoccurred in the wake of the growth in structured credit markets. As banksswitched from the traditional originate-and-hold to the originate-to-distributemodel, bank lending appears to have been driven more by expertise in under-writing than in their role as information producers. Our results suggest thatthis disintermediation of banks was accompanied by weaker covenants andlower spreads for borrowers.

E.3. Use of Bank Debt in LBO Deals

We now address how lead banks’ access to CDO capital is related to theamount of bank borrowing used to finance individual LBO deals. To do this,we aggregate multiple loans supporting the same deal and measure the totaluse of bank loans for each deal. Two ratios are calculated: bank loan amountover target EBITDA and target EBITDA over estimated interest payments onbank loans. Following Kaplan and Stein (1993), we estimate interest paymentsusing the sum of the 6-month LIBOR as of the effective date of the LBO dealplus the relevant credit spreads. The bank loan ratios are winsorized at the5% level to reduce noise from extreme values, primarily from the much smallerdeals in the sample.

Table VI examines the relation between lead banks’ CDO funding and theuse of bank loans, controlling for characteristics of the LBO targets. Model(1) shows that the loan to EBITDA ratio is positively related to Lender CDOFunding, suggesting that banks that used CDO funding heavily tolerated moreaggressive use of bank loans in LBO financing structure. Model (2) reportssimilar results controlling for year effects, suggesting that this effect is notdriven by time trends. To address reverse causality concerns, we also calculateLender Structured CDO Funding based on banks’ underwriting of structuredCDOs. Model (3) shows that the loan to EBITDA ratio is also positively relatedto this variable. This effect of Lender Structured CDO Funding is robust toinclusion of year dummies, as shown in model (4). Models (5) to (8) reportcomparable regressions using the EBITDA to interest ratio and show that it isnegatively related to our measures of CDO funding by lead banks. The economicmagnitude of the relation is meaningful. For example, model (1) implies that aone-standard-deviation increase in Lender CDO Funding leads to an increasein total bank borrowing equal to 58% of the target’s EBITDA.

Overall, the deal-level analysis suggests that when banks were more con-nected to the CDO investor base through their underwriting efforts, they lentmore aggressively to LBOs. The better access to credit for borrowers is con-sistent with the findings in the loan sales literature. Drucker and Puri (2008)show that loan sales, an alternative channel for banks to reduce their loanexposure, increase credit access for borrowers.


Table VIRegressions of Use of Bank Loans

This table reports the regressions of use of total bank loan financing in LBOs on lead banks’ CDOfunding. Lender CDO Funding is the lead bank’s CDO underwriting volume as a percentage of bankassets, averaged across all lead banks. Lender Structured CDO Funding excludes underwriting ofCLOs and CBOs. Target EBITDA is the target’s operating income scaled by total assets. Market-to-Book is the sum of market equity and total debt divided by total assets. Tangible Asset is calculatedas property, plant, and equipment divided by total assets. Book Leverage is the sum of long-termand short-term debt over total assets. Std. Dev. of Target EBITDA is the standard deviationof Target EBITDA in the 5 years before the deal. Industry EBITDA is the median operatingincome over assets for firms within the same Fama–French 48 industry in the year before theannouncement, and Industry Std. Dev. of EBITDA is the median of the standard deviation ofoperating income relative to assets for firms in the same industry over the 5 years before the deal.Target characteristics are measured in the year before the announcement of the LBO. Absolutevalues of t-statistics are reported in the second row for each independent variable. ∗∗∗, ∗∗, and ∗indicates statistical significance at the 1%, 5%, and 10% level, respectively.

Loan/EBITDA EBITDA/Interest

(1) (2) (3) (4) (5) (6) (7) (8)

Lender CDO Funding 0.49 0.35 −0.35 −0.252.59∗∗ 1.86∗ 2.47∗∗ 1.74∗

Lender Structured CDO 0.56 0.46 −0.47 −0.29Funding 2.02∗∗ 1.70∗ 2.18∗∗ 1.35

Log(Target Asset) −0.26 −0.52 −0.20 −0.50 0.28 0.29 0.25 0.281.67∗ 3.25∗∗∗ 1.29 3.17∗∗∗ 2.44∗∗ 2.47∗∗ 2.21∗∗ 2.40∗∗

Target EBITDA −10.4 −8.00 −10.2 −7.83 8.35 8.59 8.33 8.423.30∗∗∗ 2.60∗∗ 3.22∗∗∗ 2.54∗∗ 3.41∗∗∗ 3.67∗∗∗ 3.39∗∗∗ 3.59∗∗∗

Market-to-Book 1.55 1.04 1.63 1.07 −0.88 −0.50 −0.94 −0.523.88∗∗∗ 2.56∗∗ 4.07∗∗∗ 2.63∗∗∗ 3.00∗∗∗ 1.72∗ 3.20∗∗∗ 1.80∗

Book Leverage 0.18 0.30 0.12 0.30 −0.83 −0.50 −0.79 −0.530.24 0.40 0.16 0.40 1.50 0.91 1.43 0.97

Std. Dev. of Target −10.4 −5.04 −10.2 −4.83 −2.17 −7.03 −2.53 −6.97EBITDA 1.64 0.83 1.59 0.79 0.45 1.52 0.53 1.50

Growth of Sales 0.00 0.00 0.00 0.00 −0.00 −0.00 −0.00 −0.000.25 0.83 0.19 0.78 0.94 0.76 0.86 0.71

Tangible Asset −0.10 −0.09 −0.15 −0.12 −0.23 −0.55 −0.19 −0.530.19 0.18 0.30 0.25 0.61 1.56 0.52 1.50

Industry EBITDA 2.32 4.33 1.76 3.99 −0.81 −0.93 −0.32 −0.550.42 0.84 0.32 0.77 0.21 0.26 0.08 0.15

Industry Std. Dev. of 4.48 10.7 3.36 9.82 −4.36 −5.28 −3.29 −4.65EBITDA 0.54 1.37 0.40 1.26 0.74 0.97 0.56 0.85

Year dummy No Yes No Yes No Yes No YesConstant 6.37 6.67 6.19 6.69 2.32 2.07 2.40 1.95

4.58∗∗∗ 3.31∗∗∗ 4.44∗∗∗ 3.32∗∗∗ 2.27∗∗ 1.28 2.34∗∗ 1.20

Observations 222 222 222 222 184 184 184 184R2 0.12 0.29 0.11 0.29 0.13 0.35 0.13 0.34

IV. Implications for the Nature of LBO Transactions

The evidence suggests that banks’ access to structured credit markets wasassociated with larger bank loans, lower spreads, and weaker covenants. Wenow explore whether structured credit markets were associated with a changein the nature of LBO transactions. To do so, we study the characteristics of LBOtargets, the pricing and financing structure of LBO deals, and measures of expost performance. In the LBO boom of the late 1980s, Kaplan and Stein (1993)document an “overheated” market in which deals were overpriced, riskier, and


performed worse postbuyout. Guo, Hotchkiss, and Song (2011) find, however,that more recent deals during 1990 to 2006 were financed conservatively andhad lower premiums than deals in the late 1980s.

A. Target Characteristics

Table VII compares pre-LBO firm characteristics and the pricing of CDO-driven LBO deals (i.e., where the bank is heavily reliant on CDO funding,based on the median of Lender CDO Funding) to non-CDO-driven deals. Forthis comparison, we restrict our sample to deals announced after 2004. Guo,Hotchkiss, and Song (2011) show that LBOs after 2004 had riskier capitalstructures and were priced higher. Hence, restricting the sample to post-2004deals helps control for this temporal variation in deal attributes. In the InternetAppendix, we show that we obtain similar results using our entire sampleperiod.

Panel A shows that CDO-driven LBOs are much larger. The average (me-dian) transaction value for these deals is $4.45 billion ($1.7 billion), comparedto $1.64 billion ($264 million) for non-CDO-driven deals. CDO-driven dealsinvolve much better performing targets, with an average operating cash flowmargin of 15% (median of 13%), compared to 6% (median of 9%) for non-CDO-driven deals. CDO-driven deals are also less likely to be unprofitable companiesand have better growth opportunities, as measured by the market-to-book ra-tio. Prior to the LBO, they have similar leverage to non-CDO-driven deals.

Measures for potential value enhancement in LBOs such as free cash flowand tax payments are higher in CDO-driven deals. The targets in CDO-drivendeals generate, on average, two times (median of one time) more free cash flowthan non-CDO-driven deals. The higher free cash flows in CDO-driven dealssuggest that agency problems between management and shareholders mightbe more severe in these firms (Jensen (1986), Kaplan (1989b)). Therefore, byforcing management to use free cash flow more effectively, LBOs of the CDO-driven deals have the potential for creating more value. Moreover, the CDO-driven targets paid an average of 3.5% (median of 2.7%) of their assets intaxes, compared to an average of 0.6% (median of 0.1%) for non-CDO-drivendeals. The higher tax payments by targets of CDO-driven deals suggest thatthese firms have greater potential for creating value from additional interesttax shields by levering up (Kaplan (1989a), Guo, Hotchkiss, and Song (2011)).CDO-driven deals involve targets that are less risky. All measures of operatingrisk that we examine, including volatility of cash flows, volatility of growth incash flows, and volatility of growth in operating margin are at least 50% higherfor non-CDO-driven deals than for CDO-driven deals. The lower operating riskcharacteristics of CDO-driven deals suggest that they are better candidates forhighly levered transactions.

A concern with the univariate comparisons is that the LBO characteristicsmay simply reflect the much larger size of CDO-funded deals and the largebanks involved in these deals. Accordingly, in the Internet Appendix we reportregressions in which the LBO characteristics are estimated in a multivariate


Table VIIComparison of CDO-Driven Deals to Non-CDO-Driven Deals Post-2004The table compares LBO firm characteristics and pricing for CDO-driven deals and non-CDO-driven deals announced during 2004 to June 2008. CDO-driven (non-CDO-driven) deals are dealsfinanced by lenders with high (low) Lender CDO Funding, that is, above (below) the median of allLBO deals. Transaction value is the total consideration paid by the acquirer, including paymentto holders of common stock, preferred stock, options, warrants, and debts retired, but excludingfees and expenses. All financial ratios are measured in the last year before the deal is announced,and over the 5 years before, for standard deviation or growth measures. Market Equity is thestock price multiplied by the number of shares. Market-to-Book is the sum of Market Equity andtotal debt divided by total assets. EBITDA, CAPEX, and Tax Payment are calculated as operatingincome, capital expenditures, and income taxes over total assets, respectively. Tangible Asset isproperty, plant, and equipment divided by total assets. Cash is cash and short-term investmentsdivided by total assets. Book Leverage is total debt over total assets. Free Cash Flow is (sales −cost of goods − expenses − change in working capital − income taxes − dividend)/total assets. Std.Dev. of EBITDA is the standard deviation of EBITDA in the 5 years before the deal. Std. Dev. ofGrowth in EBITDA is the standard deviation of changes in EBITDA in the 5 years before the deal.Std. Dev. of Growth in Operating Margin is the standard deviation of changes in operating margin,defined as operating income over sales, in the 5 years before the deal. Premium is the premiumof the offered price over the stock price 1 month before the deal. FV/EBITDA is calculated as thetransaction value divided by operating income of the target in the year before the announcementand is missing if EBITDA is negative. EBITDA/FV is operating income divided by transactionvalue. The significance of the difference between the two subsamples is denoted with asterisks.∗∗∗, ∗∗, and ∗ indicates statistical significance at the 1%, 5%, and 10% level, respectively.

Non-CDO-Driven Deals CDO-Driven Deals

N Mean Median N Mean Median

Panel A: Target Firm Characteristics

Transaction Value ($ml) 58 1,642.1 264.0 99 4,454.6∗∗∗ 1,697.0∗∗∗Total Asset ($ml) 52 1,525.3 291.9 96 3,614.3∗∗ 3,197.5∗∗∗Market Equity ($ml) 49 906.5 204.0 94 1,337.0∗∗∗ 1,153.1∗∗∗Negative EBITDA 58 0.14 0 99 0∗∗∗ 0EBITDA 45 0.06 0.09 93 0.15∗∗∗ 0.13∗∗∗Market-to-Book 52 1.05 0.92 96 1.34∗∗∗ 1.20∗∗∗Growth of Asset 57 0.15 0.00 99 0.17 0.10∗∗∗CAPEX 51 0.04 0.03 95 0.05∗∗ 0.04∗Book Leverage 51 0.38 0.43 96 0.36 0.37Free Cash Flow 52 4.3% 4.8% 96 12.7%∗∗ 10.8%∗∗∗Tax Payment 52 0.6% 0.1% 96 3.5%∗∗∗ 2.7%∗∗∗Std. Dev. of EBITDA 52 0.05 0.04 96 0.03∗∗∗ 0.02∗∗Std. Dev. of Growth in EBITDA 49 1.29 0.49 94 0.53∗∗∗ 0.21∗∗∗Std. Dev. of Growth in 49 1.27 0.52 94 0.37∗∗∗ 0.15∗∗∗

Operating Margin

Panel B: LBO Pricing

Premium (%) 52 23.78 20.76 97 24.29 23.41FV/EBITDA 37 10.51 9.24 93 10.99 10.01EBITDA/FV 45 0.10 0.09 93 0.11 0.10


framework controlling for deal size and an indicator for large lead banks. Wefind that the differences in target characteristics across CDO and non-CDO-funded deals persist in the multivariate framework.

Thus, inspection of the firm characteristics does not suggest that structuredcredit promoted lower quality LBO transactions. Instead, it appears that thecapital from structured credit markets allowed much larger firms to be takenprivate. It is possible that these firms were suitable or desirable candidatesfor LBOs prior to the LBO boom and that the growth in structured credit cre-ated a mechanism for financing these transactions that overcame the lendingconstraints of banks.

B. LBO Pricing

We measure LBO pricing using Premium, the final offer price over the target’sstock price a month before the announcement, and the transaction multipleFV/EBITDA, the ratio of the transaction value to target EBITDA if EBITDAis positive. If target EBITDA is negative, FV/EBITDA is treated as missing.To include firms with negative EBITDA, we also calculate the inverse of thetransaction multiple, EBITDA/FV, which is monotonic in EBITDA. All threemeasures are winsorized at the 5% level to avoid extreme values, particularlyfrom small deals. Panel B of Table VII shows that LBO pricing is similar forCDO-driven and non-CDO-driven deals. The average premium in CDO-drivendeals is 24.3% (median of 23.4%), compared to 23.8% (median of 20.8%) fornon-CDO-driven deals. In comparison, Guo, Hotchkiss, and Song (2011) doc-ument a higher median premium of 29.2% for LBOs during 1990 and 2006.Using EBITDA/FV, we also find no significant difference in transaction mul-tiples across CDO- and non-CDO-driven LBOs. We also estimate multivariateregressions of LBO pricing that control for a number of target firm characteris-tics along with equity and credit market conditions but do not find meaningfuldifferences in pricing between CDO- and non-CDO-driven deals. In the Inter-net Appendix, we report the multivariate regression results on LBO pricingmetrics. Consistent with Axelson et al. (2010), these regressions show that thehigh-yield spread has a negative effect on LBO pricing.

C. Financing Structure

Even if the targets of CDO-driven deals were suitable LBO candidates, thedeals could be structured with greater financial risk. Table VIII provides thefinancing structure for deals in the post-2004 period,21 where we scale thesources of financing by the total funding need for the LBO. Not surprisingly,total funding needs are much higher for CDO-driven deals given the larger sizeof these deals.22 Consistent with earlier results, CDO-driven deals use moreterm loans as well as more total bank loans.

21 In the Internet Appendix, we report the financing structure for LBOs over our entire sampleperiod.

22 Note that total funding needs are greater than the transaction values reported in TableVII. This occurs because funding needs include deal fees and expenses and because this analy-sis is based on hand-collected data, which are unavailable for some smaller deals. In addition,


Table VIIIFinancing Structure of Post-2004 Deals

This table reports financing structure of the LBO deals after 2004 for which the complete financingstructure can be identified. The information is collected from proxy filings and supplemented withDealScan for bank loans and SDC for junk bonds. CDO-driven (non-CDO-driven) deals are dealsfinanced by lenders with high (low) Lender CDO Funding, that is, above (below) the median ofall LBO deals. Funding need, equity contribution, and asset-backed finance are collected solelyfrom proxy filings. Funding need is the total consideration including fees and expenses. Equitycontribution includes investment from private equity sponsors and pre-LBO equity investors. Thesignificance of the difference between the two subsamples is denoted with asterisks. ∗∗∗ and ∗∗indicates statistical significance at the 1% and 5% level, respectively.

Non-CDO-DrivenDeals CDO-Driven Deals

Mean Median Mean Median

Number of deals 35 89Funding need ($1m) 1,966.36 400 5,183.43∗∗∗ 1,900∗∗∗Bank loan financingRevolver 9.5% 5.0% 13.3% 7.7%∗∗∗Term loan 22.6% 0 38.1%∗∗∗ 36.8%∗∗∗Other bank loan 3.0% 0 3.2% 0

Total bank loan 35.1% 31.4% 54.7%∗∗∗ 50.9%∗∗∗Asset-backed finance 19.1% 0 4.3%∗∗ 0Junk bond/note & mezzanine 9.3% 0 19.3%∗∗∗ 22.2%∗∗∗Equity contribution 41.4% 37.4% 36.1% 31.8%Total financing arranged 104.9% 104.3% 114.4% 107.8%Total noncontingent financing arranged 95.4% 96.9% 101.1% 97.5%

CDO-driven deals also use more junk bonds/notes and mezzanine financ-ing, suggesting that bank loans are not used to substitute for other forms ofdebt.23 The equity contribution is similar across the two types of deals. Eq-uity investors, including pre-LBO shareholders, contribute, on average, 36%of the total funding need for CDO-driven deals and 41% for non-CDO-drivendeals. These equity contributions are higher than that documented by Kaplanand Stein (1993) in the late 1980s, but comparable to the 30.4% equity con-tribution in more recent LBOs studied by Guo, Hotchkiss, and Song (2011).24

When all financing sources are summed up, we find that CDO-driven dealsarranged more financing than needed to complete the deal. On average, CDO-driven deals arranged 14.4% more funding (including bank revolvers) thanneeded to complete the deal while non-CDO-driven deals raised 4.9% additional

Table VIII includes deals with no bank loans but excludes deals for which the equity contributioncannot be identified.

23 The greater use of junk bonds may appear surprising since CBO issuance activity droppedin the post-2004 period. However, banks’ incentives in CBO underwriting were relatively unaf-fected as securitization markets grew since underwriting activities were not subject to capitalrequirements. In contrast, capital considerations were potentially much more important for lend-ing decisions by banks.

24 These results hold in a multivariate framework controlling for LBO deal size and large leadbanks, as shown in the Internet Appendix.


funding. Excluding revolvers, CDO-driven deals, on average, arranged justenough funding to complete the deal without requiring a draw on their bankrevolvers, while the non-CDO-driven deals had an average shortfall of 5%,which would require a draw on bank revolvers or use of cash holdings to com-plete the deal.

Overall, we do not find that a bank’s access to structured credit led to riskierfinancing structures in the LBOs it financed as the equity contribution in CDO-and non-CDO-driven deals is similar. Instead, it appears that in CDO-fundeddeals, management teams and financial sponsors arranged more financing thanneeded to complete the deal, possibly to provide more financial flexibility andlower the probability of financial distress following the LBO.

D. Post-LBO Bond Rating Changes

For ex post evidence on the quality of CDO-driven LBOs, we consider thetrajectory of credit ratings following the buyout. To account for overall economictrends, we create a control sample of public firms that did not conduct an LBO.For each LBO firm with an issuer credit rating available from S&P, we startwith all public firms in the same one-digit SIC code and with a credit rating fromS&P in the year of the LBO’s effective date, and include the firms where thepercentage difference in total assets and leverage from the LBO firm is belowthe median difference. From this group, we select the firm that has the closestleverage ratio to the LBO firm at the effective date of LBO. This procedureresults in a time, industry, size, and leverage matched control sample. Boththe LBO and control firm samples have an average post-LBO leverage ratio of0.68 and credit rating of B+.

Table IX reports the changes in credit ratings for LBO firms and matchingfirms up to 3 years following the effective date of the LBO. Panel A shows thatthe average rating change for LBO firms was similar to matching firms forthe first 2 years after the deal, but by the third year credit ratings declinedfor LBO firms more than that for matching firms. By the third year, LBOfirms were downgraded by an average of 1.27 notches while ratings droppedby 0.55 notches for matching firms, with the difference significant at the 10%level. However, this decline in ratings for LBO firms was driven largely bynon-CDO-driven LBO deals. Panel B shows that the post-LBO rating changesfor the CDO-driven deals were similar to their matching firms, suggesting thatthese deals did not perform worse than comparable firms. In contrast, Panel Cshows that credit ratings for non-CDO-driven deals were downgraded by 1.58notches for LBO firms on average, and only by 0.17 for matching firms. Thus,the post-LBO changes in credit ratings do not suggest that the CDO-drivendeals experienced weaker ex post performance than non-CDO-driven deals.

In summary, inspection of target characteristics, deal pricing, financingstructure, and post-LBO bond rating changes does not suggest that the growthin structured credit was associated with an overheated LBO market. This ev-idence is in contrast to Kaplan and Stein’s (1993) findings on the earlier LBOboom of the late 1980s. One explanation is that structured credit allowed muchlarger companies to be taken private than was previously possible and that

Did Structured Credit Fuel the LBO Boom? 1323T

able

IXP

ost-

LB

OC

red

itR

atin

gC

han

ges

Th

ista

ble

repo

rts

the

chan

ges

incr

edit

rati

ngs

afte

rth

eL

BO

for

LB

Ofi

rms

and

mat

chin

gfi

rms

wit

hsi

mil

arch

arac

teri

stic

sas

the

post

-LB

Ofi

rms.

Rat

ing

chan

ges

are

calc

ula

ted

asth

edi

ffer

ence

inn

um

eric

alra

tin

gn

um

bers

assi

gned

tora

tin

gca

tego

ries

(e.g

.,1

isas

sign

edfo

rA

+,2

for

A,3

for

A−,

etc.

).T

he

perc

enta

geof

firm

sex

peri

enci

ng

ado

wn

grad

ein

each

peri

odis

also

repo

rted

.Th

em

atch

ing

firm

sar

ese

lect

edas

foll

ows.

For

each

LB

Ofi

rmw

ith

anis

suer

cred

itra

tin

gav

aila

ble

from

S&

P,w

est

art

wit

hal

lfi

rms

wit

hth

esa

me

one-

digi

tS

ICco

dean

dw

ith

cred

itra

tin

gsin

the

year

ofth

eef

fect

ive

date

.We

then

excl

ude

firm

sw

ith

ape

rcen

tage

diff

eren

cein

tota

las

sets

orle

vera

gefr

omth

epo

st-L

BO

firm

abov

eth

em

edia

nle

vel.

Am

ong

the

rem

ain

ing

firm

s,w

ese

lect

the

firm

wit

hth

ele

vera

gecl

oses

tto

the

post

-LB

Ole

vera

ge.t

-sta

tist

ics

test

ing

wh

eth

erth

eav

erag

era

tin

gch

ange

isdi

ffer

ent

for

the

LB

Osa

mpl

ean

dth

em

atch

ing

sam

ple

are

repo

rted

inth

ela

stco

lum

n.

Rat

ing

chan

ges

for

all

LB

Ofi

rms

and

mat

chin

gfi

rms

are

pres

ente

din

Pan

elA

.Pan

els

Ban

dC

repo

rtth

eco

mpa

riso

nfo

rC

DO

-dri

ven

deal

san

dn

on-C

DO

deal

sal

ong

wit

hth

eir

mat

chin

gfi

rms,

resp

ecti

vely

.C

DO

-dri

ven

(non

-CD

O-d

rive

n)L

BO

firm

sar

efi

nan

ced

byle

nde

rsw

ith

hig

h(l

ow)

Len

der

CD

OF

un

din

g,th

atis

,abo

ve(b

elow

)th

em

edia

nof

allL

BO

deal

s.T

he

sign

ifica

nce

ofth

edi

ffer

ence

betw

een

the

two

subs

ampl

esis

den

oted

wit

has

teri

sks.

∗∗an

d∗

indi

cate

sst

atis

tica

lsi

gnifi

can

ceat

the

5%an

d10

%le

vel,

resp

ecti

vely

.

LB

OF

irm

sM

atch

ing

Fir

ms

t-S

tat

for

Rat

ing

%of

Rat

ing

Rat

ing

NC

han

ges

Dow

ngr

ade

NC

han

ges

Dow

ngr

ade

Ch

ange

s

Pan

elA

:All

LB

OF

irm

san

dT

hei

rM

atch

ing

Fir

ms

Med

ian

rati

ng

wh

enth

eL

BO

deal

isef

fect

ive

141

B+

110

B+

0.54

Rat

ing

chan

gefr

omef

fect

ive

date

to1

year

afte

r13

00.

2012

.3%

101

0.29

20.8

%−0

.55

Rat

ing

chan

gefr

omef

fect

ive

date

to2

year

saf

ter

116

0.67

34.5

%89

0.60

31.5

%0.

33R

atin

gch

ange

from

effe

ctiv

eda

teto

3ye

ars

afte

r75

1.27

45.3

%49

0.55

34.7

%1.

85∗

Pan

elB

:CD

O-D

rive

nL

BO

Fir

ms

and

Th

eir

Mat

chin

gF

irm

s

Med

ian

rati

ng

wh

enth

eL

BO

deal

isef

fect

ive

85B

+71

B+

0.28

Rat

ing

chan

gefr

omef

fect

ive

date

to1

year

afte

r81

0.35

14.8

%68

0.35

22.1

%−0

.04

Rat

ing

chan

gefr

omef

fect

ive

date

to2

year

saf

ter

730.

6834

.2%

600.

6233

.3%

0.26

Rat

ing

chan

gefr

omef

fect

ive

date

to3

year

saf

ter

370.

9540

.5%

250.

9248

.0%

0.05

Pan

elC

:Non

-CD

O-D

rive

nL

BO

Fir

ms

and

Th

eir

Mat

chin

gF

irm

s

Med

ian

rati

ng

wh

enth

eL

BO

deal

isef

fect

ive

56B

+39

B+

1.09

Rat

ing

chan

gefr

omef

fect

ive

date

to1

year

afte

r49

−0.0

48.

2%33

0.15

18.2

%−0

.77

Rat

ing

chan

gefr

omef

fect

ive

date

to2

year

saf

ter

430.

6534

.9%

290.

5527

.6%

0.22

Rat

ing

chan

gefr

omef

fect

ive

date

to3

year

saf

ter

381.

5850

.0%

240.

1720

.8%

2.35

∗∗


these firms were desirable LBO candidates that might have previously beendifficult to take private but stood to benefit from LBOs that reduced agencycosts. It is also worth noting that the LBO boom studied by Kaplan and Stein(1993) involved much higher leverage than the recent boom we study, a trendalso shown by Guo, Hotchkiss, and Song (2011) for deals after the 1980s. In con-trast to the evidence from subprime mortgage lending (Mian and Sufi (2009),Keys et al. (2010)), our evidence does not suggest that loan securitization ledto weaker lending standards in corporate lending. Our results are consistent,however, with other recent work showing that securitized corporate loans didnot perform worse (Benmelech, Dlugosz, and Ivashina (2010)).

V. Conclusion

We study how the growth in structured credit markets affected the corporateuse of leverage by examining LBO transactions that rely heavily on debt financ-ing. We argue that developments that led to the growth of structured creditwere associated with increased credit supply that at least partially fueled therecent LBO boom. Our evidence highlights important linkages between struc-tured credit, the dual role of banks in the structured credit markets as loanoriginators and underwriters, and the use of leverage.

Our results demonstrate an important connection between the lending andunderwriting activities of banks in credit markets. We find that banks’ un-derwriting activities in structured credit markets were associated with greateraccess to credit, cheaper credit prices, and looser covenants. We do not find thatstructured credit was associated with worse LBO deals, in contrast to the evi-dence from the junk bond–fueled LBO wave of the late 1980s and more recentevidence from mortgage securitizations. However, our evidence does raise thequestion of whether structured credit investors were adequately compensatedfor their risks given lower spreads, higher leverage, and weaker covenant pro-tection. Thus, it is possible that structured credit was mispriced as suggestedby Coval, Jurek, and Stafford (2009a), perhaps due to biases in the credit rat-ing agency process for structured credit instruments (Brennan, Hein, and Poon(2009), Griffin and Tang (2010)).

We are not suggesting that structured credit markets were the only devel-opment fueling the LBO boom of 2004 to 2009. Firms during this period hadlarge cash balances and many generated sizeable cash flows, factors prone tocreate agency conflicts. Recent work such as Brav et al. (2008) documents theemergence of activist hedge funds that sought to correct some of these agencyconflicts. Financial sponsors raised record levels of capital and employed trans-action structures such as club deals (Boone and Mulherin (2009), Officer, Ozbas,and Sensoy (2010)), allowing them to pursue very large LBO deals. However,our results point to an important role of structured credit markets that wasassociated with easing the availability and pricing of bank credit, thereby af-fecting the LBO market and potentially other forms of leveraged activities.

Our results also offer a potential explanation for a puzzling aspect of therecent financial crisis—why did large commercial banks invest heavily inCDO tranches that later proved to be a source of massive asset write-downs?


Our findings suggest that the underwriting activities of commercial banks instructured credit markets might be part of the answer. Banks appear to haveoriginated large LBO loans with the intent of selling them to structured creditvehicles. Regulatory initiatives toward risk-based capital management mighthave allowed them to pursue these activities in a manner that was friendlyto their capital requirements. Further analysis of this possible explanationappears to be a useful area for future research.

Appendix: Glossary

Collateralized debt obligations (CDOs): The notes issued by a specialpurpose vehicle (SPV), which are collateralized by a portfolio of assetsacquired by the SPV. Notes issued are structured into different tranches,reflecting different seniority in claiming the cash flows from the collat-eral pool of the SPV.

CDO vehicle: The SPV set up to issue CDOs is often referred to as the CDOvehicle. The issued notes represent the liabilities of the CDO vehiclewhile the securities or assets it acquires to back the notes are its assets.

Collateralized loan obligations (CLOs): CDOs backed by a portfolio ofcorporate loans. These loans typically have speculative-grade credit rat-ings.

Collateralized bond obligations (CBOs): CDOs backed by a portfolio ofbonds.

Structured product CDOs: CDOs backed by a portfolio of structuredproducts, including mortgage-backed securities, asset-backed securities,other CDOs, etc.

Balance sheet CDOs: CDOs issued for the purpose of removing existingassets (or the risk of assets) from the balance sheet of the seller.

Arbitrage CDOs: CDOs created in an attempt to capture a mismatch be-tween the yield of CDO collateral and the financing cost of CDO tranches.In arbitrage CDOs, issuers typically do not have the underlying assetsand need to purchase them in the marketplace.

CDO tranches: The liabilities of CDO vehicles are divided into trancheswith differing levels of seniority and hence risk. Equity tranches ab-sorb the first loss, followed by mezzanine tranches, and then, senior(and in some cases, super senior) tranches.

Leveraged loan: A bank loan issued to a borrower with speculative-graderatings, also referred to as a high-yield loan.

Syndicated loan: A loan funded by a group of lenders. A syndicated loan istypically structured, arranged, and administrated by one or several com-mercial or investment banks, known as the lead arranger. The lead ar-rangers are responsible for negotiating terms with the borrowers, raisingthe fund from syndicate members, and administrating the loan contract.Large syndicated loans are commonly structured in multiple tranches,which may include revolvers, term loan A, term loan B, and other termloans.


Term loan A: A type of installment loan that allows a borrower to draw onthe loan during a short commitment period and then pay it back witha progressive repayment schedule. It is also referred to as an amortiz-ing term loan and typically allocated to commercial banks along withrevolvers as part of a larger syndication.

Institutional tranche (Term loan B, C, D, etc.): Term loan tranchescarved out for nonbank institutional investors in a larger syndication.Term loan B is the most commonly used institutional term loan. A largesyndicate loan may have term loans C, D, E, F, or H. Compared to a termloan A, which is commonly allocated to pro rata investors, institutionalterm loans have longer maturity and a back end–loaded repaymentschedule. The institutional investors are typically CLO vehicles, primefunds, hedge funds, and insurance companies.

Pro rata tranche: Includes revolvers and term loan A. These tranches arecommonly funded by banks and finance companies, which are referredto as pro rata investors in the syndicated loan market.

Pro rata spread: The drawn spread for pro rata tranches in a syndicatedloan.

Institutional spread: The credit spread on institutional term loans. Sinceterm loan B is the most common institutional tranche, the spread onterm loan B is widely used as the institutional spread. Because of theback end-loaded repayment schedule in institutional tranches, insti-tutional spreads are usually higher than the spread on the pro ratatranches in the same loan.

Covenant-lite loans: Loans with only incurrence covenants (which are metat the initiation of the loan) but none of the traditional maintenancecovenants that require borrowers to maintain financial ratios at pre-specified levels at the end of every quarter.

REFERENCESAcharya, Viral, and Tim Johnson, 2010, More insiders, more insider trading: Evidence from private

equity buyouts, Journal of Financial Economics 98, 500–523.Axelson, Ulf, Tim Jenkinson, Per Stromberg, and Michael S. Weisbach, 2010, Borrow cheap, buy

high? The determinants of leverage and pricing in buyouts, Working paper, Ohio State Uni-versity.

Barclays, 2002, The Barclays Capital Guide to Cash Flow Collateralized Debt Obligations (Bar-clays, London).

Becker, Bo, and Victoria Ivashina, 2010, Cyclicality of credit supply: Firm-level evidence, Workingpaper, Harvard University.

Benmelech, Efraim, and Jennifer Dlugosz, 2009, The alchemy of CDO credit ratings, Journal ofMonetary Economics 56, 617–634.

Benmelech, Efraim, Jennifer Dlugosz, and Victoria Ivashina, 2010, Securitization without adverseselection: The case of CLOs, Working paper, Harvard University.

Boone, Audra L., and J. Harold Mulherin, 2009, Do private equity consortiums impede takeovercompetition? Working paper, University of Georgia.

Brav, Alon, Wei Jiang, Frank Partnoy, and Randall Thomas, 2008, Hedge fund ac-tivism, corporate governance, and firm performance, Journal of Finance 63, 1729–1775.


Brennan, Michael J., Julia Hein, and Ser-Huang Poon, 2009, Tranching and rating, EuropeanFinancial Management 15, 891–922.

Brunnermeier, Markus, 2009, Deciphering the liquidity and credit crunch of 2007–2008, Journalof Economic Perspectives 23, 77–100.

Citi Credit Research, 2007, Global structured credit strategy (Citigroup, New York, NY).Coval, Joshua D., Jakub W. Jurek, and Erik Stafford, 2009a, Economic catastrophe bonds, Ameri-

can Economic Review 99, 628–666.Coval, Joshua D., Jakub W. Jurek, and Erik Stafford, 2009b, The economics of structured finance,

Journal of Economic Perspectives 23, 3–25.Demiroglu, Cem, and Christopher James, 2010, The role of private equity group reputation in LBO

financing, Journal of Financial Economics 96, 306–330.Drucker, Steven, and Manju Puri, 2008, On loan sales, loan contracting, and lending relationships,

Review of Financial Studies 14, 263–294.Faulkender, Michael, and Mitchell A. Petersen, 2006, Does the source of capital affect capital

structure? Review of Financial Studies 19, 45–79.Fitch Ratings, 2007, Inside the ratings: What credit ratings mean (Fitch Group, New York, NY).Griffin, John, and Dragon Tang, 2010, Did subjectivity play a role in CDO credit ratings? Working

paper, University of Texas at Austin.Guner, Burak A., 2006, Loan sales and the cost of corporate borrowing, Review of Financial Studies

19, 687–715.Guo, Shourun, Edith S. Hotchkiss, and Weihong Song, 2011, Do buyouts (still) create value?

Journal of Finance 66, 479–517.Ivashina, Victoria, and Anna Kovner, 2011, The private equity advantage: Leveraged buyout firms

and relationship banking, Review of Financial Studies, doi:10.1093/rfs/hhr024.Ivashina, Victoria, and Zheng Sun, 2011, Institutional demand pressure and the cost of corporate

loans, Journal of Financial Economics 99, 500–522.JP Morgan, 2007, 2008 US fixed income outlook (JP Morgan, New York, NY).Jensen, Michael C., 1986, Agency costs of free cash flows, corporate finance and takeovers, Ameri-

can Economic Review 76, 323–329.Kaplan, Steven N., 1989a, Management buyouts: Evidence on taxes as a source of value, Journal

of Finance 44, 611–632.Kaplan, Steven N., 1989b, The effects of management buyouts on operating performance and value,

Journal of Financial Economics 24, 217–254.Kaplan, Steven N., and Jeremy C. Stein, 1993, The evolution of buyout pricing and financial

structure in the 1980s, Quarterly Journal of Economics 108, 313–357.Kaplan, Steven N., and Per Stromberg, 2009, Leveraged buyouts and private equity, Journal of

Economic Perspectives 23, 121–146.Keys, Benjamin, Tanmoy Mukherjee, Amit Seru, and Vikrant Vig, 2010, Did securitization lead to

lax screening? Evidence from subprime loans, Quarterly Journal of Economics 125, 307–362.Leary, Mark T., 2009, Bank loan supply, lender choice, and corporate capital structure, Journal of

Finance 64, 1143–1185.Lemmon, Michael, and Michael Roberts, 2010, The response of corporate financing and investment

to changes in the supply of credit, Journal of Financial and Quantitative Analysis 45, 555–587.Leveraged Commentary & Data (LCD), 2007, LCD LoanStats Weekly, September 27.Loutskina, Elena, and Philip E. Strahan, 2009, Securitization and the declining impact of bank

finance on loan supply: Evidence from mortgage originations, Journal of Finance 64, 861–889.

Metrick, Andrew, and Ayoko Yasuda, 2010, The economics of private equity funds, Review ofFinancial Studies 23, 2303–2341.

Mian, Atif, and Amir Sufi, 2009, The consequences of mortgage credit expansion: Evidence fromthe U.S. mortgage default crisis, Quarterly Journal of Economics 124, 1449–1496.

Modigliani, Franco, and Merton Miller, 1958, The cost of capital, corporate finance, and the theoryof investment, American Economic Review 48, 261–297.

Nini, Greg, 2008, How non-banks increased the supply of bank loans: Evidence from institutionalterm loans, Working paper, University of Pennsylvania.


Officer, Micah S., Oguzhan Ozbas, and Berk A. Sensoy, 2010, Club deals in leveraged buyouts,Journal of Financial Economics 98, 214–240.

Parlour, Christine A., and Guillaume Plantin, 2008, Loan sales and relationship banking, Journalof Finance 63, 1291–1314.

Standard & Poor’s (S&P), 2006, A guide to the loan market (New York, NY).Sufi, Amir, 2007, Information asymmetry and financing arrangements: Evidence from syndicated

loans, Journal of Finance 62, 629–668.Sufi, Amir, 2009, The real effects of debt certification: Evidence from the introduction of bank loan

ratings, Review of Financial Studies 22, 1659–1691.

did structured credit fuel the lbo...

Documents