bank' loan ownership and troubled debt restructurings

8/20/2019 'Bank' Loan Ownership and Troubled Debt Restructurings

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“Bank” Loan Ownership and Troubled Debt Restructurings

Cem DemirogluCollege of Administrative Sciences and Economics

Koc UniversityIstanbul, TURKEY 34450

[email protected] (90-212) 338-1620

Christopher JamesWarrington College of Business

University of FloridaGainesville, FL 32611-7168

[email protected] (352) 392-3486

First Draft: April 15, 2013

mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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1. Introduction

It is generally assumed that bank loans are easier to renegotiate or restructure in financial distress

than other private debt claims, public debt, and trade credit.1 This assumption is based, in part, on the idea

that bank loans are associated with more concentrated ownership, which reduces the severity of holdout

and free rider problems in out of court debt restructurings. In addition, achieving a consensus on a debt

restructuring involving bank debt may be easier because bank lenders are thought to be more

sophisticated than other kinds of lenders and they may be better informed due to their ongoing

involvement in monitoring covenants and collateral, which in turn may reduce the information

asymmetries between the borrower and creditors that can derail out of court restructurings. Moreover,

banks may be more willing than “arm’s length” creditors to provide concessions outside of bankruptcy,

since they may obtain benefits from maintaining an existing relationship with the borrower (e.g., future

information rents and revenues from non-lending businesses) and establishing reputation for funding their

borrowers even in distressed times. Consistent with the relative ease of restructuring bank debt, Gilson,

John, and Lang (1990) (henceforth GJL), using data from the period 1978 through 1987, find that

financially troubled public firms that owe more of their debt to banks are more likely to succeed in

restructuring their debt privately and avoid a presumably more costly bankruptcy restructuring.2

During the two decades following the publication of GJL’s influential study, bank lending has

undergone a number of significant changes. Perhaps most important, have been the entry of nonbank

lenders into the term loan market and the growth of loan syndications (and a corresponding decline in

single lender loans).3 For example, according to Bord and Santos (2012), while most syndicated term

loans continue to be arranged by banks, the percentage (by dollar value) of newly issued term loans held

by banks at origination declined steadily from roughly 80% in 1988 to slightly more than 20% in 2007.

Institutional lenders such as collateralized loan/debt obligations (CLOs) and asset management firms

(hedge funds, private equity funds, mutual funds etc.) as well as investment banks and finance companies

have become the main providers of term loan funding, especially for highly levered firms.

Not only has the funding of “bank” term loans changed over time, but the relative importance of

syndicated lending has increased. For example, according to our calculations based on Dealscan data, the

percentage of loan facilities (by dollar amount) that were syndicated increased from 57% in the 1987

1 See, for example, Bulow and Shoven (1978), Smith and Warner (1979), Hart and Moore (1995), Bolton

and Freixas (2000), and Hotchkiss, John, Mooridian, and Thornburn (2008). 2 Using data from the 1980s and the 1990s, Asquith, Gertner, and Sharfstein (1994) and James (1995, 1996)

examine the role that banks play in debt restructurings involving firms with public debt outstanding.3 The former coincides with the introduction with bank loan ratings, the development of markets for trading

distressed loans, and an increase in loan securitizations.


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through 1994 period to 97.5% during the 2006 through 2011 period. Over this same time period,

syndicates became substantially larger with the median number of syndicate members increasing from

two in the earlier period to six in the later period.4

In light of these changes, in this paper we examine whether the ease of restructuring for firms that

rely on “bank” loans has changed. Specifically, we examine whether the number and type of lenders that

partici pate “bank” loans affect the nature of the restructuring process. Our analysis is based on a hand-

collected sample of 344 debt restructuring transactions (out of court restructurings and bankruptcies) by

publicly traded firms during the 2000 to mid-2012 time period. As we discuss later, our sample selection

criteria are similar to the criteria used by GJL and Gilson (1989, 1990), thus facilitating a comparison of

recent distressed debt restructurings to those in the 1970s and the 1980s.

We posit that syndicated loans with dispersed ownership are likely to be significantly more

difficult to restructure than single bank relationship loans that were more prevalent in the 1970s and the

1980s (and are the subject of most theoretical models of bank lending). For example, syndicated loans are

likely to be associated with higher coordination costs in renegotiations and more severe free-rider and

holdout problems. Also, while institutional lenders may be quite sophisticated, they may not have access

to or the incentive to generate the type of detailed “soft” information typically associated with

relationship-based bank loans. In addition, to the extent that loans are securitized, asset managers of

CLOs may not have the same incentives or flexibility to renegotiate loans that traditional banks are

assumed to have.5 Finally, institutional lenders are presumably less likely than traditional banks to invest

in building or maintaining relationships with borrowers and they might be less likely to be concerned

about developing a reputation for being supportive to distressed borrowers. Different incentives of bank

and nonbank members of the syndicate might also lead to inter-creditor disputes, increasing restructuring

costs and reducing probability of restructuring success.

We begin by examining whether a firm’s reliance on “ bank ” loans (measured by “ bank ” debt to

total liabilities) is related to the likelihood that the firm successfully restructures its troubled debt without

entering bankruptcy. We first use a definition of bank loans closest to the one used by GJL, which

includes only loans in which commercial banks or insurance companies participated as lenders. We then

expand the definition to include lending by investment banks. Finally, we include loans from institutional

lenders and finance companies in a broader definition of “ bank ” lending. Using the GJL definition of

4 Given that Dealscan tracks only large loan facilities, the percentage of all loans (by number) that are

syndicated is likely to be much lower than reported in Dealscan.5 See Piskorski, Seru, and Vig (2010) for a discussion of the difficulties associated with renegotiating

securitized mortgage loans. They find that the likelihood of renegotiating a securitized mortgage loan is much lower

than a loan held in the portfolio of the originating bank.


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bank borrowing, we find a positive and significant relationship between the likelihood of a successful out

of court restructuring and a firm’s reliance on bank financing; the same result found by GJL using data

from the late 1970s and the 1980s. However, as we broaden the definition of bank lending, the positive

relationship between the likelihood of restructuring outside of bankruptcy and reliance on “bank”

borrowing weakens. For example, if bank loans are defined broadly to include all loans from investment

banks and institutional lenders, we find no significant relationship between the likelihood of a successful

out of court restructuring and reliance on bank loans broadly defined.

We next examine whether the likelihood of an out of court restructuring is related to the number

and type of lenders involved in the firm’s loan facilities and whether or not the loan is securitized. To

examine the importance of dispersion of loan ownership, we divide bank loans into loans funded by a

single bank lender and syndicated loans. Also, to investigate whether the identity of the lender matters

(rather than the number of lenders), we examine whether the likelihood of an out of court restructuring

differs by whether the lenders in the syndicate includes institutional lenders. We also examine the

relationship between the likelihood of a restructuring and a proxy for whether the loan was securitized.

Overall, we find that the likelihood of a successful debt restructuring is positively related to a

firm’s reliance on loans from a single bank. We find no significant relationship between the likelihood of

a successful out of court restructuring and reliance on syndicated bank borrowing. Indeed, we find a

similar relationship between the likelihood of a successful restructuring and a firm’s reliance on public

debt and syndicated bank loans. Turning to institutional loans (all of which are syndicated), we find a

negative and statistically significant relationship between the likelihood of a successful out of court

restructuring and the firm’s reliance on institutional loans. Comparing syndicated bank loans and public

debt to institutional loans, we find that reliance on institutional loans is associated with a significantly

lower likelihood of successful restructuring than reliance on syndicated bank loans or public debt. These

findings suggest that both the number and identity of the lenders are related to the ability of distressed

firms to restructure outside of bankruptcy.

To investigate why institutional loans appear to be more difficult to restructure out of court, we

divide institutional loans into two groups, loans with attributes associated with securitization based on

Nadauld and Weisbach’s (2012) identification strategy (securitized loans) and institutional loans that are

unlikely to be securitized (unsecuritized loans). Using this classification, we find that the negative and

significant relationship between the likelihood of an out of court restructuring and reliance on institutional

loans is driven entirely by reliance on securitized loans. Indeed, we find no difference in the relationship

between the likelihood of a successful restructuring and reliance on unsecuritized institutional loans

versus syndicated bank loans.


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Our findings suggest that securitized loans are more difficult to restructure outside of bankruptcy

than are public and other private debt claims. One explanation for this result is that holdout problems are

more difficult to resolve when loans are securitized. There are several reasons to suspect this might be the

case. First, securitized loans are more diffusely held than unsecuritized institutional loans and syndicated

bank loans. Second, in many CLOs, even when asset managers have discretion to renegotiating troubled

loans, the dispersion of property rights in a securitization, arising from the complex capital structure

CLOs employ, may create conflicts among CLO investors in terms of their interests in renegotiating

versus declaring a default on a loan (so-called “tranche warfare”).6 Specifically, holders of senior tranches

may have little interest in the asset manager renegotiating a loan, since any upside gains associated with a

restructuring accrue the junior and equity tranches of the CLO.7 Moreover, since securitized loans are

typically senior secured claims, distressed borrowers may not be able to entice hold outs to participate in a

restructuring by offering them more senior claims — a strategy frequently employed in public exchange

offers.8 Finally, while loan restructurings are not governed by the Trust Indenture Act, syndicated loans

typically require the unanimous consent of all investors to change core terms of the loan agreement, such

as the maturity of the loan, the principal owed, or the interest rate (see Sufi (2007)).9

We examine the importance of potential holdout problems associated with securitized loans by

examining the relationship between reliance on securitized loans and the likelihood of a prepackaged

bankruptcy. In a traditional Chapter 11 bankruptcy, sometimes referred to as “free fall” restructuring, the

firm enters bankruptcy to use the tools of Chapter 11 of the Bankruptcy Code to reach agreement with its

major stakeholders and to restructure its operations. In contrast, in a prepackaged bankruptcy, the firm

enters bankruptcy after negotiating the terms of a restructuring with creditors. Thus, unlike traditional

Chapter 11 cases, prepackaged plans are typically not used to resolve inter-creditor or debtor-creditor

disputes, but rather they are used to put the prearranged plan into effect. As a result, a prepackaged plan is

generally considered a tool for dealing with holdouts (see McConnell and Servaes (1991) and Tashjian,

6 Some CLOs require the asset manager to sell loans that are in default within three to 12 months,

effectively removing asset manager ’s discretion. Requiring a quick sale of troubled loans may lead asset managers

to take a more passive role when dealing with a distressed borrower. See “The Barclay’s Capital Guide to Cash

Flow Collateralized Debt Obligation” March 2002 for a discussion of the role of asset managers in loan workouts.7

Complicating these conflicts further is the fact that asset managers typically hold the most junior or equitytranches of the CLOs.

8 See James (1996) and GJL for a discussions of the use of senior debt to resolve holdout problems in

restructurings of distressed public as well as private debt.9 A loan or an interest in a loan is generally not considered a security under the Securities Act of 1933. The

leading case on whether an interest in a syndicated loan is a security for regulatory purposes is Reves v Earnst &

Young, 494, U.S. 56,110 S. Ct 945 (1990). In Reves, the Supreme Court set forth the so called “family resemblance”

test for the determination of whether a note is a security.


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Lease, and McConnell (1996)). For a prepackaged plan to be accepted, it must be approved by the

majority of a firm’s creditors (2/3 by value and 1/2 by number). If approved, the plan becomes binding on

all parties, thus eliminating any economic benefit associated with nonparticipation. Given the speed by

which prepackaged bankruptcies can be implemented and the fact that they are prenegotiated outside of

bankruptcy, prepackaged bankruptcies are often seen as a hybrid form of corporate restructuring

combining some of the features of an out of court restructuring with some of the features of a traditional

Chapter 11 reorganization.10

Consistent with the hypothesis that the holdout problems are more severe when the debtor has

term loans that are securitized, we find that the likelihood that of a prepackaged bankruptcy is

significantly higher for firms that rely more on securitized loans rather than on other forms of bank

lending. While prepacks are more frequent for firms that rely more on securitized loans, we find no

significant difference in terms of recovery rates or the likelihood of emerging from bankruptcy as an

independent firm and reliance on various forms of “bank” lending. Moreover, we find no difference in the

frequency of loan impairment or the frequency of equity-for-debt exchanges between securitized loans

restructured via prepacks and unsecuritized institutional loans and bank loans restructured out of court.

Overall, our results suggest that the relationship between the ease of restructuring out of court and

reliance on bank borrowing is quite sensitive to what types of lenders are considered “ banks” and the

number of lenders involved in the loan syndicate. These finds are based on an empirical strategy similar

to the one use GJL and others in which reliance on bank borrowing is a proxy for the severity of holdout

and information problems. Unlike these previous studies, we have data on the number of lenders involved

in the loan facility and thus are able to investigate in more detail the role of coordination problems in debt

restructurings. Nevertheless, the obvious concern remains that syndicated or securitized loans or the

borrowers that utilize these types of lending facilities differ in some systematic way from borrowers that

rely on a single bank borrower.11 Indeed, previous studies of syndicated and securitized lending as well as

recent studies of debt specialization generally find that smaller, less transparent firms and firms with more

intangible assets rely more on concentrated sources of borrowing (see, e.g., Sufi (2007), James and

10 See Tashjian, Lease and McConnell (1996). Prepackaged bankruptcies may also have other advantages

over out of court restructurings. For example, as McConnell and Sarvaes (1991) explain, tax benefits may play a rolein encouraging firms that would have otherwise restructured outside of bankruptcy to file a prepack. Specifically,

net operating loss (NOLs) tax benefits are treated differently in prepacks than in an out of court restructuring. Also,

tax treatment of cancelled debt obligations (CODs) is more favorable in Chapter 11 than in an out of court

restructuring. The importance of CODs and NOLs in affecting the choice of prepacks versus out of court

restructurings will depend on the overall profitability of the distressed firm.11 For example, Bolton and Scharfstein (1996) argue that coordination problems in bankruptcy might

influence ex ante debt ownership structures.


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Houston (1996), and Colla, Ippolito, and Li (2013)). Moreover, for rated firms, Rauh and Sufi (2010) find

that low credit quality firms tend to use several tiers of debt. Our dataset allows us to control for

observable differences in debtor’s characteristics such as profitability and the composition and

concentration of claims. Of course, even with these controls, we cannot rule out that solo, syndicated, and

securitized loans or the borrowers that used these facilities differ in some unobservable characteristics.

Given this concern, our analysis is best viewed as descriptive.

Our analysis adds to the literature in several important ways. First, we update earlier studies on

the role of banks in the restructuring process. Updating these studies is important given the changes that

have occurred in the bank loan market as well as recent changes in the nature of bankruptcy

proceedings.12 Second, by collecting detailed information on the debt structure of the distressed firms in

our sample, we are able to examine how reliance on institutional and bank loans as well as public and

private debt is related to the likelihood of a successful out of court restructuring. Third, while several

recent papers examine the role of institutional investors (such as hedge funds and private equity groups)

in Chapter 11 proceedings, we provide evidence on how the involvement of these institutions in the loan

market is related to the likelihood of restructuring out of court. Finally, we provide additional insights on

the substitutability of institutional and traditional bank loans by documenting how reliance on these loans

is related to the resolution of financial distress. Our findings add to the results of recent theoretical and

empirical studies that suggest traditional bank “specialness” is reduced when bank loans are diffusely held

and traded in the secondary market (see, e.g., Parlour and Plantin (2008) and Gande and Saunders

(2012)).

2. Background

A number of recent studies document the growth of syndicated lending and, during the last

decade, the growth of the institutional term loan market. Given these studies, we provide only a brief

description of several salient features of the term loan market and refer the reader to these other studies

for a description of the institutional details. An excellent review of the institutional features of the

syndicated loan market can be found in Taylor and Sansone (2006), Sufi (2007), Ivashina (2009), and

Ivashina and Sun (2011). For a discussion of the role of institutional lenders and CLOs in the loan market,

see Nadauld and Weisbach (2012)

and Nini (2012).

12 Bharath, Panchapegesan, and Werner (2009) argue that the growth of debtor in possession (DIP)

financing and Key Employee Retention Plans (KERP) have led to a decline in the incidence of violations of absolute

priority rule (APR). See also Dahiya, John, Puri, and Ramirez (2003) for a discussion of the growth of DIP financing

in the 1990s.


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As Sufi (2007) explains, a syndicated loan is a loan jointly issued by more than one financial

institution. The syndication process begins with the lead arranger, typically a commercial or investment

bank, who puts together a preliminary loan agreement with the borrower. The preliminary loan agreement

includes some key loan terms such as financial covenants, collateral, and loan amount. While borrowers

typically employ only one lead arranger, some large syndicates have multiple lead arrangers. According

to Dealscan the mean and median number of lead arrangers over the 1987 to 2011 time period was 1.18

and 1 respectively. After reaching a preliminary loan agreement, the arranging bank then turns to

participating lenders to fund the loan. Once participants are in place and their share of the loan is

determined, a final loan agreement (that includes the interest rate on the loan) is signed by all parties.

After the loan is activated, the lead arranger acts as an agent bank that administers the loans. The job of

the agent bank is primarily ministerial in that it is not a trustee or fiduciary for the other lenders. Most

credit agreements contain a so-called exculpation clause that makes clear that the agent is not under any

duty to ascertain whether or not the borrower is complying with the provisions of the loan agreement. As

Taylor and Sansone (2007) explain, the exculpation clause typically affirms that “each lender takes full

responsibility for its own credit decisions with respect to the borrower and for obtaining such information

the lender deems appropriate in extending credit to the borrower and monitoring the loan” (see page 357).

Changes in key terms of the loan contract such as principal, maturity, and the interest typically

require unanimous consent of the syndicate members. Changes in other provisions of the loan agreement

(such as changes in or the waiver of financial covenants) typically require the approval of a simple

majority of syndicate members (see Taylor and Sansone (2007), chapter 9).

Most syndicated loan deals consist of multiple facilities including a revolver and one or more

term loans. The revolver is a credit line that can be drawn at the borrower ’s discretion (conditional on

compliance with covenants and borrowing base requirements) and is typically retained by the initial

syndicate lenders. Syndicated loan deals with more than one term loan typically consist of a Term A (A

stands for amortizing) and a Term B (B stands for bullet or non-amortizing) facility. The revolver, Term

A, and Term B facilities are generally secured and have the same seniority; however given the amortizing

nature of the Term A loan, the longer tenor and non-amortizing (or slow amortizing) nature of the Term B

loan, and the discretionary use of the revolver, the interest rates on these facilities will differ. Term B is

sometimes referred to as the institutional tranche, reflecting that it is mainly funded by institutional

lenders, whereas the revolver and Term A are referred to as pro-rata tranches suggesting they are


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syndicated and generally retained by banks.13 The Term B facility might be securitized or held by

nonbank institutional investors such as mutual funds, pension funds, private equity funds, and hedge

funds. As Nadauld and Weisbach (2012) explain, CLOs find Term B loans appealing because the bullet

payment implies a longer duration than the amortizing Term A loan, which reduces reinvestment risk for

the asset manager of the CLO.

3. Sample selection and summary statistics

3.1 Sample

We use a slight variant of the two-step sampling procedure in Gilson (1989, 1990) and Gilson,

John, and Lang (1990), to construct our sample of troubled debt restructurings. The first-step is to identify

a sample of financially distressed firms, defined as firms with extreme declines in their stock prices. The

idea, based on an argument in GJL, is that an extreme decline in the share price of a firm is an

unambiguous indicator of financial distress. The second step is to identify instances of troubled debt

restructurings, or out of court debt restructuring and bankruptcies, by the distressed firms.

We start by calculating, for each calendar year between 2001 and 2011, three-year cumulative

year-end returns on the ordinary shares (The Center for Research in Security Prices (CRSP) share codes

10 and 11) of US firms listed in CRSP monthly return files. If a stock is delisted from CRSP before the

end of the calendar year, we adjust its returns with CRSP delisting return and assume zero percent return

for the remainder of the year. We exclude the shares of utilities (Standard Industrial Classification (SIC)

codes 4900-4999) and financial firms (SIC codes 6000-6999). We next select firms whose shares were

ranked in the bottom five percent of the return distribution for the calendar year and pool selected firms

for each year to construct a preliminary sample of financially distressed firms. We obtain financial

information for those firms from Compustat annual files. We exclude firms with book assets less than

$100 million (in year 2000 prices) during all three fiscal year-ends over which we measure stock returns,

since financial troubles of very small firms are less likely to attract the attention of the news media and

public disclosure of these firms tend to be limited. We also exclude firms with a book leverage ratio less

than 30% and earnings before interest, taxes, depreciation, and amortization (EBITDA) to interest

expense greater than three in the same time window since those firms are unlikely to be financially

distressed. The resulting sample consists of 646 firm-years (428 unique firms) of financial distress.

13 Sometimes deals include Term C and Term D facilities which are also intended to be funded by

institutional investors. Since these other trances are structured like the B tranche, they are often referred to as B

tranches even though the name assigned to them uses another letter of the alphabet (other than A).


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We next search for evidence of an out of court debt restructuring or a bankruptcy filing by these

firms during the (-2, +2) calendar year window centered on the year at the end of which the firm was

ranked in the bottom five percent of the CRSP return distribution.

Bankruptcies are relatively easy to

identify as they start with a formal filing at a bankruptcy court. We use Bankruptcydata.com, LoPucki’s

Bankruptcy Research Database, Moody’s Default Recovery Database (MDRD), and Capital IQ (CIQ) to

identify bankruptcy filings. However, for out of court restructurings there is no formal filing or a

consensus definition. We use the GJL definition of an out of court debt restructuring, which is a

transaction in which the creditors of a financially distressed firm agree to one or more of the following

changes in the debt agreement in response to an actual or anticipated default: (1) reduce principal or

interest payments on the debt, (2) take equity securities or securities convertible into equity in exchange

for some or all of the outstanding debt claims, (3) extend the maturity of the debt. Firms obtain similar

concessions from their creditors in bankruptcy. We identify out of court debt restructurings by searching

Factiva news stories, debt footnotes in firm 10-K filings, CIQ’s key developments database, and MDRD.

If we find evidence that a firm is in financial distress (e.g., in default of debt payments) at the end of the (-

2, +2) year search window, we continue to track the firm until the distress is resolved or it is no longer

mentioned in the news or the firm’s 10-K filings. We treat restructuring transactions by the same firm

within a year of each other as a single transaction. Also, we include restructurings followed in less than a

year by a bankruptcy filing in the bankruptcy sample and exclude them from the out of court

restructurings sample. Overall, we identify 344 restructuring transactions, 174 out of court restructurings

and 170 bankruptcies.14

Similar to GJL, we assume that the restructuring begins with the earliest of the following events:

payment default, issuance of going concern doubt by the auditor, rumors about a payment default or

bankruptcy, hiring of an investment bank to help restructure the debt, denial of bankruptcy rumors by

senior management, initial announcement of debt restructuring or reference to an ongoing restructuring,

and conclusion of debt restructuring.15 We obtain information on the presence and dates of these events of

distress searching Factiva news stories, debt footnotes in firm 10-K filings (10-Q or 8-K filings if the 10-

14 These transactions are by 262 unique firms. In our sample, there are two separate out of court

restructurings by 23 firms, and three restructurings by six firms. Moreover, 37 firms restructured their debt once out

of court and once in bankruptcy; two firms filed for bankruptcy twice; one firm restructured its debt twice in bankruptcy and once out of court; and finally three firms restructured their debt twice out of court and once in

bankruptcy.15 Unlike GJL, we do not include technical violations on financial covenants in this list, since covenant

violations do not generally trigger a debt restructuring unless they are followed by more serious events of default

(see also footnote 5 in GJL). A majority of sample restructurings would begin with a covenant violation, had we

included covenant violations in the list. The main results in the paper however are not sensitive to whether we use

covenant violations in identifying the beginning date of restructuring.


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K is unavailable), payment default information in MDRD, and CIQ’s key developments database during

the two year period ending on the date of restructuring or bankruptcy filing.

Our sampling procedure differs from the procedure in GJL several ways. First, we do not restrict

our sample to firms whose shares are listed in New York Stock Exchange (NYSE) and American Stock

Exchange (AMEX); we include all industrial US firms in CRSP.16 Second, we eliminate low leverage

firms that are unlikely to be financially distressed and very small firms that might not be covered as

extensively by the news media as bigger firms. Third, we do not exclude prepackaged bankruptcies as

they have become more common during our sample period. As reported later in Table 1, one-third of the

bankruptcies in our sample are prepackaged, whereas GJL identified only one prepackaged bankruptcy in

their sample period.

An alternative sampling approach is to rely on reported cases of default. However, conditioning

on default would exclude firms that restructure their debt to avoid default. Such preemptive debt

restructurings are quite common in our sample. For example, as reported later in Panel A of Table 2,

37.9% of the firms that restructured their debt out of court experienced neither a payment default nor a

covenant violation prior to the completion of restructuring, and only 20.1% experienced a payment

default.

Table 1 presents time-series distribution of out of court restructurings and bankruptcy filings in

our sample. As shown, roughly one-third of the sample bankruptcies (34.7%) are prepackaged. There is

no significant time trend in the frequency of prepacks relative to traditional bankruptcies. Not

surprisingly debt restructurings increase during economic downturns (2001-2002 and 2008-2010).

Though not tabulated, we find that the correlation between total number of restructuring transactions and

the annual growth rate of real gross domestic product (GDP) in the restructuring year (excluding year

2012 for which we don’t yet have annual GDP numbers) is -0.744 (significantly different from zero at the

1% level).17 There is no significant relation between GDP growth and the ratio of out of court

restructurings to bankruptcies and the latter does not exhibit a significant time trend.

Panel A of Table 2 presents the proportion of sample firms that experienced various events of

distress before the final resolution (or conclusion) of the debt restructuring. We report the results

separately for the overall sample (left panel), out of court sample (middle panel), and bankruptcy sample

(right panel). We also report differences in the frequencies of events for the out of court and bankruptcy

samples. As shown, 37.5% of the firms in the overall sample defaulted on an interest or a principal

16 At the time GJL was published, CRSP’s coverage of NASD stocks was limited.17 We use the seasonally adjusted annual rate reported by the Bureau of Economic Analysis (series code

GDPC1 in FRED (Federal Reserve Economic Data)).


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payment (or both) and 64.0% violated a private debt covenant. Moreover, for 29.4% of the firms the

auditor raised substantial doubt about the firm’s ability to continue as a going concern. Further, 27.3% of

the firms hired an investment bank to help the firm in the debt restructuring. These events of distress are

more likely to be experienced by firms in the bankruptcy sample than firms in the out of court sample,

although average stock returns of the two groups of firms are similar by construction. We think that these

differences are consistent with the preemptive nature of some out of court debt restructurings, as

discussed above.

Panel B of Table 2 shows the frequency of events corresponding to the beginning of debt

restructurings. As shown, 24.1% of debt restructurings in the bankruptcy sample begin with a payment

default, 21.8% begin with an announcement that the firm hired a restructuring adviser, and 18.2% begin

when the auditor of the firm issues an opinion that it has substantial doubts about the firm’s ability to

continue as going concern. Also, for 14.1% of the firms in this sample, the first public reference to the

restructuring is on the bankruptcy date.

In the out of court sample, for 40.8% of the firms the first public reference to the restructuring is

on or after the conclusion date. For these firms we set the beginning date of restructuring to the

conclusion date. Also, 23% of out of court restructurings begin with an initial announcement of debt

restructuring or reference to an ongoing restructuring. Further, 11.5% of debt restructurings begin with

the issuance of going concern doubt by the firm’s auditor, 9.8% begin with the announcement of a

payment default, and 9.8% begin when the firm hires a restructuring adviser.

2.2 Univariate evidence on the determinants of bankruptcy vs. out of court restructuring

Table 3 presents a comparison of the univariate characteristics of firms in the out of court and

bankruptcy samples. All of the variables in the table are measured at the end of the last fiscal year before

the beginning date of debt restructuring. Measures of firm financial condition are based on Compustat

data, whereas measures of debt structure and debt concentration are primarily on based on data hand-

collected from firm 10-K filings and supplemented by other data sources as discussed below.18 To reduce

the influence of potential outliers we winsorized all Compustat-based financial ratios at the top and

bottom 2.5%. We do not winsorize debt composition measures because they are by construction between

zero and one. Also, we deflated book assets to year 2000 prices using seasonally-adjusted Consumer Price

Index (CPI) for all urban consumers. Variable definitions are available in the Appendix.

18 All of the results in the paper are robust to measuring firm financial condition using quarterly instead of

annual Compustat data.


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We start by examining differences between out of court and bankruptcy samples in terms of firm

financial condition. As shown, most measures of firm financial condition including size and tangibility

have mean and median values that are not significantly different across the two samples. There are,

however, a few notable differences. For example, the average cash to assets ratio is 6% higher in the out

of court sample (15.3% vs. 9.3%, the difference is significant at the 1% level). This difference does not

appear to arise from differences in profitability, investment spending, research and development

expenses, or propensity to pay dividends, since we do not find differences between the two samples along

these dimensions. Rather, the evidence suggests that firms in the bankruptcy sample have better access to

external debt markets, as evidenced by a significantly higher fraction of firms with a credit rating (53.5%

vs. 43.7%) and a significantly higher average book leverage (66.4% vs. 60.1%), which reduces their need

to hoard precautionary cash. The higher average debt ratio of the bankruptcy sample is primarily due to

greater reliance of firms in this sample on securitized loans (loans funded by CLOs).19 When we compare

debt and interest coverage ratios of the two groups of firms, we find that firms in the bankruptcy sample

have slightly higher operating earnings per unit of debt or interest expense (not significant). Thus, at the

onset of restructuring, firms in the bankruptcy sample do not appear to have greater default risk due to

carrying more debt relative to their assets.

GJL find that firms with more intangible assets are more likely to restructure their debt out of

court. Consistent with this finding, we find that the median (but not average) three-year Tobin’s Q is

significantly higher in the out of court sample. However, when we examine the most recent Qs (not

tabulated), we do not find a significant difference between the two groups of firms (the average one-year

Q is 1.04 for both samples and the median is 0.85 and 0.81 for the out of court and bankruptcy sample,

respectively). Overall, there is no evidence in Table 3 to suggest that firms in the bankruptcy sample were

in significantly worse financial shape than firms in the out of court sample at the onset of restructuring.

We next examine whether the two groups of firms rely on different types of debt. Before

discussing the results, a brief description of data collection and variable definitions is in order. We gather

the list of debt obligations for each firm from debt footnotes in the 10-K filings. We update missing

information on the source of debt as well as debt seniority and security using CIQ, Dealscan (for loans),

and SDC Platinum (for bonds). We categorize debt obligations as: private loans (drawn credit lines, Term

A loans, and institutional term loans (e.g., Term B or C loans)), straight and convertible bonds (public

bonds (including commercial paper and medium term notes), Rule 144A and non-Rule 144A debt (private

placements), equipment and mortgage debt, borrowings from affiliates (i.e., parent company, executives,

19 A negative relationship between access to CLO funding and cash holdings is consistent with the notion

that securitization reduces firm financial constraints.


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directors), and other debt (including capitalized lease obligations, revenue bonds, and unclassified debt).20

We measure trade credit as the difference between total liabilities and total debt.

We identify the lenders involved in private loans using Dealscan. If the loan is not in Dealscan,

we identify the lenders from the original loan contract attached as an exhibit to an SEC filing of the

borrowing firm (if the loan is considered by the SEC as a material source of funding for the firm). We

categorize lenders as: commercial banks, insurance companies, investment banks, finance companies,

hedge funds, private equity funds, mutual funds, pension funds and endowments, CLOs, and corporations.

To make these categorizations, we use lenders’ names, SIC codes, and institution types in Dealscan as

well as their business descriptions on CIQ, their web pages, and Google.com. To identify hedge funds we

also use TASS, CISDM, hedgefundnewswire.com, and Nelson’s Investment Database. Further, to identify

CLOs, we use Moody’s CDOEdge Structure Library.

As discussed in the data appendix, we identify lenders at the subsidiary level and not the parent

level. For example, the investment banking subsidiary of a commercial bank holding company would be

classified as an investment bank, whereas a loan from the banking subsidiary would be classified as a

bank loan. The reasons for this classification are twofold. First, given the combination of commercial

banking with investment banking, the subsidiary in which the loan is originated is likely to be more

reflective of the type of relationship the lender has with the borrower. Second, unlike investors in public

securities, loan participants may recieve detailed nonpublic information from the borrower. To avoid

concerns that nonpublic information may be shared with the public securities trading desk, lenders often

establish ethical walls so that public market trading personnel do not have access to the nonpublic

information provided to the lending subsidiary.21

GJL consider all outstanding liabilities to commercial banks and insurance companies when

measuring firms’ reliance on bank debt. We modify this definition two ways. First, following Rauh and

Sufi (2010), we classify mortgage and equipment loans as a separate (i.e., non-bank) class of secured

debt, since these loans generally receive a separate treatment in debt restructurings and bankruptcy.22

Second, because there is no clear motivation for treating loans by insurance companies but not investment

banks or finance companies as bank loans, we define bank debt ratio three different ways: (1) Loans

provided only by commercial banks or insurance companies/Liabilities ( Banks as defined by GJL), (2)

20 We classify notes and debentures not recorded by SDC Platinum as private placements. Also, using firm

SEC filings, we identify bonds recorded by SDC as Rule 144A private placements but were subsequently exchanged

for registered bonds and classify them as public bonds.21 See Taylor and Sansone (2007) pages 620-623.22 Including mortgage and equipment loans does not change any of our main findings in a meaningful way

and in fact slightly strengthen them.


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Loans provided only by commercial banks, insurance companies, or investment banks/Liabilities ( Banks

plus investment banks), and (3) All credit lines, Term A, and Term B loans/Liabilities ( Banks broadly

defined ).23 Note that very few loans in our sample are funded by insurance companies so that our findings

are not sensitive to excluding insurance company loans form the sample of bank loans. We use the first of

the three measures as our primary measure of reliance on bank loans because it is the closest to the

measure used by GJL. In some of our analyses, we distinguish between solo and syndicated bank loans.

We consider all Term B loans as well as credit lines and Term A loans with one or more

institutional lenders (i.e., CLOs, hedge fund, private equity fund, mutual fund, pension fund, or

endowment) as institutional loans.24 We also distinguish between securitized and unsecuritized

institutional loans. In many cases, we only have information on the identity of the loan participants at the

time of origination and not on who holds the loan at the onset of the restructuring. This is a common

problem encountered by empirical studies of the syndicated loan market (see, for example, Ivashina and

Sun (2011)). However, this is a particular concern when examining the securitization of term loans since,

for loans that are securitized, arranging banks often initially acquire the loan and then transfer or sell the

loan to a CLO or another institutional lender.25 To address this problem we employ the empirical strategy

used by Nadauld and Weisbach (2012) who use information on actual CLO holdings to identify attributes

of loan facilities that are correlated with securitization activity. Specifically, we consider an institutional

loan as securitized if one or more CLO participate the original syndicate or the loan is a Term B loan

originated by one of the top 10 CLO originators listed in Nadauld and Weisbach (2012). We consider the

remaining institutional loans as unsecuritized.26

Table 3 includes univariate results on differences in debt composition between firms that

restructure their debt out of court vs. through bankruptcy. We first revisit the evidence in GJL that firms

that owe more of their debt to banks are more likely to restructure their debt out of court. As discussed

above, we measure reliance on banks three different ways. None of those three measures indicate a

significant difference between the out of court and bankruptcy samples. The mean value of our primary

23 Several recent studies classify bank loans broadly as all credit lines and term loans unconditional on the

source of funding (see Sufi and Rauh (2010)), whereas others limit bank loans to loans made only by commercial or

investment banks (see Lim, Minton, and Weisbach (2012)). Also, some studies on syndicated loans focus on the

arranger’s identity and relationship with the borrower (see Ivashina (2009) and Sufi (2007)). 24 We obtain qualitatively similar results when we consider only Term B loans as institutional.25

Dealscan does not track loan ownership after origination. In a recent study of syndicated loans, Bord and

Santos (2012) use confidential data from the Shared National Credit program to track loan ownership. They find

little change in institutional ownership over the three years following origination.26 According to LPC, during our sample period, collateralized loan obligations (CLOs) provided roughly

two-thirds of the institutional funding, suggesting that a significant portion of institutional term loans made in this

period were securitized.


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measure, Banks as defined by GJL, is 2.7% higher in the out of court sample (29% = 2.7% / 9.3% at the

mean), but the difference is not statistically significant. The differences based on the other two measures

are much smaller in magnitude. Overall, the univariate evidence appears inconsistent with GJL’s finding

that reliance on bank loans (narrowly or broadly defined) is associated with a greater likelihood of out of

court restructuring.

To examine whether the number of bank lenders is related to the likelihood of restructuring, we

divide bank debt into two as solo bank debt and syndicated bank debt. We find that the average ratio of

solo bank loans to total liabilities is 3.2% higher in the out of court sample than in the bankruptcy sample

(4.7% vs. 1.5%, significantly different at the 1% level). This difference does not arise from differences in

the fraction of firms with solo bank loans. Instead, it arises from differences in the amount of solo bank

loans conditional on presence. Specifically, roughly one-third of firms in each group have some solo bank

loan, but conditional on presence, the average ratio of solo bank loan to total liabilities is 13.7% in the out

of court sample and only 4.7% in the bankruptcy sample (not tabulated). On the other hand, we find no

difference between the two groups of firms in terms of reliance on syndicated bank loans, suggesting that

reliance on diffusely held bank debt has no significant relationship with the probability of out of court

debt restructuring. Overall, since the importance of syndicated bank loans in public firm financing

increased over time and the importance of solo bank loans decreased, it is not surprising that we find total

bank debt matters less for the form of debt restructuring in our sample period than in the 1970s and the

1980s (GJL’s sample period).

Another important difference between the out of court and bankruptcy samples is the degree of

reliance on institutional loans. In particular, firms in the bankruptcy sample, on average, have

significantly more institutional loans relative to their liabilities than firms in the out of court sample (8.9%

vs. 5.6%, significantly different at the 5% level). When we decompose institutional loans into two as

securitized and unsecuritized, we find that all of this difference arises from differences in reliance on

securitized loans. The average ratio of securitized loans to liabilities is 8.1% in the bankruptcy sample and

only 2.6% in the out of court sample (the difference is significant at the 1% level). In un-tabulated results,

we find that 24% of firms in the bankruptcy sample and 15% of the firms in out of court sample have

securitized loans in their debt structure (the proportions are significantly different at the 5% level).

Conditional on having securitized loans, the average ratio of securitized loans to liabilities is 24.4% and

17.4% in the bankruptcy and out of court sample, respectively (the difference is significant at the 10%

level).

The greater frequency of bankruptcy filings among firms with securitized debt does not arise

from firms with securitized debt having poorer financial performance at the onset of distress. In

particular, as shown later in Panel B of Table 4, we find firms with securitized loans are more profitable


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and have higher earnings relative to interest expense than firms that rely on traditional bank loans for

funding. As we discuss later in the paper, the greater frequency of bankruptcy among firms with

securitized bank loans appears to result from greater holdout problems associated with restructuring

diffusely held securitized claims.27

Finally, we examine in Table 3 differences in debt concentration, using three alternative

measures: (1) number of debt contracts, (2) number of debt contracts scaled by total liabilities, and (3)

sum of squared weights of secured, unsecured, and subordinated debt (which we refer to as Herfindahl

index). GJL use the second measure. We find no difference in the mean and median values of the first two

measures across out of court and bankruptcy samples. However, firms in the out of court sample appear to

have a slightly more concentrated debt structure based on the third measure.

To summarize our findings in Table 3, we find no evidence that firms in the bankruptcy sample

are in substantially worse financial condition than firms in the out of court sample. We also find that firms

that rely more on solo bank loans and less on securitized institutional loans are more likely to restructure

their debt out of court rather than through formal bankruptcy. Finally, we find some evidence that

complexity of debt structure is positively related to the probability of bankruptcy.

Before turning to the multivariate analysis of the determinants of out of court restructuring, we

examine whether the covenant structure or seniority of solo and syndicated bank loans (as defined by

GJL) and institutional loans differ. Our examination is motivated in part by the theoretical literature on

syndicate structure that suggests that as syndicate size expands the lead arranger’s incentives to monitor

the borrower and enforce covenants decreases. The basic idea is that if the lead arranger ’s monitoring

efforts are not perfectly observable, then as the syndicate grows the lead arranger internalizes less of the

benefits of monitoring (see Sufi (2007)). Moreover, Dass, Nanda, and Wang (2012) argue that the

benefits of building or maintaining a lending relationship are likely to accrue primarily to the lead

arranger, leading the lead arranger to prefer renegotiation over strict adherence to the loan agreement. As

a result, they argue, large syndicated loans are more likely to contain financial covenants. In terms of

seniority, Asquith et al. (1994) and James (1996) argue that a bank ’s incentives to restructure a loan vary

with whether or not the loan is secured. Specifically, James (1996) argues that unimpaired bank lenders

have little incentive to scale down their claims or modify key terms of the loan agreement, since absolute

priority is generally maintained for secured lenders in bankruptcy and because they bear very little of the

27 These findings are consistent with those of Benmelech, Dlugosz, and Ivashina (2012) who find no

significant difference in the performance of securitized loans relative to other syndicated loans. Note that our focus

is on how debt is restructured conditional on distress and not on unconditional performance.


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costs of delay.28 Thus, secured lenders are only likely to modify key terms of the loan contract when they

are impaired and even then only when large unsecured creditors also restructure their claims.

Panel A of Table 4 provides descriptive statistics concerning number of financial covenants, the

proportion of the secured claims, and number of lenders by type of loan. As shown, we find that the

average number of financial covenants in both bank loans and institutional loans is 3.22. Also, the

proportion of secured loans (83.7% for bank loans and 87.7% for institutional loans) is not significantly

different across these two groups of loans ( p-value from a two-tailed test of equal proportions, assuming

unequal variances, is 0.294).29 The only significant difference between bank and institutional loans is in

the average number of lenders. In particular, while there are only 2.85 lenders in the average traditional

bank loan, there are 10.23 lenders in the average institutional loan (the difference is statistically

significant at the 1% level). We also compare the average number of financial covenants and the

proportion of secured claims in solo and syndicated bank loans, securitized and unsecuritized institutional

loans, as well as syndicated bank loans and securitized institutional loans and do not find significant

differences.30 Overall, these results suggest that the covenant structure or seniority of bank and

institutional loans are similar, but these two types of loans mainly differ in terms of number of lenders.

28 Secured creditors are generally paid in full in the bankruptcy if they are fully secured and the collateral is

not included borrower’s reorganization plan. If the collateral is included in the reorganization plan then fully secured

creditors typically receive a note from the reorganized entity secured by the same collateral. The terms of the note

(i.e., the interest, maturity, and the amortization schedule) may differ from the terms of the secured lender’s

prepetition note. If the secured loan is impaired as a result of collateral inadequacy, lenders will not be able to

recover interest that is due after the filing date under Section 506 of the Bankruptcy Code. See Taylor and Sansone

(2007).29 We obtain information on whether the loan is secured from CIQ. However, for loans classified as

unsecured, we also read debt footnotes in firm 10-K filings to verify the accuracy of the information in CIQ.

Overall, we found a significant number of mistakes in CIQ on loan security classifications, especially in the case of

sole lender bank loans. For example, we find that, of the 31 sole lender bank loans classified as unsecured by CIQ,

12 were in fact secured, one is recourse debt, and three became secured after an amendment (but before the onset of

restructuring). Among the remaining 15 loans, five were classified by CIQ as unsecured although there is no

information in the 10-Ks about whether those loans were secured. In addition, six of the unsecured bank loans were

issued by a firm with no secured debt. Overall, this analysis suggests that bank loans are not junior in the borrower’s

debt structure.30

Our primary source of information on number of financial covenants is Dealscan. When information on

covenants is missing in Dealscan, it is not clear whether this is because the loan does not have any financialcovenants (i.e., it is covenant-lite) or the information is just unavailable. Covenant-lite structures are generally

associated with institutional loans thus, for institutional loans the average number of financial covenants based on

available Dealscan data might be higher than the true average. However, assuming a covenant-lite structure when

there is no information on covenants in Dealscan might lead us to underestimate average number of covenants for

institutional loans. To address these issues, for institutional loans with missing covenant information, we hand-

collect this information from the loan contract attached as an exhibit to the borrower’s EDGAR fili ngs (firm 10-K

filings include an exhibit index that shows the filing date of all material contracts). Out of the 28 loans that we


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In Panel B of Table 4, we provide average firm characteristics by the presence of loan types at the

onset of restructuring. Not surprisingly, distressed firms that rely on securitized loans are on average

larger, more heavily levered, but also have higher earnings per dollar of debt than firms that rely on bank

borrowing. Firms that rely on securitized loans are also more likely to have rated or public debt

outstanding than firms that rely on traditional bank loans. Finally, firms that rely on traditional bank loans

have slightly more concentrated debt holdings but more debt contracts per dollar of liabilities (these

differences are not significant at the 10% level).

Given that reliance on various types of loans varies with firm characteristics such as size and

leverage, it is important to control for these factors as well as differences in the debt structure when

examining the relationship between the likelihood of an out of court restructurings and a firm’s debt

structure.

4. The likelihood of out of court restructurings and reliance on bank loans

We begin our analysis by estimating a logit regression with a specification similar to the one

reported in Table 7 of GJL. The dependent variable in the logit regression takes on the value of one if the

distressed firm successfully restructures its debt outside of bankruptcy; and zero otherwise. The first

column in Table 5 provides logit regression using the same controls as GJL (Tobin’s Q, the ratio of GJL

bank debt to total liabilities, and the number of debt contracts outstanding per dollar of liabilities) plus

additional firm-level controls such as firm size, cash holdings, the ratio of net power, plant, and

equipment to total assets (a measure of tangibility), leverage, and EBITDA to lagged assets (a measure of

profitability). We also include year and industry fixed effects. Using this broader set of controls, we find

results similar to those of GJL. In particular, we find a positive and statically significant relationship

between the likelihood of an out of court restructuring and a firm’s reliance on bank debt. Similar to GJL,

we also find a negative and significant relationship between the likelihood of an out of court restructuring

and the standardized number of debt contracts. These results suggest that creditor holdout problems are

less severe when more debt is owed to banks and when there are fewer creditors.

Our results differ from those of GJL in that we find no significant relationship between Tobin’s Q

and the likelihood of a successful out of court restructuring. This difference arises from our including in

the regression industry fixed effects and additional control variables. Specifically, if we estimate the logit

model using just the controls used by GJL (and exclude industry fixed effects) we find a positive and

examine only four were covenant-lite, suggesting that setting number of covenants to zero when Dealscan reports no

covenant information might lead researchers underestimate number of covenants.


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significant relationship between out of court restructurings and Tobin’s Q. Thus, industry effects and our

other controls likely capture the importance of intangibles.

In the regressions reported in columns 2 through 3 of Table 5, we expand our definition of bank

loans to include loans with participation by investments banks (column 2) and institutional lenders

(column 3). As shown, the point estimates and level of significance declines as we expand the definition

of bank loans. As shown in column 3, if we include institutional loans with traditional bank loans we fail

to find a significant relationship between out of court restructurings and reliance on loans.

Before turning to an analysis of the importance of syndicate size and securitized loans, a couple

of comments are in order. First, the specifications used in Table 5 examine how changes in reliance on

bank loans compared to reliance on all other liabilities (i.e., public and private debt as well as trade credit

and mortgage debt) is related to the likelihood of restructuring. The positive coefficient on narrowly

defined bank loans indicates that as the reliance on bank loans increases (and the reliance on other sources

of debt financing decreases) the likelihood of restructuring increases. However, the importance of holdout

problems as well as necessity of restructuring may vary across these other types of debt claims. We

address this issue in sub-section 4.2 below. Second, the relationship between the likelihood of out of court

restructurings and reliance on bank debt is robust to several alternative specifications (not reported for

brevity). For example, we obtain similar results when we include debt concentration (i.e., Herfindahl

index) and other firm-level variables reported in Table 3 in the regressions. In addition, we find no

significant difference between restructuring success and reliance on bank debt versus reliance on secured

bank debt. This latter result is probably due to the fact that the majority of all types of loans are secured

and when not secured, loans are senior to other unsecured creditors.

4.1. Does the number or identity of “ bank ” lenders matter?

As discussed earlier, previous empirical studies assume that the reason bank loans are easier to

restructure is because bank loans involve fewer and more sophisticated lenders. To evaluate the

reasonableness of this assumption, we collect information on whether or not a loan was syndicated and

the number of syndicate members. We first examine this issue using the GJL definition of bank loans. In

column 1 of Table 6, we distinguish between solo (non-syndicated loans) and syndicated loans. As

shown, we find a positive and significant relationship between out of court restructurings only for solo

bank loans. In contrast we find no significant relationship between the out of court restructurings and

reliance on syndicated loans (the coefficient on the solo bank variable is significantly different from the

coefficient on the syndicated bank variable at the 1% level). One concern with this specification is that

solo bank loans may differ from syndicated loans because the type of loan differs between the two types

of lenders. To address this potential concern, we decompose solo bank loans into term loans and


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revolvers. As shown in column 2, we find that the positive relationship between reliance on solo loans

and restructuring success arises mainly from reliance on solo term loans.

To evaluate the importance of syndicate size, in column 3, we include an interaction of the

syndicated bank variable with an indicator variable that is equal to one if the average number of lenders

(weighted by loan size) in the syndicated loans of the firm is above four (median number of lenders in all

syndicated bank loans of our sample firms). As shown, while the point estimate for the interaction term is

negative, the coefficient is not statistically significant. We experimented with alternative ways to measure

syndicate size. For example, we estimated the logit regression including reliance on small versus large

syndicates as two separate variables. We also estimated a regression separately including amount

borrowed from syndicates with two, three, four, and five or more lenders. Moreover, we estimated

specifications including the interaction of the bank syndicate variable with the number of syndicate

members, the log of the number of syndicate members, and a specification which included the square of

the number of syndicate members. For all these specifications, we find that the syndicate size measure is

not significantly related to the likelihood of restructuring.31 These findings suggest that coordination

problems increase when firms rely on more than one bank lender but conditional on using multiple

lenders the syndicate size is unrelated to the likelihood of restructuring.

We next turn to an analysis of the relationship between out of court restructurings and

involvement of institutional lenders in the loan syndicate. In column 4, we report the logit regression

estimates in which we distinguish between traditional bank lenders (GJL banks) and institutional lenders.

As shown, while reliance on bank lenders is positively related to the likelihood of an out of court

restructuring, reliance on institutional lenders is negatively related to the likelihood of restructuring out of

court. As shown in column 5, the relationship between out of court restructuring and reliance on

institutional loans is significantly different from both solo bank and syndicated bank loans (using an Chi-

squared test, we can reject the hypothesis that the coefficient estimate on institutional loans is equal to the

coefficient on either solo or syndicated bank loans at the 5% level).

To further examine the role of institutional lenders in the debt restructuring process, we divide

institutional loans into loans that are likely to be securitized (i.e., held by CLOs) and institutional loans

that are unlikely to be securitized. We make this division based on the whether the facility is a Term B

loan and whether the loan was arranged by one of the top 10 CLO sponsors listed in listed in Nadauld

and Weisbach (2012). We also include in the securitized group Term A and revolver facilities in which

31 Unfortunately, we are unable to determine the importance of ownership concentration for most of our

sample loans, since information on loan shares of the lead arranger and other participants is unavailable from

Dealscan for a substantial portion of the loans in our sample.


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one of the participants was a CLO. As shown in column 6 of Table 5, the negative relationship between

the likelihood of restructuring and reliance on institutional loans arises because reliance on securitized

loans is negatively related to restructuring success. Indeed, we find no significant difference between the

restructuring success and reliance on syndicated bank loans or unsecuritized institutional loans ( p-value =

0.76).

4.2. Are bank loans different from public and private debt claims?

Practitioners contend that trade credit is more difficult to restructure than other liabilities of

financially distressed firms. The reason is that trade credit tends to be diffusely held. Also, trade claims

tend to be more heterogeneous than privately placed or public debt. GJL argue that private restructurings

of trade credit are more difficult because trade creditors tend to be ‘acrimonious’ and ‘unsophisticated’.

To investigate this issue we estimate the logit regression with trade credit as the omitted category of

liabilities. As discussed in the data appendix, we construct for each firm in our sample measures of

reliance on various types of bank loans, as well as public debt, private placements, mortgage and

equipment debt as well as other non-trade credit related debt. This specification allows us to compare

reliance on various types of debt to what is arguably the most difficult set of claims to restructure, trade

credit. Panel A of Table 7 presents the logit estimates and panel B provides p-values associated with Chi-

squared tests of the equality of coefficient estimates.

As shown, we find a positive and significant relationship between the likelihood of out of court

restructurings and reliance on bank loans (both solo and syndicated loans), unsecuritized institutional

loans, as well public and privately placed nonbank debt. Overall, these results are consistent with the

argument that trade credit is particularly difficult to restructure relative to traditional bank debt as well

publicly traded debt and most forms of private debt, with the exception of securitized institutional loans.

Indeed, the results in Table 6 indicate that reliance on securitized loans affects the likelihood of out of

court restructurings in a manner similar to trade credit.

In Panel B, we provide the results of Chi-squared tests of the equality of regression coefficients.

Overall, these tests indicate that reliance on solo bank lenders significantly increases the likelihood of

restructurings relative to syndicated loans, institutional loans (both securitized and unsecuritized) as well

as well as public debt claims. Interestingly, we cannot reject the hypothesis that reliance on syndicate

loans and public debt impact the likelihood of restructuring in a similar manner. In contrast, reliance on

securitized loans appears to reduce the likelihood of restructuring relative to syndicate loans. Overall,

these results suggest that the severity of holdout problems increases when loans are securitized.


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4.3. Endogeneity concerns

A concern with the analysis so far is that a firm’s reliance on the various types of loans is

endogenous. While our loan variables and financial controls are measured prior to the onset of distress

(thus mitigating simultaneity concerns), concern with endogeneity arising from omitted variables remains.

Specifically, there are potentially other firm-specific factors that affect the likelihood of an out of court

restructuring that are also correlated with a firm’s reliance on GJL bank borrowing or institutional loans.

To examine the robustness of our findings and to partially address endogeneity concerns, we

estimate instrumental variables (IV) regressions (not tabulated). In our analysis of endogeneity, we

estimate linear probability models because the properties of two stage estimators for linear models are

well understood.32

Finding instruments that meet both the relevance and exclusion criteria for each of the categories

of loans in Table 6 is a challenge. Thus, we focus on the original GJL specification (column 1 in Table 5)

and the specification that includes reliance of GJL bank loans and institutional loans (column 4 in Table

6). For reliance on bank loans, as instrument we use a measure of time-series variation in overall bank

lending standards, specifically the net percentage of commercial banks tightening lending standards in

medium and large commercial and industrial loans according to Federal Reserve’s Survey of Terms of

Business Lending. We calculate this instrument using the four quarter average value of the change in

lending standards during the last fiscal year before the year of distress. For institutional loans, we use

annual changes in the volume of asset-backed securities (ABS) and CLOs as well as presence of rated

outstanding debt as instruments. Shivdasani and Wang (2011) and Nini (2012) use securitization activity

as instruments for institutional loan spreads because they argue these factors reflect shifts in the supply of

institutional loans. We include whether the firm has rated loans or debt outstanding prior to the onset of

distress as an instrument since Naudaud and Weisbach find that CLO purchase activity focused primarily

on loans to firms with rated debt.

We begin by estimating a first stage regression for reliance on GJL bank loans and our

instruments as well as all of the explanatory variables used in the earlier logit regressions. Next, we test

for exogeneity using the Wu-Hausman test of endogeneity (see Wu (1973) and Hausman (1978)). We find

that the coefficient estimate on the residual from the first stage regression is not significantly different

from zero. Assuming our instruments are valid, we cannot reject the hypothesis that the GJL bank loan

variable is exogenous. In contrast, when we estimate a first stage regression for reliance on institutional

loans and then include the residuals in a second stage regression, we find the coefficient on the residual

32 See Angrist and Pischke (2009) chapter 4.


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for institutional loans is positive and significant (at the 1% level), suggesting reliance on institutional

loans may be endogenous.33

We next estimate IV models instrumenting for institutional loans using two stage least squares.

Consistent with our earlier results, we find a negative and statistically significant relationship between the

likelihood of an out of court restructuring and reliance on institutional loans (the coefficient estimate is

-1.061 and significant at the 1% level). The coefficient estimate on the GJL bank loan variable is positive

and marginally significant (the t -statistic is 1.70). Moreover, we can reject the hypothesis that reliance on

GJL bank loans affects the likelihood in the same way reliance on institutional loans. In particular, the

coefficient on instrument for institutional loans is significantly less than the coefficient on GJL bank loans

(at the 1% level). If endogeneity is an important concern, then these results suggest that the identity of

distressed firm’s lenders matters.

4.4. The nature of out of court restructurings

The results in Tables 5 through 7 indicate that the likelihood of an out of court restructuring is

related to the type of loans the distressed firms relies on. We also examine the nature of the out of court

restructurings, in terms of types of debt that are restructured and the form of the restructuring. Our

findings are reported in Table 8. It is perhaps not surprising, given public debt is long term and generally

junior to bank and institutional loans, that public debt is the most frequently restructured claim in our

sample. Moreover, conditional on having public debt, roughly 80% of the firms in our sample restructure

their public debt. When public debt is restructured the restructuring typically involves a reduction of

principal and in most cases the exchange of some debt for an equity claim. Cases in which public debt is

outstanding but not restructured and banks restructure their claims are rare. Most these cases involve bank

loan amendments in which the maturity of the loan is extended while some other contract terms are

tightened (7 cases). In the other cases (8) junior nonpublic claims were restructured (along with bank

loans).

As shown in Table 8, the most common form of loan restructuring involves the extension of the

maturity. While solo bank loan restructurings more frequently involve the reduction of principal than the

restructuring of other types of loans, the differences in frequencies are not statistically significant. Finally,

notice that the frequency of equity-for-debt exchanges is similar for bank and institutional loans (30.6%

and 27.8%, respectively). This finding suggests that it is the fact that term loans are senior and not

regulatory restrictions on bank ownership of equity that explains the infrequent use of equity-for-debt

33 The instruments for both GJL bank loans (lending standards) and institutional loans (CLO and ABS

activity and rating), pass the relevance criteria (the F-statistics for the instruments are 53 and 14 respectively).


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exchanges in loan restructurings.34 Overall, while we find that type of loan matters in terms of the

likelihood of a successful restructuring, we do not find significant differences in how the various types of

loans are restructured.

In the next section, we examine the relationship between the nature of the out of court

restructuring as well the type of bankruptcy (prepackaged versus traditional Chapter 11) and the

distressed firm’s reliance on the various types of loans. However, before addressing those issues, we

examine whether the results reported in Table 6 are sensitive to the whether institutional loans are funded

by hedge funds or private equity groups. This analysis is motivated by two recent papers. The first paper

is by Jiang, Li, and Wang (2012) who examine the influence of hedge funds in Chapter 11. The second

paper is by Hotchkiss, Smith, and Stromberg (2012) who investigate the role of private equity in the

resolution of financial distress. The first paper finds that the presence of hedge fund investors as

unsecured creditors is associated with a higher probability of emerging from bankruptcy, greater CEO

turnover while the firm is in bankruptcy, and higher payoffs to junior creditor upon the firm’s emergence

from bankruptcy. Overall, Jiang et al. interpret these findings as evidence that hedge funds balance the

power between the debtor and secured creditors thus leading to restructurings that are more “management

neutral” than when hedge funds do not participate. While Jiang et al. examine the influence of hedge

funds on the outcomes of bankruptcy; the expected outcome of bankruptcy may affect the incentives of

creditors to negotiate outside of bankruptcy. The Hotchkiss et al. paper examines the influence of private

equity firms on the restructuring process in financial distress. They find that, conditional on default, PE-

backed firms are more likely to restructure out of court than non-PE backed firms. Unlike our sample of

distressed public firms, Hotchkiss et al.’s sample consists primarily of private and not solely public firms

and therefore involvement of private equity firms in the restructuring process is likely to be much greater

for firms in their sample.

To examine the influence of hedge funds and private equity groups as investors in institutional

loans, we create two dummy variables for hedge fund and private equity fund involvement as investors in

the distressed firm’s institutional loans and then include an interaction of these dummy variables with

reliance on institutional debt. In our sample, there are 17 (27) firms with an institutional loan that includes

one or more hedge (private equity) funds as lenders. Slightly more than 75% of loans with hedge fund

participation were securitized, whereas only 19% of loans with private equity participation were

securitized.

34 See James (1995) for a discussion of the regulatory restrictions on banks taking equity in troubled debt

restructurings.


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As shown in Table 9, the interaction terms are insignificant. Also, including the interaction terms

does not affect the sign or significance of reliance on institutional loans (column 1) or securitized loans

(column 2). Overall, the evidence suggests that involvement of hedge funds and private equity groups

does not have a significant effect on the difficulty of restructuring securitized loans out of court.

5. Bankruptcy characteristics and the reliance on bank loans

The findings reported in section 4 suggest that holdout problems are more severe when firms rely

more on syndicated loans and particularly securitized loans than on loans from a single bank lender.

However, as Gilson (2012) points out, some distressed firms (or their creditors) may prefer bankruptcy

over out of court restructurings even in the absence of obstacles, such as holdout problems. For example,

there may be tax benefits associated with cancelling debt obligations in bankruptcy. In addition, firms

may file for bankruptcy to obtain debtor in possession (DIP) financing. As Gilson (2012) and Dahiya,

John, Puri and Ramirez (2003) explain, DIP financing is a major source of funding for bankrupt firms.

Moreover, because DIP loans are senior claims (ranking pari-pasu with or just below pre-petition secured

claims), they provide a way of mitigating under-investment problems associated with a debt overhang.

Since secured lenders are likely to be reluctant to share collateral with new lenders in an out of court

restructuring, cash-constrained firms that rely heavily on senior bank debt may prefer bankruptcy over an

out of court restructuring.

To investigate these questions, we obtain information on characteristics of the bankruptcies for

the firms in our sample from a number of sources including LoPucki’s Bankruptcy Research Database,

Bankruptcydata.com, MBRD, CIQ, and firms’ SEC filings. Using these sources, we collect information

on whether the bankruptcy was prepackaged or pre-negotiated (which we refer to collectively as

prepacks), whether the court approved a DIP financing, the duration of the bankruptcy (in months),

whether the firm emerged from bankruptcy as an independent firm, whether the firm liquidated or is in

the process of liquidating the majority of its assets, whether the firm was acquired while in bankruptcy,

and for bankruptcies not still pending at the end of 2012, recovery rates for all creditors from Moody’s.

Panel A of Table 10 provides descriptive statistics for the bankruptcies in our sample. We set

months in bankruptcy to missing for four pending and four dismissed bankruptcies as well as six ongoing

bankruptcy liquidations as of this writing. Also, Moody’s reports recovery rates for only 60% or 102 of

our sample firms. As shown, just over a third of the bankruptcies in our sample are prepacks (see also

Table 1). As discussed earlier, we find no change over our sample period in the fr

bank' loan ownership and troubled debt restructurings

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