In- and Out-of-Court Debt Restructuring in the Presence
of Credit Default Swaps ∗
Mascia Bedendo†
Lara Cathcart‡
Universita’ Bocconi
Imperial College Business School
Lina El-Jahel§
Imperial College Business School
∗
We thank Yakov Amihud, Mark Flannery, Vidhan Goyal, Issam Hallak, Per Strömberg, Josef Zechner, and seminar participants at Bocconi University, Queen Mary University, and the FIRS 2011 Sydney meeting for suggestions
and comments. We acknowledge financial support from Carefin (Bocconi Centre for Applied Research in Finance).
† Corresponding author. Address: Department of Finance, Universita’ Bocconi. Via G. Röntgen, 1 - 20136 Milan
(Italy). E-mail: mascia.bedendo@unibocconi.it. Ph. +39 0258365973, fax +39 0258362970.
‡ Address:
Imperial College London, South Kensington Campus, London SW7 2AZ, U.K. E-mail:
l.cathcart@imperial.ac.uk. Ph. +44 2075949126.
§ Address: Imperial College London, South Kensington Campus, London SW7 2AZ, U.K. E-mail: l.eljahel@imperial.ac.uk. Ph. +44 2075949177.
ABSTRACT
This paper empirically investigates whether the availability of credit insurance via credit
default swaps (CDS) affects the debt restructuring process of distressed firms, as predicted
by the empty creditors theory. Looking at the restructuring outcome (distressed exchanges
versus Chapter 11 filings) of a sample of rated, non-financial U.S. companies over the period
2007-2011, we do not find evidence that the access to credit insurance favors bankruptcy over
a debt workout. Instead, we observe that the probability of filing for Chapter 11 is significantly associated with high leverage and short-term refinancing needs, and a simplified debt
structure characterized by a high proportion of secured debt. Our results do not change once
we specifically account for the liquidity of the CDS traded on the firm’s debt, and are robust
to the inclusion of instrumental variables. The analysis of the impact of CDS availability on
recovery rates from distresses exchanges and bankruptcies also fails to uncover any evidence
of distortive pressure from insured bondholders on the restructuring process.
JEL Classification: E51, G32, G33.
Keywords: Credit default swaps, empty creditors, debt restructuring.
1. Introduction
What determines a distressed firm’s choice between in- and out-of-court debt renegotiation? A
large body of theoretical and empirical research, thoroughly reviewed in the survey by Hotchkiss,
John, Mooradian and Thorburn (2008), has addressed this question. Direct bankruptcy costs, financial leverage, short-term liquidity, and debt structure are widely acknowledged as key determinants
of the outcome of the restructuring process. Although these findings are well consolidated, the explosive growth of the credit insurance market in the last decade, mainly in the form of credit default
swaps (CDS), has renewed interest in the topic. The availability of credit insurance via CDS has
been closely associated with the emergence of empty creditors, i.e. insured debtholders who may
find it convenient to force distressed firms into bankruptcy, even when a debt workout would be
the most efficient choice. In this paper we revisit the initial question from this perspective, and
empirically investigate whether the presence of CDS has played a significant role in determining the outcome of the restructuring process for a number of reference entities during the period
2007-2011.
In a CDS contract the protection buyer pays a periodic premium to the protection seller, and
receives a settlement equal to the difference between the par and market value of the underlying debt of the reference entity, should a default event occur. Typical default events include the
bankruptcy of the debtor or the failure to pay principal / interests on the debt. Out-of-court debt
renegotiations, instead, formally constitute credit events only under particular CDS restructuring
clauses.1 In practice, no workout in the U.S. corporate segment has ever triggered a default in the
CDS market, given the general disagreement about what constitutes a restructuring event.2
In this context, legal scholars Hu and Black (2008a, 2008b) advance the empty creditor hypothesis, according to which insured creditors of a financially distressed firm have a strong incentive
to exercise their voting rights to favor bankruptcy over a debt workout, as long as the CDS payoff
1 Modified
restructuring (Mod-R) for U.S. contracts, and modified-modified restructuring (Mod-Mod-R) for European contracts.
2
The Small Bang protocol released by ISDA in July 2009 is the first document to detail the auction settlement
following a restructuring event.
2
in bankruptcy is larger than the post-renegotiation value of debt. Along the same line, Yavorsky,
Bauer, Gates and Marshella (2009) explain why bondholders with CDS protection and, more in
general, investors with negative basis trades, are more likely to take a “hard-line” in debt renegotiations with the issuer. In a theoretical framework, Bolton and Oehmke (2011) develop a model
where the presence of CDS can yield a market equilibrium characterized by overinsurance of bondholders, and an inefficiently high incidence of bankruptcy filings compared to the optimum.
The concerns regarding the distortive effects of CDS on debt restructuring may, however, be
excessive. First of all, the proportion of insured bondholders must be sufficiently large in order
to affect, in practice, the outcome of the restructuring process. In this respect, a recent theoretical
model developed by Campello and Matta (2011) predicts that, in recession, overinsurance affects
only the survival of firms with low-continuation values, while no overinsurance is associated with
firms with high continuation values. Second, insured creditors may decide to support an outof-court renegotiation due to reputational concerns, or to the uncertainty related to the outcome
of the bankruptcy process. Ultimately, whether the presence of CDS significantly affects debt
restructuring remains, to a large extent, an empirical question.
To our knowledge, no rigorous empirical investigation of the role played by the availability
of credit insurance in determining the restructuring choice exists, and this study represents a first
contribution in this direction. In what follows, we provide a detailed analysis of the restructuring
process in both CDS reference entities and non-reference entities, and of the extent to which the
process in the former is driven by company’s fundamentals, in line with what we observe in the
latter. Instead, we do not provide a direct empirical verification of the empty creditor hypothesis,
which is unfeasible, as it would require data on the proportion of the firm’s unsecured debt which
is insured through CDS, and such data are not available.3
Our sample consists of all bankruptcy filings and out-of-court renegotiations initiated by nonfinancial U.S. rated companies over the period January 2007 - June 2011. This period represents
3 Information on
the 1000 most liquid single name CDS volumes outstanding has been provided by the Depository
Trust and Clearing Corporation (DTCC) since November 2008, but the breakdown of the CDS volumes between
covered and naked CDS positions is not available.
3
a good laboratory for looking at distressed firms’ debt restructuring, as it includes the first generalized crisis since CDS contracts have become liquid. In assessing the role of CDS on the restructuring process, we need to control for a set of variables that former literature has found to be
significant in explaining the restructuring outcome of U.S. firms, namely operating performance,
leverage, short-term refinancing needs, and debt structure.
We conduct both a univariate analysis to highlight any differences in the determinants of the
restructuring method between CDS reference entities and non-reference entities, and a probit regression to test whether the availability of credit insurance has increased the probability of filing
for bankruptcy for reference entities. We report three main results. First, we do not find evidence
that companies whose bondholders can purchase insurance via CDS are more likely to restructure their debt in court. In fact, the outcome of the debt renegotiation process seems to be driven
by essentially the same variables in both reference entities and non-reference entities. Second, in
agreement with previous literature on debt restructuring, we observe that firms that file for Chapter 11 report higher leverage, higher short-term debt ratios, and a larger proportion of secured
debt than companies that reorganize out of court. Third, in contrast with earlier research, we find
bankrupt firms in our sample to be characterized, on average, by a more simplified debt structure
than firms that successfully complete a debt workout. Additionally, we document a significant increase in the proportion of out-of-court renegotiations following the introduction of Recovery Act
which provided significant tax relief on distressed exchanges completed in 2009-2010.
We perform further tests of the effect of credit insurance on debt restructuring by looking at the
recovery rates of the defaults in the sample. Even though the presence of CDS does not seem to
drive the restructuring outcome, insured bondholders may still condition the restructuring process
to some extent. If this is the case, we would expect to observe higher recovery rates for reference
entities in both in- and out-of-court debt renegotiations. In our sample, however, the availability
of credit insurance does not help explain the recovery prices of either distressed exchanges or
bankruptcies, once the standard determinants of recovery values have been taken into account.
As a final check, we investigate whether the presence of empty creditors may undermine the
4
debt restructuring attempts of a company at an earlier stage than the actual default. If a significant proportion of debtholders are insured via CDS, they may be reluctant to renegotiate the debt
following the first signs of financial distress, thus pushing the company into further trouble. All
else equal, this would translate into a higher proportion of defaults (either distressed exchanges or
bankruptcy filings) among reference entities. Again, we find no evidence supporting such claims
in our sample.
Our findings do not change once we specifically account for the liquidity of the CDS traded
on the firm’s debt, which is proxied by the inclusion in the CDX.NA.HY index, as well as the
availability of outstanding CDS volumes. Further, they are robust to the inclusion of instrumental
variables in an attempt to control for a potential omitted variable bias.
Our paper contributes to the existing research in several ways. Our findings on the impact of
CDS on the debt renegotiation process of reference entities directly enrich the fast-growing literature on empty creditors, which lacks empirical evidence in this respect. Some anecdotal evidence
in favor of the empty creditor hypothesis has been produced by Hu and Black (2008a, 2008b),
Bolton and Oehmke (2011), and the financial press (Risk (2009), Economist (2009)). On the contrary, Mengle (2009) provides a preliminary analysis of the empty creditor issue and questions its
validity due to the lack of compelling evidence. His conclusions, however, are based solely on the
dynamics of the ratio of distressed exchanges to bankruptcies during both the 2007-2009 crisis and
earlier pre-CDS crises, without accounting for other factors. Two recent papers by Subrahmanyam,
Tang and Wang (2011), Peristiani and Savino (2011) investigate, from an ex-ante perspective, the
effect of the introduction of CDS on the probability of bankruptcy of firms, estimated through a
structural approach or credit rating changes. The authors document an increase in the probability
of bankruptcy of CDS reference entities and take this finding as evidence supporting the empty
creditor theory. We, instead, look at the impact of CDS from an ex-post perspective, and assess
whether, once a firm is in distress, the availability of credit insurance distorts the debt restructuring
process by favoring bankruptcy over an out-of-court renegotiation. We believe our approach to be
more in line with the empty creditor issue, as the presence of insured creditors should ultimately
translate into inefficient debt restructuring choices in distressed firms. Further, our analysis has
5
the advantage of avoiding the endogeneity issues that may arise around the introduction of CDS
trading.
We also add to the empirical literature on the costs and benefits of CDS for firms, which is still
in its infancy, as highlighted by Stulz (2010). In this respect, notable contributions on the effects of
CDS on the credit market are by Saretto and Tookes (2011) who document an increase in leverage
and debt maturity following the introduction of CDS on a company’s debt, by Hirtle (2009), who
shows that the presence of CDS yields an increase in bank credit supply and an improvement in
credit terms, and by Ashcraft and Santos (2009) who suggest that the introduction of CDS may
reduce the cost of borrowing for safe and transparent firms.
At a broader level, we contribute to the more established literature on the determinants of inand out-of-court debt restructuring (see Gilson, John and Lang (1990), Franks and Torous (1994),
Asquith, Gertner and Scharfstein (1994), Chatterjee, Dhillon and Ramı́rez (1996), Brunner and
Krahnen (2008), Jostarndt and Sautner (2010)) by revisiting the role played by classic determinants
of the restructuring process during the 2007-2009 financial crisis.
Our research has also interesting policy implications in terms of regulation of the CDS market.
Various measures have been suggested to limit the distortions introduced by CDS on the debt
renegotiation process, such as a revision of the bankruptcy law, or the disclosure of CDS positions
held by bondholders. Our results indicate that a change in the bankruptcy law would probably
be excessive, given the lack of evidence, at an aggregate level, in favor of the empty creditors
theory. However, more transparency on who holds the CDS positions is essential for a deeper
interpretation of our findings. This is also in line with the more general recommendations of the
Dodd-Frank act regarding disclosure requirements for the CDS market.
The rest of this paper is organized as follows. Section 2 describes data and sample collection.
In Section 3 we review the determinants of distressed exchange and bankruptcy. We discuss the
methodological steps and the main findings in Section 4. In Section 5 we present further evidence
and perform some robustness checks. Section 6 concludes.
6
2. Data and Sample Selection
To investigate whether the availability of insurance on a company’s debt affects the debt restructuring method in case of distress, we first need to identify a full set of in- and out-of-court debt renegotiations undertaken by financially distressed firms. Our initial sample includes all bankruptcy
filings (under either Chapter 7 or Chapter 11) and distressed exchanges completed by U.S. rated
companies over the period January 2007 - June 2011, as reported by Moody’s Default and Recovery Database. Bankruptcy filings include prepackaged Chapter 11 cases, which constitute regular
credit events in CDS contracts.
According to Moody’s (2000), for a debt restructuring to be classified as distressed exchange,
two conditions must be satisfied: (i) the issuer offers creditors a new or restructured debt, or a
new package of securities, cash or assets, that amount to a diminished financial obligation relative
to the original obligation, and (ii) the exchange has the effect of allowing the issuer to avoid a
bankruptcy or payment default. In this respect, distressed exchanges should represent the out-ofcourt alternative to Chapter 11 filings for the purpose of debt reorganization.
The definition of distressed exchange excludes, in principle, debt exchanges arranged by financially healthy issuers to reduce their debt levels at attractive market conditions. Following
Mooradian and Ryan (2005), we assess whether this is also the case in practice through a detailed
search for news on the individual debt exchanges in Factiva. The search confirms that all events
classified as distressed exchanges actually refer to financially distressed firms, and are therefore
relevant to our analysis.4 Further, to ensure that all the substantial debt renegotiations carried out
by Moody’s rated firms in the period are included in our sample, we adopt an approach similar
to Franks and Torous (1994): For all companies with corporate rating of Caa1 or lower in the selected period, we run a search in Factiva to determine whether those firms restructure their debt,
informally by reaching an agreement with the creditors, or formally through Chapter 11 or Chapter
7. Our search returns 24 additional debt renegotiations, mainly completed by firms no longer cov4
As a further check, we compare mean and median values of interest coverage ratio and EBITDA-to-total assets
ratio at year-end before default for both distressed exchanges and bankruptcy filings, and we find them not to be
significantly different at 5% confidence level.
7
ered by Moody’s following a rating withdrawn. It is worth noticing that, at this point, our sample
includes comparable restructuring methods only, i.e. bankruptcy filings and out-of-court renegotiations that are aimed at avoiding bankruptcy. Instead, we ignore less severe reorganization measures
undertaken to prevent the firm from sliding into further distress. This issue will be investigated in
detail in Section 5.3.5
We apply a first filter to discard from the sample all debt exchanges followed by a bankruptcy
filing within six months. Multiple distressed exchanges that occur during the same six-month period are instead combined together. We limit our sample to rated companies to ensure a more
meaningful comparison between CDS reference entities, which are always rated given that a significant portion of their debt is publicly traded, and other firms in the sample.
The choice of the sample period is dictated by two considerations. First, the CDS market has
been very liquid over the last few years only, and the proportion of corporate defaults where the
obligor is a CDS reference entity was negligible prior to 2005. Second, the defaults recorded during the 2007-2009 crisis are characterized by an unusually large recourse to distressed exchanges
(Altman and Karlin (2009)), comparable to the debt workouts boom observed in the 1980s. This
can be explained with: (i) the generalized shortage of liquidity and, consequently, of debtor-inpossession (DIP) and exit financing, which has increased the uncertainty linked to bankruptcy
filings; (ii) the approval, in February 2009, of the American Recovery and Reinvestment Act (“The
Recovery Act”), which allows the deferral of the income arising from debt cancellations arranged
in 2009 and 2010 until 2014, and its amortization over the following five-year period. Given that
out-of-court debt renegotiations accounted only for 9% of the total number of corporate defaults
over the years 2000-2006, we restrict our sample period to 2007-2011 for consistency. The overall
number of in- and out-of-court restructurings for U.S. rated firms over this period is equal to 285.
5 In
a recent paper, Hotchkiss, Smith and Strömberg (2011) adopt similar sample selection criteria to investigate the
role of private equity firms in the debt restructuring of distressed companies. Some previous empirical studies on the
determinants of the default method (Gilson, John and Lang (1990), Asquith, Gertner and Scharfstein (1994)) follow
a different approach in selecting the sample. First, they identify all financially distressed companies by looking at
their stock performance over a certain period. Then, they investigate the actions taken by those companies in response
to distress. However, we opt for an alternative method in order to retain both public and private companies in our
analysis.
8
Previous literature has shown that the composition of capital and debt structure plays a major
role in explaining the outcome of the debt restructuring process. Detailed information on these
variables is available from the annual 10-K filings, hence we exclude those firms which did not
file a 10-K report at the end of the fiscal year prior to the default event.6 Further, we eliminate
companies that underwent major reorganizations (M&A, spin-offs) between the last 10-K report
date and default. We also drop all financial companies, given the peculiarities of their capital
structure. Our final sample includes a total of 163 default events, 40% of which are distressed
exchanges, and 60% are Chapter 11 filings.7 The sample size is small, albeit comparable to the
size of existing related empirical studies (Gilson, John and Lang (1990), Asquith, Gertner and
Scharfstein (1994), Franks and Torous (1994)).
Finally, we need to identify the default events pertaining to CDS reference entities. For this
purpose, we match the firms in our sample with the list of CDS reference entities provided by
MarkIt. We identify 69 matches, which account for 42% of all defaults, thus ensuring a meaningful
comparison between reference entities and other firms. 36% of those reference entities are part of
the CDX North America High Yield (CDX.NA.HY) index in the year before default and, therefore,
have actively traded CDS on their debt, while in the remaining cases the CDS is more illiquid, as
confirmed by the stale spreads recorded by MarkIt in the weeks before the default event.
The composition of our sample of in- and out-of-court restructurings is reported in Table 1.
Most default events (61% of the total) occur in 2009, at the peak of the economic crisis. For the
reasons outlined above, distressed exchanges become more popular in 2009 and 2010, when they
account for 45% and 35% of the default events, respectively.
The industry classification of the defaulted companies reveals that the sample is distributed
across all sectors, with a concentration in manufacturing industries, which account for 50% of all
6 The
average number of days between the last report date and the default event is 182 for distressed exchanges
and 184 for Chapter 11 filings. Using the last available 10-Q quarterly report prior to default would provide a more
accurate picture of the financial and economic conditions of the distressed firm, but is unfeasible due to the lack of
details on the debt structure for most 10-Q filings.
7 There are no Chapter 7 filings in our final sample. Hence, in the rest of the paper, we will use “bankruptcy” and
“Chapter 11” interchangeably.
9
defaults.8 Interestingly, around 55% of the default events are associated with public firms, while
the remaining 45% refer to privately held firms with public debt, most of which had been taken
private during the leveraged buyout boom of 2004-2007. In this respect, we observe a higher
(although not statistically different) proportion of distressed exchanges in private companies than
in public ones.
INSERT TABLE 1 HERE
Surprisingly, we find that the incidence of in- and out-of-court restructurings in CDS reference
entities is almost identical to the one observed in other firms: 61% of all defaults of reference
entities are represented by bankruptcy filings, against 60% for other companies. This seems to
contradict the claim that reference entities are more likely to file for Chapter 11 under the pressure
of insured creditors. The frequency of bankruptcies among reference entities with very liquid CDS
is even lower (52%), partly due to the fact that around 85% of the defaults of these firms occur
in 2009-2010 (when distressed exchanges are relatively more frequent, as discussed), against a
corresponding percentage of 60% for reference entities with less liquid CDS.
One may argue that our final sample of defaults might have been biased by the filtering procedure and, as a result, may not be representative of the entire universe of the default events prescreening. We find this not to be the case, as the proportions of distressed exchanges and Chapter
11 filings for the default events excluded from our sample are very similar (39% and 61%, respectively) to the corresponding proportions in-sample (40% and 60%, respectively). In fact, the
defaults of the reference entities included in the sample seem to be biased towards bankruptcy, as
61% correspond to Chapter 11 filings, against only 54% for the reference entities excluded from
the sample.9
8
Similar findings were reported by Chatterjee, Dhillon and Ramı́rez (1996).
is worth noticing that two thirds of the reference entities not included in the sample are financial companies
with widely traded CDS, most of which benefited from government aid during the crisis.
9 It
10
3. The Determinants of the Restructuring Method
In assessing whether distressed reference entities are more prone to bankruptcy than other firms, we
need to control for a set of variables that former literature has found to be significant determinants
of the outcome of the restructuring process, namely operating performance, leverage, and debt
structure.10
Data on the values of those variables at the fiscal year-end prior to default are collected manually from the 10-K filings available from the SEC’s EDGAR database. Specifically, data on
operating income and financial leverage are obtained from the consolidated financial statements,
while the debt structure is detailed in the notes to the financial statements.
3.1. Financial and Economic Distress
The degrees of financial and economic distress have been included in most empirical analyses of
the determinants of the restructuring method. However, while high indebtedness and poor performance are essential ingredients for pushing a company into distress, it is not clear whether and how
they should play a role in determining the restructuring outcome. We can expect highly-leveraged
companies with a large proportion of debt due in the short run to face time constraints in attempting
to reorganize their debt out of court, which can lead to a higher chance of filing for bankruptcy. In
addition, we can foresee this effect to be particularly strong during times of generalized liquidity
shortage in the economy. The existing empirical findings on the link between restructuring choices
and financial / economic variables are mixed. Chatterjee, Dhillon and Ramı́rez (1996) document
a lower operating performance and a lower leverage for companies that renegotiate in court, as
well as a higher proportion of short-term debt, which is viewed as a proxy of the firm’s liquidity
needs. Gilson, John and Lang (1990), Franks and Torous (1994), Asquith, Gertner and Scharfstein
(1994) also observe more stringent liquidity needs in firms that file for bankruptcy, whose levels of
leverage and operating performance, however, are in line with those of firms that complete a debt
10 We
have not included other variables, such as direct bankruptcy costs or market-based solvency measures, due to
data unavailability for the firms in our sample.
11
workout.
We measure firm leverage with the ratio of total debt to total assets, and operating performance
with the ratio of EBITDA to total assets. To correct for industry-specific effects on capital structure
and operating performance, we also compute industry-adjusted indicators of firm’s leverage and
EBITDA, by subtracting the industry median leverage and the industry median EBITDA-to-total
assets from the raw measures. The industry medians are based on 3-digit SIC codes provided that
there are at least five firms in the industry, excluding the sample firm. When there are less than
five companies at the 3-digit SIC level, industry adjustments are made at the 2-digit SIC level.
The industry constituents, as well as their values of leverage and operating performance, are taken
from Compustat. The ratio of debt due within a year (short-term debt) to total debt is adopted as a
measure of a firm’s immediate liquidity needs.
3.2. Debt Structure
The most relevant determinant of the choice between in-court and out-of-court restructuring is the
firm’s debt structure, in the form of both debt seniority and debt composition.
3.2.1. Debt Seniority
The priority structure of debt claims plays a crucial role in the debt restructuring process. Secured
creditors are unlikely to become significantly impaired in bankruptcy, while they may be worse off
in a distressed exchange when new issues with similar priority are offered to more junior creditors.
As a result, they tend to express a preference for in-court reorganizations. On the contrary, senior
unsecured and subordinated creditors may be more likely to agree to the debt exchange offer, given
their nature of residual claimants in the bankruptcy process. Asquith, Gertner and Scharfstein
(1994), and Chatterjee, Dhillon and Ramı́rez (1996) report a higher likelihood of bankruptcy when
the proportion of secured debt is larger. Asquith, Gertner and Scharfstein (1994) also suggest that
a higher fraction of secured debt may indicate a relatively low proportion of intangible assets in
12
the company, which translates into less costly bankruptcy proceedings.
Based on the information provided in the financial footnotes on the debt structure, we classify
each debt issue into one of three groups: (i) secured, if any firm’s assets were pledged as collateral
for the debt issue; (ii) senior unsecured; (iii) subordinated (which includes junior subordinated,
subordinated, or senior subordinated). For each firm, we compute the percentages of total debt
represented by secured, senior unsecured, and subordinated debt. We also record the total number
of tiers in the debt structure as a proxy for the complexity of the priority structure of the company.
3.2.2. Debt Composition
In the presence of a single lender, complete debt contracts, and symmetric information, a distressed exchange is always more efficient than a costly bankruptcy. In a more realistic setting,
severe information asymmetries between creditors and firm’s managers may undermine attempts
to renegotiate out of court (Mooradian (1994)). Additionally, when the firm’s debt is held by a
multitude of creditors, successful debt workouts may become more problematic due to both holdout problems and conflicts of interest (Gertner and Scharfstein (1991)). In this respect, a strand of
the existing literature (Gilson, John and Lang (1990), Brunner and Krahnen (2008)) argues that a
large proportion of bank debt is beneficial in avoiding bankruptcy, given that: (i) banks are better
informed than other creditors; (ii) different banks lending to the same firm normally share similar
interests and coordinate their response in case of financial distress.
This claim, however, is debatable under several aspects. First, virtually all the bank debt of
financially distressed firms is secured (James (1995)), hence banks have no incentive to make
concessions in favor of an out-of-court restructuring (see, for example, the evidence provided by
Asquith, Gertner and Scharfstein (1994), and Chatterjee, Dhillon and Ramı́rez (1996)). Second, as
first hinted by Franks and Torous (1994), when bank loans are syndicated, coordination problems
may arise among the participants to the syndicate, which include both banks and institutional
investors. Third, during a banking crisis the distressed conditions of financial institutions can
adversely affect bank-dependent borrowers (Khwaja and Mian (2008), Chava and Purnanandam
13
(2011), Li and Srinivasan (2011)).
For the above reasons, the direction in which debt composition affects the restructuring outcome remains unclear from an empirical standpoint. We analyze the debt structure by looking
at the composition of total debt between public debt and bank debt, which jointly represent, on
average, 95% of the total debt of the companies in the sample. The 10-K financial notes are generally not very helpful in identifying public debt issues. For this purpose, we consider both the
debt issues that were originally registered with the SEC, and those issued under Rule 144A and
subsequently registered with the SEC.11 Data on the securities registrations are available from the
SEC’s EDGAR database. We also record, for each company in the sample, the number of public
issues outstanding at the time of default, as a further proxy of potential creditors’ coordination
problems.
As a final measure of the overall complexity of the debt structure, we include firm’s size,
computed as the natural logarithm of total assets. Large firms are likely to experience more severe
creditors’ coordination problems. However, very large companies can benefit from the too big to
fail effect in times of crisis.
4. Empirical Findings
4.1. Distressed Exchange vs Bankruptcy: Univariate Analysis
Table 2 contrasts mean and median values of size, leverage, operating performance, and debt structure for firms that restructure out of court against those that file for bankruptcy: Panel A reports
the findings for the entire sample, while Panel B refers to reference entities only, and Panel C to
non-reference entities only. The last two columns report the values of the t-test for difference in
means and the non-parametric Wilcoxon test for difference in medians. In discussing the findings,
11
In line with previous evidence on the registrations of 144A offerings (Fenn (2000) and Huang and Ramı́rez
(2010)), we find that 87% of the Rule 144A issues in our sample are later registered with the SEC and, therefore, are
more similar to bonds than to private placements held by a handful of institutional investors.
14
we mainly focus on the medians due to the skewed distribution of most variables.
INSERT TABLE 2
4.1.1. Severity of Financial and Economic Distress
Looking at Panel A of Table 2, we observe that companies that file for bankruptcy are more financially distressed and face more immediate liquidity needs than those that manage to restructure
their debt out of court. The median value of the industry-adjusted leverage ratio for the former
reaches 0.52, against a value of 0.40 for the latter, and the median ratios of short-term debt to total
debt are equal to 0.15 and 0.02, respectively. The high proportion of short-term debt in bankrupt
firms is largely explained by the covenant violations recorded on some companies’ debt before default: Following a covenant violation, the debt of a distressed firm often becomes due immediately,
and is then reported as short-term debt in the SEC filings.12
No significant differences arise between the two groups in terms of operating performance,
which is also in line with the median operating performance of the corresponding industries. This
indicates that, in general, at year-end prior to the default event, the companies were not in a situation of economic distress relative to other firms in their industry.
4.1.2. Characteristics of the Debt Structure
In line with both our predictions and previous empirical findings, firms that renegotiate their debt
under Chapter 11 during the sample period are characterized by a higher proportion of secured
debt to total debt, and a lower proportion of senior unsecured debt to total debt (median values of
0.60 and 0.11), than companies that complete a debt workout (median values of 0.51 and 0.40,
12 To
assess whether the covenant structure may be a significant determinant of the restructuring method, we compare the number of financial covenants on bank debt and on bond issues in companies that filed for bankruptcy and in
those that restructured out of court. Information on the financial covenants imposed on bank debt and bond issues is
obtained from Dealscan and Mergent Fixed Income Securities Database (FISD), respectively. We do not find significant differences, either in mean or in median, in the covenant structure of firms with a different restructuring outcome.
Further, we do not find the number of financial covenants to vary significantly between CDS reference entities and
other firms. The results are available from the authors upon request.
15
respectively). More in general, we find that firms with a larger number of debt priority tiers have
better chances to restructure their debt out of court.
However, in contrast with a large body of existing research according to which banks are more
likely to overcome lender coordination problems, thus facilitating debt workouts, we do not find
the proportions of bank debt or public debt over total debt to be significantly different across firms
that restructure in court or out of court. In fact, we observe that successful debt workouts seem
more likely if the debt structure is diversified, as suggested by the higher number of public issues
outstanding and the larger size of companies that restructure out of court in our sample. These
findings are investigated more in depth in the Appendix, which provides a detailed analysis of the
characteristics of bank debt of the firms in the sample.
4.1.3. Presence of Credit Default Swaps
In Panels B and C of Table 2 we replicate the comparison among firms that arrange out-of-court
versus in-court restructurings for both CDS reference entities and non-reference entities. The
purpose is to highlight potential differences in the two groups that could help us shed some light
onto any peculiarities that may characterize the restructuring choice of reference entities.
The underlying intuition is the following: If we observed significant differences in the degree
of economic / financial distress, or in the debt structure, between firms that complete a distressed
exchange and firms that file for Chapter 11 within non-reference entities, but no significant differences within reference entities, then additional factors not included in our analysis would be
responsible for the restructuring choice of the latter, and the presence of empty creditors may be
one of those. If the proportion of insured creditors in CDS reference entities was sufficiently high
to influence the outcome of the debt renegotiation, we would not expect such outcome to depend
significantly on the company’s fundamentals, as bankruptcy would be primarily the (inefficient) result of the presence of empty creditors.13 Further, we would not expect the debt priority structure
13 It
is worth reminding that when bankruptcy represents the efficient restructuring outcome (e.g. when justified by
poor economic fundamentals, or low bankruptcy costs), the presence of insured creditors becomes irrelevant and no
empty creditor issue arises.
16
to be a significant determinant of the restructuring method, as a substantial portion of unsecured
creditors would behave like secured debtholders, finding it convenient to force the company into
Chapter 11.
Our empirical findings reveal that most of the factors that significantly affect the choice between distressed exchange and bankruptcy are common to both groups of reference entities and
non-reference entities. In both cases, companies that file for bankruptcy are more financially distressed than those that renegotiate out of court, are smaller, have more immediate liquidity needs
in order to repay short-term debt, a higher proportion of secured debt, and a less diversified debt
structure, with a lower number of public debt issues outstanding. All in all, the traditional determinants of the restructuring method remain significant also for CDS reference entities, and we do not
find evidence suggesting that the presence of empty creditors may be an important omitted variable
that would play a major role in this respect.
In order to legitimate, from a methodological perspective, both the comparative study presented
in Panels B and C and the probit analysis that follows, we contrast in Table 3 the mean and median
characteristics of CDS reference entities and non-reference entities at year-end prior to default.
Such comparison is also interesting per se, since no attempts have been made so far in the literature
to assess in what respect CDS reference entities differ from other firms with similar credit quality.
INSERT TABLE 3 HERE
As expected, we find reference entities to be significantly larger than other firms in the sample, as CDS contracts are typically available on large companies with widely traded bond issues,
for the purpose of hedging or trading positions in those securities. Reference entities also have a
larger proportion of senior unsecured debt compared to non-reference entities, given that it is the
standard class of reference obligations underlying CDS contracts. Moreover, we record a significantly higher number of tiers in the debt structure and of public issues outstanding for reference
entities compared to other firms. Interestingly, however, the overall proportions of bank debt and
public debt in reference entities are not significantly different from those in other companies in the
17
sample. Contrary to the general perception, reference entities are not significantly more leveraged
or economically distressed than non-reference entities.
4.2. Distressed Exchange vs Bankruptcy: Probit Analysis
In order to formally test whether CDS reference entities experience a higher probability of filing
for bankruptcy than other firms, we estimate a simple probit model where the dependent variable
equals zero if the company restructures out of court and equals one if it renegotiates under Chapter
11. A dummy variable indicates whether the firm is a CDS reference entity. In a first specification,
we regress the dependent variable only on the CDS dummy. The estimates (Table 4, column 2)
confirm that the presence of CDS per se is not a significant determinant of the debt restructuring
outcome in our sample.
In a second specification we introduce a selection of variables from the univariate analysis
to control for the effects of financial leverage and debt structure on the choice between the two
restructuring methods. Specifically, we include the size of the firm, the industry-adjusted measure
of leverage, the proportion of debt due within a year, the ratio of secured debt to total debt, and the
number of tiers in the debt structure. We also add a taxation dummy that equals one for default
events over the period Feb 2009 - Dec 2010, and zero otherwise, to control for the introduction of
the Recovery Act in February 2009, which granted tax incentives for distressed exchanges. The
results from the probit regression are reported in Table 4, column 3. In line with the findings from
the univariate analysis, we observe that the probability of restructuring in court is higher for firms
with higher leverage, more severe short-term financing needs, a larger fraction of secured debt, and
a lower number of debt tiers. As expected, out-of-court renegotiations become more likely when
the tax relief for distressed exchanges is in place. Again, we do not observe a higher likelihood of
filing for bankruptcy in reference entities.
To assess whether the determinants of the restructuring outcome are the same for both CDS reference entities and non-reference entities, we re-estimate the probit model on the two subsamples
of firms (Table 4, columns 4-5). Leverage and short-term funding needs are significant bankruptcy
18
determinants in both groups, while the number of debt tiers and the taxation dummy remain statistically significant only for non-reference entities. The hypothesis that the coefficients of the two
regressions are not significantly different cannot be rejected either at a pairwise level (Table 4,
column 6), or at a joint level according to a likelihood-ratio Chow test.14
Based on the model estimates for the subsample of non-reference entities, we then derive predicted probabilities of distressed exchange and bankruptcy, both in-sample for non-reference entities, and out-of-sample for CDS reference entities. If the latter were more prone to bankruptcy
due to the pressure of empty creditors, we would expect to observe larger errors in predicting debt
workouts for those firms. Specifically, in such cases, a company would be forced into bankruptcy
even though the financial and economic fundamentals are sound enough to yield a forecast of distressed exchange. Our findings (Table 4, column 5) do not support this claim. In CDS reference
entities, the percentage of correct predictions of debt workouts (i.e. the ratio between correctly predicted distressed exchanges and total number of predicted distressed exchanges) is indeed lower
than the percentage of correct predictions of Chapter 11 filings (66% against 78%). However, this
also occurs in-sample for non-reference entities, and the ratios of the percentage of correctly predicted workouts to the percentage of correctly predicted bankruptcies are essentially the same for
both reference entities and non-reference entities.
INSERT TABLE 4 HERE
A number of recent default events have gained considerable attention from the press, to quickly
become anecdotal evidence in favor of the empty creditor theory. Some of those defaults are
included in our sample, specifically the bankruptcies of Lear Corp., LyondellBasell Industries,
General Motors, R.H. Donnelley, and Six Flags Inc. From the estimates of the probit model we
derive predicted probabilities of filing for Chapter 11 for these companies. In all cases but one,
we find bankruptcy probabilities larger than 50% (72% for Lear Corp., 65% for LyondellBasell
14 The
comparison of probit coefficients across groups is biased if there are significant differences in the degree of
residual variation. The estimates from a heteroskedastic probit model reveal that, in our sample, the residual variance
of the non-reference entities group is only 20% lower than the residual variance of reference entities, and the two are
not significantly different. The results are available from the authors upon request.
19
Industries, 55% for General Motors, 47% for R.H. Donnelley, and 50% for Six Flags Inc.) which
suggests that in most cases the restructuring outcome was actually in line with the prediction based
on firms’ fundamentals.
It is worth noticing that throughout our investigation we have focused on distressed exchanges
and bankruptcies, while dismissing the default events due to missed payments that sometimes
precede debt renegotiations. After the expiry of a grace period, missed payments of interests
or principal are considered default events by Moody’s and reported in the Default and Recovery
Database. A missed payment also produces a credit event (“failure to pay”) under CDS contract
specifications and may therefore have an impact on our analysis. Once a failure to pay occurs,
insured bondholders can claim from protection sellers the payment of the face amount of debt,
net of the recovery value, upon delivery of the bonds. If all bondholders with a position in CDS
claim their payoffs, the protection sellers become the new bondholders who will participate to
the renegotiation talks. At this stage, in the absence of insured creditors with an incentive to file
for Chapter 11, the firm becomes essentially comparable to a non-reference entity. One way to
correct our sample to take this issue into account would be to remove, for CDS reference entities,
all debt workouts and bankruptcy filings that were preceded by a missed payment. However, since
the percentage of those bankruptcies is higher than the percentage of the distressed exchanges
(30% against 9%), this would bias the remaining sample of reference entity defaults towards debt
workouts. Alternatively, one could remove all renegotiations preceded by missed payments for
both reference entities and other firms, but the size of the residual sample would be too small.
Hence, we decide not to apply any correction in this respect: This is unlikely to introduce biases
in our study, as the percentages of debt workouts and bankruptcy filings anticipated by missed
payments are not significantly different between reference entities and other companies.
4.3. Evidence from Recovery Rates
Another avenue for the empty creditor issue to manifest itself is through the recovery rates of the
debt issues in default. Even though the presence of insured debtholders does not seem to directly
20
affect the restructuring outcome, it may still translate into higher recovery rates for reference entities than for other companies. This may occur in both: (i) distressed exchanges, given the better
conditions that tendering debtholders might be able to negotiate to grant their participation to the
restructuring; (ii) bankruptcy, if the pressure of empty creditors forces bankruptcy to occur prematurely, when the assets’ value is still sufficient to ensure satisfactory recoveries.
In order to test whether the presence of CDS has had an impact on recovery rates, we run two
separate regressions to explain recoveries following distressed exchanges and Chapter 11 filings.
Our measure of recovery rate is the price of the defaulted security recorded 30 days after the default
event. In addition to the CDS dummy, we add some control variables that the existing empirical
literature (see, for example, Acharya, Bharath and Srinivasan (2007)) has found to be significant
determinants of recovery rates. We expect the seniority of the security in default to be positively
associated with recovery rates. To account for seniority, we define a variable that equals zero for
secured debt, one for senior unsecured and two for subordinated. All else equal, the size of the
issue in default can also have a positive impact on recoveries, given the higher bargaining power
of debtholders of large issues. We include the ratio of EBITDA to total assets as an indicator of
the operating profitability of the firm, which may have a positive effect on recovery rates, through
an increase in the overall firm’s value. We control for leverage, measured as the ratio of total debt
to total assets, since debt restructuring may be harder to achieve in highly-leveraged firms, and
recoveries may be lower. Firm’s size can have a negative impact on the amount recovered due to
coordination problems that may arise among creditors. Asset tangibility, measured as the ratio of
net property, plant and equipment to total assets, is instead expected to increase recovery rates.
Finally, we include the median industry Tobin’s Q to account for the potential effect of growth
prospects of firm’s assets on the recovery value. The median industry Q is computed as median of
the ratio of market value of the firm (estimated as book value of total assets−book value of total
equity + market value of equity) to the book value of the firm, and is taken over all firms (excluding
the one in default) in the same 3-digit SIC code. Industry dummies, defined according to the firstdigit SIC code, are also added to capture any residual industry-related effects. Information on
recovery rates, seniority, size and number of the securities in default is taken from the Moody’s
21
Default and Recovery Database. Firm-specific variables are obtained from the 10-K filings, while
industry characteristics are taken from Compustat.
INSERT TABLE 5 HERE
We estimate the model separately for distressed exchanges and bankruptcy filings, using ordinary least squares, with standard errors adjusted for heteroscedasticity and clustered at firm’s level.
The results are reported in Table 5 (columns 2 and 4), and are in line with our previous findings
regarding the lack of evidence of the empty creditor issue: We do not find the presence of CDS
to have a significant impact on recovery rates of either distressed exchanges or bankruptcies. Instead, we observe that recovery values in general are positively associated with debt seniority and
industry Tobin’s Q, and negatively associated with firm’s leverage. Recovery values that follow a
bankruptcy filing are also negatively related to the size of the company.
As a final check, we assess the effects of the presence of CDS on the recovery rates of different
seniority classes of debt. Since most CDS are written on senior unsecured debt, there might be a
significant effect on the recovery rates of this class only, which may disappear when all seniority
classes are pooled together. For this purpose, we run similar regressions as the above, but we
include in the model three interaction dummies that take the value of one if the issue in default has
a given seniority (secured, senior unsecured, or subordinated) and the issuer is a CDS reference
entity, and zero otherwise. The results are reported in Table 5, columns 3 and 5. The interaction
dummy variables are all insignificant in explaining recoveries in both distressed exchanges and
Chapter 11 filings. To conclude, our investigation of recovery rates has shed some light on the
determinants of recovery rates in the recent crisis, but has not provided any evidence in favor of
the empty creditor issue.
We suggest two potential explanations to the lack of evidence of empty creditors in our findings. First, the proportion of insured bondholders in some reference entities may not be sufficiently
large to affect the outcome of the restructuring process. This explanation would be consistent with
some of the theoretical predictions on the empty creditor issue recently advanced by Campello and
22
Matta (2011). According to their model, in times of recession, CDS overinsurance (and, therefore,
a higher probability of CDS-led bankruptcy) only affects distressed firms with low continuation
values, while no overinsurance is associated with distressed firms with high continuation values.
Second, even though the proportion of insured creditors is large enough to impede the negotiation
of a workout, creditors may choose to support it, instead of forcing the company into bankruptcy.
This behavior can be ascribed to several factors, such as reputational issues for the creditors, or
the uncertainty related to the bankruptcy process. In this respect, it is worth reminding that involuntary bankruptcy cases initiated by creditors can be challenged by the debtor. Furthermore,
when disputes between creditors and obligors are taken to court, judges can investigate in detail the
economic interests of bondholders by requiring, for example, to disclose their positions in credit
derivatives on the firm’s debt.15 Unfortunately, the unavailability of data on CDS positions held by
firms’ creditors makes it unfeasible to empirically verify which of the two conjectures applies in
practice.
5. Further Evidence and Robustness Checks
5.1. Reference Entities Identification and CDS Liquidity
A crucial component of the above analysis is the identification of CDS reference entities. As explained, we regard as reference entities all firms for which MarkIt provides a valid CDS spread
at the time of default. One may argue that a different approach may be more appropriate in selecting the reference entities that can be affected by empty creditors. On the one hand, we may
expect the number of insured creditors to be significant only in firms with liquid CDS, as CDS
liquidity enables debtholders to easily create and unwind insured positions. On the other hand,
most of the activity in liquid CDS typically reflects offsetting positions held by market dealers,16
whereas trades in less liquid CDS might be mostly associated with “buy-and-hold” hedges for bond
15 This
happened, for instance, in the LyondellBasell bankruptcy filing.
Based on the DTCC Warehouse CDS Data at www.dtcc.com, we estimate that, on average, less than 10% of the
corporate CDS volumes are net of the offsetting dealers’ trades.
16
23
portfolios.
To address those concerns, we extend the probit analysis presented in Section 4.2. to allow the
impact of credit insurance on the restructuring method to depend upon CDS liquidity. In a first
specification, reported in Table 6, columns 2-3, we regress our binary dependent variable for the
restructuring outcome on both the CDS dummy and a CDS liquidity dummy which equals one if
the CDS of the firm is part of the CDX.NA.HY index in the year before default, and zero otherwise.17 In this specification, the CDS dummy accounts for the effect of CDS on the reorganization
choice for firms with less liquid CDS, while the sum of the coefficients on the CDS dummy and on
the CDS liquidity dummy captures the impact of CDS on debt restructuring for firms with more
liquid CDS. Our estimates reveal that neither the two dummy variables nor their sum are statistically significant, thus confirming that the availability of credit insurance does not seem to play a
role in explaining debt restructuring, irrespectively of the CDS liquidity.
INSERT TABLE 6 HERE
In a second specification, we exploit the information available on CDS volumes for liquid CDS
contracts by adding a variable equal to the ratio of the outstanding net notional value of CDS
(from DTCC Warehouse CDS Data) over the amount of senior unsecured debt of the company, at
the reference date of the last 10-K prior to default. Given the lack of data on naked and covered
CDS positions, the outstanding CDS volumes net of the offsetting dealers’ trades represent the best
available proxy for covered CDS volumes. Intuitively, the higher the ratio of net CDS volumes to
senior unsecured debt issued by the firm, the higher the proportion of insured creditors, which may
translate into a more significant impact on the restructuring outcome. In this specification, shown
in Table 6, column 4, the effect of CDS on debt renegotiations is captured by: 1) the CDS dummy
and the CDS volumes ratio for reference entities with liquid CDS contracts; 2) the sum of the CDS
dummy and a no-volumes dummy (which equals one if the CDS is illiquid and no volumes are
available, and zero otherwise), for reference entities with less liquid CDS contract. Again, we do
17 The CDX.NA.HY index constituents are the 100 most liquid single-name CDS on North American sub-investment
grade companies, hence we expect our approach to be accurate in capturing CDS liquidity.
24
not find the probability of filing for bankruptcy to be significantly affected by the presence of CDS
in general, nor by the volumes of CDS traded on the market.
Finally, we remove from the sample the firms with widely traded CDS, and we identify as
reference entities those with less liquid CDS, which are then compared to non-reference entities.
The rationale behind this choice is that firms with very liquid CDS can be substantially different,
for example in terms of size and debt structure, with respect to non-reference entities, which may
bias the comparison of the two groups. The results from the probit estimates are reported in Table
6, columns 5-6. In line with our earlier findings, CDS reference entities are not characterized by a
significantly higher probability of filing for bankruptcy compared to other firms.
5.2. Sample Matching and Instrumental Variables Estimates
A potential concern with our analysis is the presence of omitted latent variables: If reference entities differ from non-reference entities in ways which are systematically related to the restructuring
outcome, then the estimates of coefficients and standard errors from the probit model in Table 4
may be biased. To address this issue, we follow two approaches.
In a first approach, we group distressed companies into size quartiles, in an attempt to identify
a set of reference and non-reference entities with similar characteristics. As shown in Table 3,
companies with CDS traded on their debt are larger than other firms and, as a result, they are normally characterized by a more diversified debt structure. In this respect, working with firms in the
same size quartile can help overcome biases that may arise from the comparison of entities which
are substantially different.18 We focus on the third size quartile, which includes approximately
the same number of reference entities and other firms (45% and 55% respectively). In Table 7 we
present estimates of the probit model for the restructuring choice on the subsample. We find that
the presence of CDS seems to increase the probability of filing for bankruptcy when taken in isolation, but becomes insignificant after controlling for the company’s leverage and for the taxation
dummy.
18 A
propensity score matching would be desirable, but is unfeasible in this context given the small sample size.
25
INSERT TABLE 7 HERE
In a second approach, we look at the entire sample and attempt to identify instrumental variables which are correlated with the presence of CDS traded on the company’s debt, but uncorrelated with the restructuring choice. This is a very difficult task, as most variables that characterize
reference entities (e.g. large size, diversified and dispersed debt structure) may also, in principle,
affect the restructuring choice, as discussed in Section 3.
We select the bond turnover of a company as our instrumental variable for the presence of
CDS on the company’s debt. We compute the bond turnover over the year that terminates on the
reference date of the last available 10-K report prior to default. As argued by Saretto and Tookes
(2011), a high bond turnover typically arises from a significant trading activity, rather than from
“buy-and-hold” hedging strategies. In this respect, we expect a high turnover to be associated
with the presence of CDS, which greatly facilitates the implementation of trading strategies in the
bond market. Instead, we do not expect the bond turnover to be a significant determinant of the
restructuring outcome, especially when it is computed over a large time span well ahead of the
default event, as in our case.19
The bond turnover is computed as the ratio of the volumes of bonds exchanged during the
fiscal year prior to default over the average notional amount of bonds outstanding (calculated as
the average between the outstanding notionals at the beginning and at the end of the fiscal year
prior to default). The volumes of bonds exchanged on the market are obtained from the TRACE
system (Trade Reporting and Compliance Engine). We apply the corrections suggested by DickNielsen (2009) in order to filter out erroneous trades. Our measure of bond turnover may in fact
underestimate the real turnover for very liquid bonds, since TRACE does not report the exact
amount exchanged for volumes larger than one million USD, and we set those volumes equal to
one million. However, this is unlikely to bias our results, given that we do not aim at comparing
liquidity among very liquid bonds, but rather at distinguishing between liquid and illiquid bonds.
19 It
is worth reminding that, on average, there are 183 days between the last report date and default. This means
that the bond turnover is computed, on average, between 18 and 6 months prior to the restructuring choice.
26
INSERT TABLE 8 HERE
Table 8 reports estimates from our instrumental variables analysis. In column 2 we include,
for comparison, estimates from the probit model presented in Section 4.2. For consistency, both
models are estimated only on the subsample of firms with non-zero publicly traded debt, for which
the bond turnover can be computed. Column 4 shows estimates from the first-stage probit model
where the dependent variable is whether the firm is a CDS reference entity. The independent
variables include all the regressors from the probit model of the restructuring outcome, as well
as the bond turnover. As predicted, the coefficient of the bond turnover is statistically significant,
thus confirming that it is a valid instrument. In column 3 we report estimates from the secondstage instrumental variable probit model for the restructuring outcome, where the CDS reference
dummy is replaced with the predicted probability of having CDS traded on the debt from the firststage probit.20 Instrumenting for being a reference entity increases the estimated coefficient from
0.718 to 1.105. However, the coefficient remains statistically insignificant, thus supporting our
claim that the presence of CDS does not seem to affect the restructuring outcome.
5.3. Presence of CDS and Default Frequency
So far our analysis has examined whether the presence of CDS on the company’s debt affects the
restructuring decision of severely distressed companies that have experienced some type of default,
defined according to Moody’s criteria. A potential objection to our approach is that the presence
of empty creditors can in fact undermine the debt restructuring attempts of a company at an earlier
stage than the actual default. In practice, most companies initiate talks with debtholders aimed at
restructuring or refinancing their debt at the onset of financial distress. However, if a significant
proportion of debtholders are insured via CDS, they may be reluctant to renegotiate the debt, thus
pushing the company into more severe distress: All else equal, this would translate into a higher
frequency of defaults (either distressed exchanges or bankruptcy filings) among reference entities.
20 Wooldridge
(2001) argues that this method yields consistent coefficients and correct standard errors when the
dependent variable in the first stage is a binary variable.
27
To test this hypothesis, we define a sample of distressed firms which includes all U.S. companies that have been assigned a Moody’s corporate rating of Caa1 or lower at any time between
January 2006 and June 2010. Specifically, a company enters the sample if its rating is equal to, or
lower than, Caa1, and leaves the sample following an upgrade to B3 or higher, or a rating withdrawal. We then compute 1-year ahead default frequencies for the companies in the sample, based
on whether a firm completes a distressed exchange or files for bankruptcy within a year.21 Contrary
to the predictions of the empty creditors theory, 1-year ahead default frequencies are very similar
for reference entities (26%) and non-reference entities (23%), and not significantly different in
statistical terms.
We also analyze the rating dynamics for those firms that ultimately experience a default, and
investigate whether they differ in the period before default depending on whether CDS are traded or
not on the company’s debt. Intuitively, if rating agencies anticipate that an empty creditor problem
may threaten the company’s refinancing plans, we can expect to observe a faster downgrade of
those firms following the first signs of financial distress. In Table 9 we report mean values of
a number of variables that characterize the rating history of the defaulted companies (both CDS
reference entities and other firms) in the year before default. In particular, we consider: 1) the
corporate rating of the firm at year-end prior to default, where ratings are assigned a numeric
value ranging from 1 (Ba1) to 10 (Ca); 2) the rating change (in notches) that resulted in the rating
in 1); 3) the number of days for which the company has had the rating in 1), as a measure of
rating persistence; 4) the number of rating downgrades in the year before default; 5) the overall
net change in rating over the same year, measured in notches. Information on the rating history
of the firms in the sample is obtained from Moody’s. Our findings, based on t-test on the means
and Wilcoxon test on the medians, fail to highlight any significant differences between the rating
path of reference entities and other companies while approaching default. In fact, non-reference
entities seem to experience more severe downgrades before default, which is the opposite of what
we would expect if the threat of empty creditors had an impact on the rating agencies’ decisions.
21
Information on defaults is retrieved from Moody’s for rated firms and from Factiva for those firms whose rating
had been withdrawn by Moody’s in the year prior to default and are therefore no longer covered by the rating agency.
28
INSERT TABLE 9 HERE
Finally, we formally assess whether the presence of CDS has played a significant role in explaining the default frequency in our sample of distressed firms. We estimate a probit model where
the dependent variable, which equals one if a firm defaults (either in- or out-of-court) in the following year and zero otherwise, is regressed on a number of control variables and on a dummy
variable that indicates whether the company has CDS traded on its debt at the time of default.
The results are shown in Table 10. In a first specification (model 1), the set of control variables
only includes information on the rating history of the firm prior to default, which enables us to
retain all the companies in the sample. In a second specification (model 2), we add to the rating
variables a set of indicators of firm’s distress (leverage ratio and EBITDA over total assets), and of
complexity of the debt structure (proportion of bank debt over total debt and firm size), computed
at year-end before default.22 The financial variables are obtained from the 10-K filings, which are
however available only for 35% of the companies originally included in the sample. As a result,
this specification can be estimated only on a limited sample. Both models include yearly dummies
to capture annual variations in default rates due to market conditions. According to our findings,
the presence of CDS is not significantly associated with an increased probability of experiencing a
default, which is instead positively linked to the firm’s leverage and negatively associated with the
persistence of the company’s rating in the period before default.
INSERT TABLE 10 HERE
6. Concluding Remarks
Recent contributions from both the legal and financial literature have described the availability of
credit insurance via CDS as detrimental to efficient debt renegotiations in conditions of financial
22 These
variables are slightly different from the ones used to explain the restructuring outcome, and, in our opinion,
more appropriate as potential determinants of the probability of experiencing a default. For robustness, we have also
estimated the model by substituting the overall measures of leverage and operating income with the corresponding
industry-adjusted indicators, and the proportion of bank debt with the proportion of secured debt. The results, available
from the authors, are essentially unchanged.
29
distress. In this paper we look into this claim by investigating, from an empirical perspective,
whether the presence of CDS has, in fact, played a significant role in determining the debt renegotiation outcome. Our findings, from both univariate and probit analyses, indicate that reference
entities do not seem more likely to file for Chapter 11 than other firms. In fact, the main determinants of the restructuring method are the same in both reference entities and non-reference entities.
Also, we do not find evidence of a distortive pressure from insured bondholders on the recovery
rates following default. Interestingly, our results hold regardless of the liquidity of the CDS traded
on the company’s debt.
Further, we uncover some interesting facts regarding the role played by the traditional determinants of the debt restructuring. In line with what was documented in previous economic downturns,
we observe that high leverage and short-term debt ratios, and a large proportion of secured debt
increase the probability of filing for Chapter 11. In contrast with previous findings, bankruptcy
seems more likely for companies with a less diversified and dispersed debt structure.
Our results on the determinants of the debt restructuring outcome, and in particular the lack
of a significant role of CDS, allow us to conclude that, although the empty creditor issue might
have represented a specific concern for some companies, it was not a phenomenon of significant
proportions for the generality of reference entities. Further research on this topic requires detailed
information on who holds CDS positions on the market. In this respect, we contribute to the
current policy debate regarding the regulation of the CDS market following the enactment of the
Dodd-Frank Act, by highlighting the need for the introduction of measures aimed at increasing
transparency on CDS transactions.
30
Appendix: Bank Debt Characteristics
Contrary to the theoretical prediction that banks help avoid formal bankruptcy by solving information asymmetries and lenders’ coordination problems, we do not find the composition between
bank and public debt to play a significant role in determining the restructuring outcome. In this
appendix we attempt to provide an explanation by looking in more detail at the structure of bank
debt of the firms in the sample. Our findings can be ascribed to various factors. First, most of the
bank debt granted to distressed firms is secured, hence banks may be less willing to agree to a debt
workout. Second, the nature of bank debt in our sample is very different and much more complex than that reported by most related studies (which date back to the mid 1990s), following the
impressive growth of the syndicated loan market in the past 20 years (Sufi (2007)) that has introduced coordination issues among bank lenders. Third, since the 2007-2009 crisis originated in the
financial sector, the distressed conditions of banks might have had an impact on the restructuring
outcome of their borrowers.
The notes to the SEC financial statements reveal the overall amount of bank debt, the proportion
of bank debt which is secured, and how bank loans are divided between revolving lines of credit
and term loans, but do not provide information on lenders. To better investigate the structure of
bank debt, we restrict our attention to the loans covered in detail by the DealScan’s database,
which includes 99.3% of all bank loans in our sample. The following indicators are computed to
proxy for loan complexity and lenders’ coordination problems: 1) proportion of bank debt in the
form of institutional term loans (B-term, C-term or D-term), which are designed to be placed with
nonbank institutional investors, such as investment banks, pension funds, hedge funds, insurance
companies; 2) number of lead arrangers, i.e. number of banks that establish a relationship with
the firm and organize the loan; 3) number of participants, i.e. number of banks and/or institutional
investors that join the lead arrangers to fund part of the syndicated loan.
To assess whether the distressed conditions of bank lenders during the financial crisis might
have affected their ability to refinance their borrowers and, ultimately, might have played a role in
their restructuring process, we compute measures of both profitability and profitability shocks, i.e.
31
the annual return-on-assets (ROA) and the change in annual ROA, of the lead arranger at the end of
the fiscal period prior to the borrower’s default.23 Data on banks’ ROA are taken from BankScope.
Table A.1 reports mean and median values of the bank debt characteristics for firms that restructure out of court against those that file for bankruptcy (Panel A for the entire sample, Panel B
for reference entities only, and Panel C for non-reference entities only), and for reference entities
against non-reference entities (Panel D).24 We find the proportion of secured bank debt to be significantly higher in firms that restructure in court compared to those that complete a debt workout,
which suggests that the seniority of the claim plays an important role in the renegotiation process
also for bank lenders. Instead, we do not find the complexity of the loan syndicate to undermine
out-of-court renegotiations which seem, in fact, more likely when the syndicate has a higher number of lead arrangers. Finally, we observe bankruptcies to be more common among firms whose
main bank lenders experienced, on average, more severe shocks in profitability, although the relation is not statistically significant.
In summary, our findings suggest that the seniority of bank debt and, to a lesser extent, the
distressed conditions of bank lenders can help explain why banks may have been more reluctant to
agree to a debt workout in our sample.
No significant differences arise in terms of seniority of bank debt or profitability of lead arrangers between CDS reference entities and other firms. As expected, syndicated loans arranged
for reference entities involve a significantly larger number of lead banks and participants, given
that firms with CDS available on their debt are typically larger in size.
23 Average values are calculated when there are multiple lead arrangers. Other indicators of distress, such as Tier 1
capital ratios or net charge-off ratios, were considered, but had to be excluded due to the lack of data for a significant
number of the lead arrangers in our sample.
24
The difference in the size of the subsamples in Table A.1 and in Tables 2 and 3 is due to a number of firms that
have zero bank debt, which are included in Tables 2 and 3 but not in Table A.1.
32
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35
Table 1 – Summary Statistics on Default Events
Table 1 reports summary statistics on the default events recorded over the period Jan 07 – Jun 11 for nonfinancial U.S. rated companies with 10-K filings available at year-end prior to default. Data on the default
events are obtained from the Moody’s Default and Recovery Database. Information on which defaulted
companies are CDS reference entities is provided by MarkIt. Liquid CDS reference entities refer to firms
whose CDS was part of the CDX North America High Yield index in the year prior to default.
Number of Defaults
Percentages
Overall
Defaults
(N=163)
Out-ofCourt
(N = 65)
Chapter 11
Filings
(N = 98)
Overall
Defaults
Out-ofCourt
Chapter 11
Filings
5
34
99
20
5
2
10
45
7
1
3
24
54
13
4
3%
21%
61%
12%
3%
40%
29%
45%
35%
20%
60%
71%
55%
65%
80%
11
82
6
27
5
55
7%
50%
55%
33%
45%
67%
25
18
27
17
7
8
8
11
19
15%
11%
17%
68%
39%
30%
32%
61%
70%
Company type
Public
Private with public debt
89
74
32
33
57
41
55%
45%
36%
45%
64%
55%
Presence of CDS
Non-reference entities
Reference entities:
- liquid CDS
- less liquid CDS
94
69
25
44
38
27
12
15
56
42
13
29
58%
42%
15%
27%
40%
39%
48%
34%
60%
61%
52%
66%
Year
2007
2008
2009
2010
2011 (first semester)
Industry
Mining and construction
Manufacturing
Transportation, Communication
and Utilities
Trade: Retail and Wholesale
Services
36
Table 2 – Debt Workout vs Chapter 11: Univariate Analysis
Table 2 presents average and median values of firm characteristics for companies that restructured out-ofcourt or filed for Chapter 11. The last two columns report the t-test for difference in means and the
Wilcoxon test for difference in medians. Panel A reports findings for all companies in the sample, Panel B
only includes CDS reference entities and Panel C only includes non-reference entities. Size is the log of
total assets. Leverage is computed as the ratio of total debt to total assets, and Operating income as the ratio
of EBITDA to total assets. Industry-adjusted measures are computed by subtracting the industry median
(based on 3-digit SIC codes) leverage and EBITDA-to-total assets from the raw measures. Short-term debt
is debt due within one year. Public debt includes both debt issues originally registered with the SEC and
Rule 144A issues later registered. Data on firm characteristics are obtained from the 10-K filings at yearend before default. Information on which defaulted companies are CDS reference entities is provided by
MarkIt. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
Panel A: Overall Sample
Out-of-Court
Mean
Median
Size
Leverage
Leverage - industry adjusted
Operating income
Operating income - industry adjusted
Short term debt / Total debt
Bank debt / Total debt
Public debt / Total debt
Secured debt / Total debt
Senior unsecured debt / Total debt
Subordinated debt / Total debt
Number of tiers
Number public issues outstanding
# Observations
7.33
0.75
0.43
0.11
0.02
0.09
0.38
0.59
0.48
0.38
0.14
2.88
4.23
65
6.94
0.70
0.40
0.09
0.00
0.02
0.39
0.57
0.51
0.40
0.03
3.00
2.00
65
Chapter 11 Filings
Mean
Median
6.76
0.99
0.68
0.07
0.00
0.44
0.42
0.53
0.61
0.23
0.16
2.45
2.66
98
6.51
0.81
0.52
0.09
-0.02
0.15
0.40
0.57
0.60
0.11
0.00
2.00
2.00
98
t -test
2.57**
-3.33***
-3.52***
1.74*
1.14
-6.72***
-1.00
1.27
-2.67***
2.76***
-0.52
2.7***
1.59
Wilcoxon
2.53**
-2.51**
-2.97***
0.80
0.52
-4.74***
-0.83
0.95
-2.66***
2.82***
0.22
2.58**
2.48**
Panel B: CDS Reference Entities
Out-of-Court
Mean
Median
Size
Leverage
Leverage - industry adjusted
Operating income
Operating income - industry adjusted
Short term debt / Total debt
Bank debt / Total debt
Public debt / Total debt
Secured debt / Total debt
Senior unsecured debt / Total debt
Subordinated debt / Total debt
Number of tiers
Number public issues outstanding
# Observations
8.50
0.77
0.45
0.10
0.02
0.08
0.38
0.58
0.47
0.44
0.09
3.26
7.78
27
8.25
0.62
0.35
0.09
0.00
0.02
0.39
0.60
0.43
0.44
0.03
3.00
5.00
27
Chapter 11 Filings
Mean
Median
7.57
1.01
0.68
0.05
-0.02
0.37
0.42
0.54
0.59
0.34
0.14
2.86
4.55
42
7.50
0.79
0.48
0.07
-0.02
0.11
0.48
0.48
0.56
0.31
0.01
3.00
3.00
42
t -test
2.75***
-1.72*
-1.69*
1.44
1.32
-4.11***
-0.62
0.70
-1.76*
1.38
-1.33
1.53
1.51
Wilcoxon
2.85***
-1.91*
-1.86*
1.01
0.98
-2.63***
-0.95
0.95
-1.68*
1.62
0.41
1.56
2.65***
(continued on next page)
37
Table 2 (continued) – Debt Workout vs Chapter 11: Univariate Analysis
Panel C: Non-Reference Entities
Out-of-Court
Mean
Median
Size
Leverage
Leverage - industry adjusted
Operating income
Operating income - industry adjusted
Short term debt / Total debt
Bank debt / Total debt
Public debt / Total debt
Secured debt / Total debt
Senior unsecured debt / Total debt
Subordinated debt / Total debt
Number of tiers
Number public issues outstanding
# Observations
6.50
0.73
0.42
0.11
0.02
0.09
0.38
0.59
0.50
0.33
0.18
2.61
1.71
38
6.44
0.68
0.40
0.10
0.00
0.01
0.33
0.63
0.53
0.18
0.04
3.00
1.00
38
38
Chapter 11 Filings
Mean
Median
6.14
0.97
0.68
0.09
0.01
0.49
0.42
0.53
0.67
0.15
0.18
2.14
1.25
56
6.06
0.86
0.60
0.09
0.00
0.18
0.40
0.55
0.67
0.00
0.00
2.00
1.00
56
t -test
2.06**
-3.27***
-3.63***
1.01
0.42
-5.35***
-0.77
1.04
-2.72***
2.55**
0.05
2.84***
2.18**
Wilcoxon
1.97**
-2.63***
-3.04***
0.31
0.15
-4.03***
-0.30
0.43
-2.68***
2.62***
0.35
2.72***
1.99**
Table 3 – CDS Reference Entities vs Non-Reference Entities: Univariate Analysis
Table 3 presents average and median values of firm characteristics for CDS reference entities and nonreference entities included in our sample of defaulted firms. The last two columns report the values of the ttest for difference in means and the Wilcoxon test for difference in medians. For a description of the
variables, see Table 2. Data on firm characteristics are obtained from the 10-K filings at year-end before
default. The identification of CDS reference entities is based on information provided by MarkIt. *, **, and
*** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
CDS Reference entities
Mean
Median
Non-reference entities
Mean
Median
Size
Leverage
Leverage - industry adjusted
Operating income
Operating income - industry adjusted
Short term debt / Total debt
Bank debt / Total debt
Public debt / Total debt
Secured debt / Total debt
Senior unsecured debt / Total debt
Subordinated debt / Total debt
Number of tiers
Number public issues outstanding
7.94
0.92
0.59
0.08
0.00
0.26
0.41
0.55
0.52
0.38
0.12
3.01
5.81
7.97
0.76
0.41
0.09
-0.01
0.04
0.41
0.54
0.54
0.37
0.03
3.00
3.00
6.29
0.87
0.57
0.10
0.02
0.33
0.40
0.55
0.60
0.22
0.18
2.33
1.44
6.22
0.78
0.50
0.09
0.00
0.05
0.35
0.58
0.59
0.04
0.00
2.00
1.00
# Observations
69
69
94
94
39
t -test Wilcoxon
8.59***
0.46
0.20
-0.74
-0.74
-1.10
0.07
0.02
-2.07**
3.29***
-1.86*
4.59***
4.54***
7.44***
-0.51
-0.98
-0.86
-0.99
-0.50
0.52
-0.47
-2.01**
4.22***
0.28
4.49***
6.84***
Table 4 – Debt Workout vs Chapter 11: Probit Analysis
Table 4 presents estimates of probit models, where the dependent variable equals 1 if the company files for
Chapter 11 and equals 0 if the company restructures out of court. For a description of the variables, see
Table 2. Taxation dummy equals 1 for default events occurring in 2009-10, following the introduction of
the Recovery Act. CDS Reference Entity dummy equals 1 if the firm is a reference entity, according to
MarkIt. Robust standard errors are reported in brackets. The last column reports values of χ2 test of
individual coefficient equality between regressions (a) and (b), with p-values in brackets. *, **, and ***
indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
Independent variables
Entire
sample
Size
Entire
sample
CDS
NonCoefficient
reference
reference equality in
entities (a) entitites (b) (a) and (b)
-0.054
(0.12)
-0.186
(0.15)
0.202
(0.22)
2.04
(0.15)
Leverage - industry adjusted
0.834**
(0.34)
0.679*
(0.40)
1.527***
(0.53)
2.07
(0.15)
Secured debt / Total debt
0.717*
(0.39)
0.526
(0.65)
0.774
(0.49)
0.09
(0.76)
Short term debt / Total debt
1.771***
(0.37)
1.915**
(0.60)
1.765***
(0.46)
0.04
(0.84)
Number of tiers
-0.310**
(0.14)
-0.103
(0.18)
-0.528***
(0.19)
2.60
(0.11)
Taxation dummy
-0.548**
(0.27)
-0.306
(0.40)
-0.777**
(0.39)
0.73
(0.39)
1.519
(1.17)
-0.888
(1.55)
1.54
(0.21)
CDS reference entity dummy
0.034
(0.20)
0.479
(0.32)
Constant
0.242*
(0.13)
0.425
(0.82)
# Observations
Pseudo R2
163
163
69
94
1.00E-04
0.274
0.241
0.350
Chow test of coefficient equality in (a) and (b) - χ2 (7)
p-value
5.254
0.629
% correct predicted probabilities of Chapter 11
filings for non-reference entities (in-sample)
83%
% correct predicted probabilities of out-of-court
restructurings for non-reference entities (in-sample)
72%
% correct predicted probabilities of Chapter 11
filings for reference entities (out-of-sample)
78%
% correct predicted probabilities of out-of.court
restructurings for reference entities (out-of-sample)
66%
40
Table 5 – Determinants of Recovery Prices
Table 5 presents OLS estimates of determinants of recovery prices for both debt workouts and Chapter 11
filings. Seniority class equals 0 if the issue in default is secured, 1 if it is senior unsecured, 2 if it is
subordinated. Asset tangibility is the ratio of property, plant and equipment to total assets. Median industry
Q is the median of the ratio of market value of the firm to book value, for all firms in the 3-digit SIC code
of the defaulted firm. The interaction dummies in regressions (b) equal 1 if the issue in default has a given
seniority and the issuer is a CDS reference entity, and 0 otherwise. For a description of the other variables,
see Table 2. All regressions include industry dummies. Standard errors (in brackets) are corrected for
heteroscedasticity and clustered at firm’s level. *, **, and *** indicate statistical significance at the 10%,
5%, and 1% levels, respectively.
Independent variables
Out-of-Court Out-of-Court
(a)
(b)
Chapter 11
Filings
(a)
Chapter 11
Filings
(b)
Log (Issue size)
0.010
(0.01)
0.004
(0.01)
0.004
(0.01)
-0.006
(0.01)
Seniority class
-0.095**
(0.04)
-0.108
(0.07)
-0.269***
(0.03)
-0.203***
(0.03)
-0.032
(0.02)
-0.026
(0.02)
-0.047***
(0.02)
-0.027*
(0.02)
-0.227**
(0.09)
-0.213**
(0.09)
-0.057*
(0.03)
-0.049*
(0.03)
Operating income
0.000
(0.29)
-0.053
(0.31)
0.190
(0.15)
0.151
(0.16)
Asset tangibility
0.046
(0.17)
0.044
(0.17)
0.031
(0.10)
0.075
(0.09)
0.202**
(0.10)
0.218**
(0.10)
0.088*
(0.05)
0.092*
(0.05)
Size
Leverage
Median industry Q
CDS reference entity dummy
0.016
(0.09)
0.072
(0.06)
CDS dummy * Secured dummy
0.160
(0.15)
0.151
(0.10)
CDS dummy * Sen unsecured dummy
-0.030
(0.09)
-0.067
(0.06)
CDS dummy * Subordinated dummy
0.069
(0.12)
0.026
(0.08)
Constant
0.507*
(0.28)
0.579*
(0.29)
1.009***
(0.17)
0.875***
(0.24)
Industry class dummies
Yes
Yes
Yes
Yes
# Observations
189
189
276
276
0.218
0.215
0.227
0.218
2
R
41
Table 6 – CDS Liquidity
Table 6 presents robustness checks for the probit models in Table 4. The dependent variable equals 1 if the
company files for Chapter 11 and equals 0 if the company restructures out of court. The CDS Reference
Entity dummy equals 1 if the firm is a reference entity, and 0 otherwise. The CDS Liquidity dummy equals 1
if the firm is a reference entity whose CDS was part of the CDX North America High Yield index in the
year prior to default, and 0 otherwise. The CDS No Volumes dummy equals 1 if the firm is a reference
entity but outstanding volumes of CDS traded are not available from DTCC. The probit models in columns
5-6 are estimated on a sample that includes non reference entities and reference entities whose CDS are not
part of the CDX.NA.HY index. For a description of the variables, see Tables 2 and 4. Robust standard
errors are reported in brackets. *, **, and *** indicate statistical significance at the 10%, 5%, and 1%
levels, respectively.
Reference entities with
illiquid CDS only
Entire sample
Independent variables
Size
-0.085
(0.15)
-0.060
(0.15)
-0.085
(0.17)
Leverage - industry adjusted
0.806**
(0.34)
0.815**
(0.35)
0.847**
(0.37)
Secured debt / Total debt
0.757*
(0.39)
0.706*
(0.39)
0.678*
(0.40)
Short term debt / Total debt
1.763***
(0.37)
1.784***
(0.38)
1.579***
(0.36)
Number of tiers
-0.306**
(0.14)
-0.328**
(0.14)
-0.311**
(0.16)
Taxation dummy
-0.540**
(0.27)
-0.546**
(0.27)
-0.582**
(0.29)
0.703
(0.76)
CDS reference entity dummy (a)
0.168
(0.23)
0.477
(0.32)
CDS liquidity dummy (b)
-0.360
(0.32)
0.153
(0.43)
CDS no volumes dummy (c )
-0.188
(0.65)
CDS outstanding (net) / Senior unsec. debt
-1.008
(1.57)
Constant
# Observations
0.242*
(0.13)
0.599
(0.94)
0.522
(0.98)
0.168
(0.24)
0.465
(0.34)
0.242*
(0.13)
0.705
(1.17)
163
163
163
138
138
Pseudo R2
0.006
0.271
0.272
0.281
0.263
χ2 (2) test: (a) + (b) = 0
p-value
0.460
(0.50)
1.621
(0.20)
χ2 (2) test: (a) + (c) = 0
p-value
2.412
(0.12)
42
Table 7 – Debt Workout vs Chapter 11: Probit Analysis on Size Quartiles
Table 7 presents estimates of probit models, where the dependent variable equals 1 if the company files for
Chapter 11 and equals 0 if the company restructures out of court. The observations include only the
companies in the third quartile of the overall sample by size. For a description of the variables, see Tables 2
and 4. Robust standard errors are reported in brackets. *, **, and *** indicate statistical significance at the
10%, 5%, and 1% levels, respectively.
(1)
Independent variables
Leverage - industry adjusted
(2)
1.697*
(1.02)
Taxation dummy
-1.353**
(0.66)
CDS reference entity dummy
Constant
# Observations
2
Pseudo R
43
1.022**
(0.44)
0.743
(0.50)
-0.055
(0.26)
0.498
(0.67)
41
41
0.107
0.238
Table 8 – Instrumental Variables Regression
Table 8 contains estimates from an instrumental variables probit regression for the restructuring outcome.
Column 4 presents estimates from a probit model (first stage of the IV) where the dependent variable
equals to one if the company is a reference entity, and zero otherwise. The independent variables are the
variables discussed in Table 4, and the instrument used is the Bond turnover over the fiscal year before
default. Column 3 presents estimates from the second stage of the IV probit model for the restructuring
outcome, where the CDS reference entity dummy is replaced by the predicted probability of having a CDS
as estimated from the first stage of the IV regression. Column 2 contains non-instrumented probit estimates,
for comparison. All models are estimated on companies with non-zero public debt. Robust standard errors
are reported in brackets. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels,
respectively.
Probit
Second
Stage IV
First
Stage IV
Size
-0.085
(0.13)
-0.172
(0.20)
1.099***
(0.20)
Leverage - industry adjusted
0.597*
(0.35)
0.457*
(0.27)
1.459**
(0.68)
Secured debt / Total debt
1.029**
(0.45)
1.112**
(0.46)
-1.352**
(0.64)
Short term debt / Total debt
1.965***
(0.42)
1.970***
(0.44)
0.154
(0.36)
Number of tiers
-0.384**
(0.15)
-0.385**
(0.16)
0.164
(0.19)
Taxation dummy
-0.445
(0.30)
-0.393
(0.31)
-0.744**
(0.35)
CDS reference entity
0.718
(0.46)
1.105
(0.74)
Independent variables
Bond turnover
0.558***
(0.20)
Constant
# Observations
2
Pseudo R
44
0.595
(0.91)
1.040
(1.18)
-5.839***
(1.64)
139
139
139
0.295
0.282
0.473
Table 9 – Rating History Before Default
Table 9 reports average values of variables that characterize the rating history of all U.S. Moody’s rated
defaulted companies (distressed exchanges and bankruptcies) over the period Jan 2007-Jun 2011 in the year
before default. The Company rating is taken at year-end prior to default, where ratings are assigned a
numeric value ranging from 1 (Ba1) to 10 (Ca). The Rating change (in notches) is the change that resulted
in this company rating. Days in rating indicate the number of days in which the firm has had this company
rating. The last two columns report the t-test for difference in means and the Wilcoxon test for difference in
medians. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.
Company rating before default
Rating change
Days in rating
Number of downgrades in year before default
Notches changes in year before default
Reference
entities
Mean
Non-reference
entities
Mean
8.30
-1.36
186.43
1.49
-2.19
8.30
-1.67
159.93
1.23
-2.03
70
138
# Observations
45
t -test Wilcoxon
0.03
2.19**
0.73
1.59
-0.61
-0.03
1.29
-0.46
1.31
-0.52
Table 10 – Determinants of Default
Table 10 presents estimates of probit models, where the dependent variable equals 1 if a company defaults
(in- or out-of-court restructuring) in a given year and zero otherwise. The sample in model (1) includes all
U.S. rated companies that have a corporate rating of Caa1 or lower over the period Jan 2006-Jun 2010. The
sample in model (2) includes only the companies in (1) with 10-K filings available. For a description of the
variables, see Tables 2 and 9. CDS Reference Entity dummy equals 1 if the firm is a reference entity,
according to MarkIt. Robust standard errors are reported in brackets. *, **, and *** indicate statistical
significance at the 10%, 5%, and 1% levels, respectively.
(1)
Independent variables
(2)
Company rating before default
0.070
(0.05)
Rating change
-0.087
(0.05)
-0.032
(0.09)
-0.232***
(0.05)
-0.203**
(0.08)
-0.016
(0.08)
-0.032
(0.12)
Ln(days in rating)
Number of downgrades in year before default
Leverage
0.906***
(0.22)
Operating income
-0.297
(0.83)
Size
0.100
(0.08)
Bank debt / Total debt
0.137
(0.32)
CDS reference entity dummy
0.068
(0.12)
0.206
(0.20)
Constant
-0.734
(0.45)
-1.706**
(0.79)
Yearly dummies
Yes
Yes
# Observations
790
310
0.135
0.201
2
Pseudo R
46
Table A.1 –Bank Debt Characteristics
Table A.1 presents average and median values of bank debt characteristics for companies that completed a
distressed exchange and companies that filed for Chapter 11. The statistics only refer to firms with nonzero bank debt. Number of lead arrangers and participants refer to the origination of the existing
syndicated loans of the company. Profitability and ∆ Profitability of lead arranger refer, respectively, to
annual ROA and change in annual ROA of the lead arranger computed at quarter-end before default. In
case of multiple lead arrangers, we consider average values for profitability and ∆ in profitability.
Information on loans and bank profitability is obtained from Dealscan and Bankscope. Information on
which defaulted companies are CDS reference entities is provided by MarkIt. *, **, and *** indicate
statistical significance at the 10%, 5%, and 1% levels, respectively.
Panel A: Overall Sample
Out-of-Court
Mean
Median
Secured bank debt / Bank debt
Term loans B / Bank debt
Number lead arrangers
Number participants
Profitability lead arranger
Δ Profitability lead arranger
# Observations
0.91
0.49
1.81
6.54
0.14
-0.19
59
1.00
0.65
2.00
4.00
0.48
-0.28
59
Chapter 11 Filings
Mean
Median
0.98
0.49
1.61
5.37
-0.04
-0.48
87
1.00
0.53
1.00
3.00
0.30
-0.29
87
t -test
-2.04**
0.05
1.48
0.84
1.00
1.63
Wilcoxon
-2.83***
0.33
1.68*
1.02
0.42
1.04
Panel B: CDS Reference Entities
Out-of-Court
Mean
Median
Secured bank debt / Bank debt
Term loans B / Bank debt
Number lead arrangers
Number participants
Profitability lead arranger
Δ Profitability lead arranger
# Observations
0.94
0.57
2.16
8.92
0.04
-0.30
25
1.00
0.74
2.00
5.00
0.49
-0.28
25
Chapter 11 Filings
Mean
Median
0.96
0.50
1.88
6.83
0.06
-0.49
40
1.00
0.72
2.00
4.50
0.35
-0.31
40
t -test
-1.52
0.65
1.13
0.70
-0.07
0.83
Wilcoxon
-1.65*
0.83
1.08
-0.44
-0.09
0.51
Panel C: Non-Reference Entities
Out-of-Court
Mean
Median
Secured bank debt / Bank debt
Term loans B / Bank debt
Number lead arrangers
Number participants
Profitability lead arranger
Δ Profitability lead arranger
# Observations
0.89
0.43
1.56
4.79
0.20
-0.11
34
1.00
0.00
1.50
4.00
0.45
-0.29
34
Chapter 11 Filings
Mean
Median
1.00
0.47
1.38
4.13
-0.12
-0.47
47
1.00
0.52
1.00
3.00
0.29
-0.29
47
t -test
-2.19**
-0.43
1.38
0.70
1.30
1.35
Wilcoxon
-2.75***
-0.31
1.66*
1.20
0.84
0.90
(continued on next page)
47
Table A.1 (continued) – Bank Debt Characteristics
Panel D: Overall Sample
CDS Reference entities
Mean
Median
Secured bank debt / Bank debt
Term loans B / Bank debt
Number lead arrangers
Number participants
Profitability lead arranger
Δ Profitability lead arranger
# Observations
0.96
0.53
1.99
7.63
0.05
-0.42
65
1.00
0.74
2.00
5.00
0.37
-0.28
65
48
Non-reference entities
Mean
Median
0.95
0.45
1.46
4.41
0.02
-0.32
81
1.00
0.51
1.00
4.00
0.30
-0.29
81
t -test
0.30
1.09
3.96***
2.45**
0.21
-0.53
Wilcoxon
0.12
0.77
3.81***
2.42**
-0.35
-0.66