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Emerging from Chapter 11 Bankruptcy:
Is It Good News or Bad News for Industry Competitors?
Gaiyan Zhang
A firm under Chapter 11 bankruptcy protection may emerge from bankruptcy in a more
advantageous competitive position within its industry to the detriment of their industry rivals.
Using a sample of 264 firms that emerged from Chapter 11 bankruptcy during the period 1999-
2006, I find that its industry competitors demonstrate negative post-emergence long-term equity
returns and deteriorating financial performance. Additional tests indicate that this outcome is less
likely due to overall industry distress. Competitors tend to be more adversely affected if they are
in more concentrated industries, if they have lower credit quality, when a more efficient firm
emerges, and when the duration of bankruptcy is longer. This study suggests a need to reconsider
Chapter 11’s role in promoting competition and allocation of resources given its negative
externalities on industry competitors.
Gaiyan Zhang is an Assistant Professor in the College of Business Administration at the
University of Missouri-St. Louis, St. Louis, MO 63121. E-mail: [email protected]
I appreciate many constructive suggestions of the editor (William Christie), Jean Helwege, Philippe Jorion, Neal Stoughton, and one anonymous referee. All remaining errors are my own.
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I. Introduction
Chapter 11 of the U.S. Bankruptcy Code is designed to save firms that are in temporary
financial distress, allowing them to reduce their debt and giving them time and financing to re-
emerge as going concerns. Proponents of Chapter 11 bankruptcy assert that it is desirable to
allow viable firms to survive as more choices enhance competition and benefit consumers.
However, the role of Chapter 11 bankruptcy in promoting competition and allocation of
resources has been under debate. Opponents of Chapter 11 bankruptcy hold that Chapter 11
reorganization facilitates the rescue of inefficient firms (Hotchkiss, 1995), and that the existing
reorganization process resolves the problem of division among stakeholders in a way that suffers
from substantial imperfections (Bebchuk, 1988). These studies focus on the impact of Chapter 11
bankruptcy on the filing firm and its stakeholders.
It is also argued that the Chapter 11 reorganization law is excessively lenient, granting
filing firms such advantages as debt write-offs and cost savings from renegotiated labor contracts,
which distort the market and harm more competitive businesses. Jensen (1991) writes that
Chapter 11 bankruptcy is strongly pro-debtor and that certain features of the reorganization
process may lead to "chronic inefficiencies." One case in point is repetitive bankruptcy filings in
the U.S. airline industry. Many consider Chapter 11 reorganization to be an indirect subsidy that
gives weak airlines an unfair advantage by allowing them to stop making debt payments freeing
up cash to expand routes and continue predatory pricing to the detriment of their competitors.1
Some airline industry experts even liken the bankrupt carriers to a virus that will eventually
infect the entire industry (Gong, 2007).
1 Ciliberto and Schenone (2008) examine whether a firm operating under bankruptcy protection significantly reshapes competition for the firm’s product in markets where the bankrupt and the non-bankrupt firms are in direct competition, using evidence from the U.S. airline industry. They find a significant drop in the median airfare price in markets where a bankrupt carrier operates.
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To fully understand the role of Chapter 11 bankruptcy, it is important to investigate
whether and how industry competitors are affected when a firm files for Chapter 11 protection
and after its emergence from bankruptcy. Existing studies document that industry peers posted
negative equity returns and widening credit spread around a firm’s Chapter 11 bankruptcy (Lang
and Stulz, 1992; Jorion and Zhang, 2007). However, it is not clear whether a firm’s bankruptcy
filing has an enduring effect on industry peers after it emerges.
In this paper, I study the long-term equity returns and financial performance of industry
competitors after a firm in the same industry emerges from Chapter 11 protection. The purpose is
to examine industry effects of a firm’s emergence from bankruptcy, and the determinants and
underlying economic reasons for those industry effects.
It is not clear a priori whether a firm’s emergence from bankruptcy has a good or bad
influence on its industry. Emergence may imply improving or more promising industry
economic prospects, such as greater demand or lower costs of raw materials and thus raising
profit margins. These effects should be associated with greater cash flow and, if unanticipated,
higher equity returns for the whole industry. I call this the “positive spillover” hypothesis.
Alternatively, a firm’s emergence may have a negative effect on its industry if the firm under
bankruptcy protection emerges as a healthier and leaner competitor. The firm may have shed a
heavy debt burden and substantially reduced its labor costs, shuffled its management team, and
developed new strategies, making it ready to compete vigorously with a lower cost structure and
lower prices. For example, Ciliberto and Schenone (2008) found that when a bankrupt airline
cuts airfares as a result of the lower costs associated with operating under bankruptcy protection,
large national carriers react to those lower fares by cutting their prices as well. A study by
Dattner (2005) found that competitors of WorldCom such as AT&T, SBC, and Verizon spent
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enormous resources to derail WorldCom’s emergence from bankruptcy because of the
competitive advantage the newly reorganized company would gain from the bankruptcy filing. I
call the adverse effects on industry rivals associated with a firm’s emergence the “competition
effects” hypothesis.
Using a sample of 264 public firms that emerged from Chapter 11 bankruptcy during the
period 1999-2006, I find that a firm’s emergence from Chapter 11 has an adverse long-term
effect on equity returns for its industry peers. Under the book-to-market and size-matched
(BMSM) model, I find large negative cumulative abnormal returns (CAR) for industry portfolios
of -6.70% in the 200 days following emergence. The calendar-time portfolio approach yields
consistent evidence that industry portfolios suffer significant annualized abnormal equity returns
of -5.95%. The abnormal returns under the two models translate into a significant loss of $5.0
billion to $5.6 billion for equity shareholders for an average industry.2 In contrast, Eberhart,
Altman, and Aggarwal (1999) find that the CAR for the reorganized firm ranges from 24.6%-
138.8% for the 200 days after its emergence using the BMSM model. This comparison suggests
that while the reorganized firm appears to do better than the market had expected at the time of
emergence, industry rivals do worse than the market had anticipated.
One may argue that negative industry returns are not related to competition effects from
the emergent firm; rather, the whole industry may have been experiencing financial distress for a
long time, thus displaying low equity returns. I investigate this possibility by tracking abnormal
equity returns for the industry portfolio prior to the emergence announcement. With the BMSM
model, the industry peer group earns positive CAR of 5.83% for the [200, 1] event window
prior to a firm’s emergence from bankruptcy. With the calendar-time portfolio approach,
2 This is estimated by multiplying the CAR by the average industry market value of equity (INDMVEQ in Table III).
5
industry peers earn significant annualized abnormal equity returns of 8.34% during the year prior
to the emergence. This evidence suggests that the equity returns for a given industry declined
considerably following a firm’s emergence, inconsistent with the industry distress explanation.
Industry rivals are not affected in the same way. I analyze cross-sectional industry, firm,
and event differences in the industry abnormal returns. Competitors tend to be more adversely
affected if they are in more concentrated industries, when competitors have lower credit quality,
when a more efficient firm emerges, or when the duration of bankruptcy is longer. There is
mixed evidence regarding industry effects when a large firm emerges. The emergence of a larger
firm may have stronger competition effects, but it may also signal promising industry prospects
providing offsetting positive industry effects.
What are the fundamental reasons for the loss of equity value among industry rivals? To
address this question, I further investigate the dynamics of an industry’s financial performance
by comparing a variety of market-adjusted accounting ratios of industry competitors before and
after a firm’s emergence from bankruptcy. An average industry shows signs of deterioration in
terms of profitability and the cost-effectiveness ratio during the first two years after the
emergence, while, in contrast, the reorganized firm demonstrates signs of improvement in these
performance dimensions after controlling for industry-wide factors. The weaker financial
performance of industry peers may occur as a result of a price war between the emerging firm
and its competitors. For example, if a reorganized firm launches a price war or its emergence
contributes to overcapacity in the industry, its competitors may react by cutting their own prices,
raising costs to improve product quality, or increasing marketing and sales expenses.
This study should be of broad interest to researchers and regulators. It provides new
insight into the role of Chapter 11 in promoting competition given its negative externality effects
6
on industry competitors. Chapter 11 reorganization appears to provide competitive advantages to
the reorganized firm at the expense of industry rivals. Policymakers may need to weigh the
potential benefits of allowing firms to reorganize against the possible costs to the industry.
The remainder of the paper is structured as follows. Section II presents the issue of
bankruptcy and emergence, reviews related literature, and develops the hypotheses. Section III
discusses the data and method. Section IV presents my empirical results regarding industry
equity returns and the financial performance of industry rivals and the reorganized firm. Section
V provides my conclusions.
II. The Issue
The major concern of this study is the effect of a firm’s emergence from Chapter 11
bankruptcy on industry competitors. As such, it is necessary to understand the Chapter 11 plan of
reorganization. Upon filing for Chapter 11, a company may operate under Chapter 11 as a debtor
in possession. A debtor in possession can acquire financing and loans on favorable terms by
giving new lenders first priority on the business's earnings. The court may permit the debtor in
possession to reject and cancel contracts entered into earlier. Debtors are also protected from
other litigation against the business through the imposition of an automatic stay on litigation.
While the automatic stay is in place, most litigation against the debtor is stayed or put on hold
until it can be resolved in bankruptcy court or resumed in its original venue. So, the firm may
operate without making payments on its debt and interest expenses as creditors are not allowed
to collect on their claims or file lawsuits against the filing firm. Therefore, a filing firm’s
operating expenses are greatly reduced. In addition, the company may be better positioned to
renegotiate unfavorable contracts with stakeholders such as suppliers and unions. As such,
7
although Chapter 11 bankruptcy filing may be a financially costly process, reduce consumer
confidence, and make it difficult for the firm to contract with third parties, there are also benefits
associated with bankruptcy protection.3 These benefits include the automatic stay, the reduction
in debt load, and greater bargaining power with labor unions and other third parties.
Reorganized firms may emerge from Chapter 11 bankruptcy within a few months or in
several years, depending on the size and complexity of the bankruptcy. They emerge from
Chapter 11 when their creditors approve a plan of reorganization that is filed with the court. The
plan effective date (formal emergence date) is usually within a month after the plan is confirmed
by the bankruptcy court. Creditors rarely receive full return of their principle as most companies
entering Chapter 11 are insolvent. Moreover, the rights of shareholders are typically eliminated.
Therefore, Chapter 11, by design, allows companies to emerge with their assets intact and a
substantially reduced debt load. Such companies are expected to compete with their competitors
at a lower cost.
Prior studies have examined the financial performance and stock returns of companies
after they emerged from bankruptcy. For example, in a study of 197 public firms that emerged
from Chapter 11 bankruptcy, Hotchkiss (1995) found that a reorganized firm, on average, earned
an operating profit margin lower than the industry median, had a debt ratio higher than the
industry median, and frequently needed to restructure its debt again. The terms "Chapter 22" and
“Chapter 33” have been coined to describe firms that go through Chapter 11 a second and a third
time. Their need to re-enter bankruptcy is evidence that the reorganization process does not
always effectively rehabilitate distressed firms. Indeed, there are economically important biases
3 However, Ciliberto and Schenone (2008) examined airline market competition between bankrupt and non-bankrupt airlines, and found no evidence that consumers substitute away from a bankrupt airline to its competitors.
8
that favor the continued operation of unprofitable firms.4 Alderson and Betker (1999) found that
the total cash flow returns for 89 firms as a group produced weak operating margins in the post-
bankruptcy environment. However, the total cash flows produced by the reorganized firms, and
returned to both debt and equity holders, provided a return that was competitive with returns on
alternative investments of similar risk. Eberhart et al. (1999) assessed the long-term stock return
performance of 131 firms emerging from Chapter 11. They found large positive excess returns in
the 200 days after emergence. They concluded that although firms may not do well in their post-
Chapter 11 financial performance, they appear to do better than the market had expected at the
time of emergence from Chapter 11.
Researchers have also examined the short-term industry effects of Chapter 11 bankruptcy
announcements. Lang and Stulz (1992), for example, reported significant 11-day negative
abnormal returns for industry portfolios around the Chapter 11 bankruptcy filing based on 59
filings over the period 1970-1989. Jorion and Zhang (2007) extended the study to credit default
swaps (CDS) and the stock markets based on 272 Chapter 11 bankruptcies and 22 Chapter 7
bankruptcies. They found that industry rivals experienced widening CDS spreads and negative
abnormal returns for the 11 days around Chapter 11 bankruptcy filings, consistent with industry
contagion effects. For Chapter 7 bankruptcy filings, however, they found narrowing CDS
spreads and positive abnormal returns, consistent with industry competition effects.
However, there has been little systematic investigation of long run industry effects
following a firm’s emergence from Chapter 11 bankruptcy. This paper aims to fill this important
void. With protection from Chapter 11 law, a firm may emerge as a new entity competing
fiercely with industry rivals. After achieving cost savings under the protection of Chapter 11, the
4 See Hotchkiss (1995) for a survey of reasons such as over-investment and management’s self-serving that Chapter 11 may facilitate the rescue of inefficient firms.
9
firm can afford to lower product prices in order to gain a larger share of the market. In response,
rivals may lower their product prices as well thus hurting their profit margins. In the context of
the U.S. airline industry, Ciliberto and Schenone (2008) examined whether a firm operating
under bankruptcy protection significantly reshapes competition for the firm's product in markets
where the bankrupt and the non-bankrupt firms are in direct competition. They indicated a
significant drop in the median product price across competitors in markets where a bankrupt
carrier operates. For bankrupt airlines, they found that the lower costs associated with operating
under bankruptcy protection can explain price cuts. For instance, airlines can renege on their debt
payments, reject leases, lay off employees, reduce wages, and suspend pension contribution
payments. In addition, firms operating under bankruptcy protection do not suffer a significant
loss of reputation because they experience no real demand shock. Moreover, large national
carriers typically react to another firm’s bankruptcy by cutting prices.5
The potential negative competition effect for industry competitors of a firm’s emergence
from Chapter 11 is also evidenced anecdotally by the intensity and enormous resources spent by
competitors like AT&T, SBC, and Verizon to derail WorldCom’s emergence from bankruptcy
(Dattner, 2005). WorldCom’s competitors seemed to believe that the newly reorganized
company would receive some substantial benefits stemming from its reorganization. Dattner
(2005) also analyzed different ways in which the mechanisms of Chapter 11 could adversely
affect a bankrupt firm's competitors. He ultimately recommended that, under limited
circumstances, a bankrupt firm’s competitors should receive a claim in the bankruptcy
proceedings in order to compensate them for the losses suffered due to the generous provisions
of the bankruptcy code.
5 Using a different time period, Barla and Koo (1999) also found that airlines in Chapter 11 lowered their prices once bankruptcy was declared. Rivals of a Chapter 11 airline reacted to the bankruptcy by lowering their prices even further.
10
Based on the above discussion, my main hypothesis is:
After a firm’s emergence from bankruptcy, its industry rivals suffer a net
competition effect, or lower stock prices and a deterioration of financial
performance for industry rivals.
III. Data and Method
A. Identification of Emergence Events
A list of 323 firms emerging from bankruptcy from 1999-2007 is collected from
www.bankruptcydata.com, which specializes in collecting bankruptcy data and news. The source
contains the bankruptcy date, plan confirmation date, formal emergence date, and whether the
bankruptcy is prepackaged.
For each of the firms emerging from bankruptcy, I compile financial information
including total assets, sales, and operating income from COMPUSTAT. All of the emerging
firms in the sample have at least one industry peer firm with the same four-digit SIC code as the
emerging firm. Firms in the industry sample need to have financial information on
COMPUSTAT as well as equity returns data available for the [250, 200] period around the
emergence date and the plan confirmation date in the CRSP data files. These restrictions reduce
the sample to 296 events over the period from 1999-2006. Assuming that the emergence of a
small company has a negligible effect on industry peers, I require the emerging firms in the
sample to have total assets of greater than $50 million.6 These requirements produce a final
sample of 264 events.
6 There is no such size restriction on the firms in the industry peer group.
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Panel A of Table I presents the distribution of events, industries, total assets of emerging
firms, and the number of months in bankruptcy by year. The sample includes firms from diverse
industries, with 264 announcements covering 124 four-digit SIC industries. The mean book
value of total assets is $14.12 billion. The average duration in bankruptcy is 23.56 months, with a
median of 17.04 months. This is close to the duration (average of 22.39 months and median of
20.17 months) in the sample spanning from 1980-1989 used by Eberhart et al. (1999).
Insert Table I about here.
B. Construction of Industry Portfolios
The first purpose of this study is to examine the abnormal changes in equity returns for
industry competitors. For each reorganized firm, an industry portfolio is constructed as a value-
weighted portfolio of firms satisfying the following conditions. Each firm in the industry
portfolio must have: 1) the same four-digit SIC code as the “event” firm, and 2) stock returns for
the [250, 200] days in the CRSP Daily database and the [-24, 24] months in the CRSP monthly
database around the event dates. If there is more than one firm from the same four-digit SIC
industry emerging from bankruptcy, these firms are excluded from each other’s industry
portfolio.
Panel B reports the number of industry peer firms within each industry portfolio by year.
On average, there are 33 firms in the industry portfolio, with a median of 16, a maximum of 302,
and a minimum of 1.
C. Measures of Short-Term Industry Stock Market Valuations (Market Model)
12
A standard short-term event study is conducted for industry portfolios around the
emergence event based on the market model. Two event dates are used. The first is the
emergence announcement date that the reorganization plan becomes effective, and the second is
the plan confirmation date that the bankruptcy court approves the reorganization plan. Because
the formal emergence date is preceded by the plan confirmation date, I conduct the event study
around the plan confirmation date to capture the short-term stock price impact of the anticipated
conclusion of the bankruptcy on the rest of the industry. Both the plan confirmation date and the
plan effective date are obtained from www.bankruptcydata.com. For each announcement, I
perform event tests on the value-weighted industry portfolio returns. The abnormal return is the
deviation of the stock return from a contemporaneous expected return generated by a market
model. The market is proxied by the value-weighted CRSP equity returns. The market model’s
parameters are estimated over a 200 trading day estimation period ending 50 days prior to the
announcement. The window is defined as [ 1T , 2T ]. Abnormal returns (ARs) for each day of the
[1, 1] event window and cumulative abnormal returns (CARs) for the [1, 1] event window and
the [1, 200] event window are computed and then aggregated over all events in the sample. Tests
of significance follow the procedure described in MacKinlay (1997).
The abnormal returns can be aggregated over all N events in the sample:
1 1
1 1( )
N N
t it it i i m ti i
A R A R R RN N
(1)
D. Long-Term Event Study Methods
13
There is much discussion in the literature regarding long-term event study methods. In
long horizon tests, appropriate adjustment for risk is critical in calculating abnormal equity
returns. Two main methods for assessing and calibrating post-event risk-adjusted performance
are usually used: 1) the Book-to-Market and Size-Matched (BMSM) model (Barber and Lyon,
1997) and 2) the calendar-time portfolio approach (Fama, 1998; Mitchell and Stafford, 2000).
Fama (1998) and Mitchell and Stafford (2000) argue that event-time returns, which are used by
the BMSM model, are an inappropriate metric for computing long-term abnormal returns. Event-
time returns have a cross-sectional dependence problem that biases the standard error downwards.
Barber and Lyon (1997), however, show that the arithmetic summation of returns, as is done
with calendar-time returns, does not precisely measure investor experience. Lyon, Barber, and
Tsai (1999) demonstrate that the calendar-time method is generally mis-specified in non-random
samples. Loughran and Ritter (2000) argue that the calendar-time return metric has low power.
Eberhart, Maxwell, and Siddique (2004) and Kothari and Warner (2004) review the literature
and compare these two approaches. They contend that there is still no clear winner in this horse
race. Therefore, I use both methods to test the main hypothesis.
1. Book-to-Market and Size-Matched (BMSM) Model
To test long-term abnormal equity returns, Barber and Lyon (1997) suggest that the
market model is subject to the issue of mean-reversion. Following Eberhart et al. (1999), I
estimate expected returns using book-to-market and size-matched (BMSM) firms as a
benchmark. The matching method is applied to each firm in the industry portfolio. First, each
stock is assigned to size deciles based on the market value of equity at the end of June each year.
Fourteen size deciles are formed based on equity capitalization. Next, I choose the firm in the
14
same size decile as the firm in the industry portfolio that has the closest book-to-market ratio,
which is calculated as the most recent book value of equity at the end of December divided by
the market value of equity at the end of December. This firm is called the book-to-market and
size-matched (BMSM) firm. Then a value-weighted “matching” portfolio is constructed with the
BMSM matched firms.
Next, I test whether the industry portfolio exhibits abnormal returns from the event date
through a 200-day holding period above the returns for the BMSM matching portfolio. I define
the 200-day cumulative abnormal return for stock i that starts at the beginning of the event day t
as:
1
,
1
, )1()1(),(
Ht
tjjb
Ht
tjjii RRHtCAR (2)
where Ri,t is the return for industry portfolio i in Day t, and Rb,t is the return in Day t for the
matching portfolio b. To compare with the results of Eberhart et al. (1999), I choose 200 days as
the long-run performance horizon H.7 The average of CAR is computed across 264 events.
N
iiCAR
NACAR
1
1 (3)
2. Calendar-Time Portfolio Approach
Due to the concerns that event time returns have a cross-sectional dependence problem
that biases standard errors downward, I also use the calendar-time portfolio approach to test the
7 For robustness, long run performance for two years is also tested. Results are qualitatively similar.
15
long horizon industry effect. Following Eberhart et al. (2004), I use the Fama and French (1993)
three-factor model to test for long-term abnormal stock returns as demonstrated in the equation
below:
ptttftmtftpt hHMLsSMBRRbaRR )( (4)
where ptR is the average of value-weighted industry portfolio returns in calendar month t (where
an industry portfolio is included if month t is within the T-month period (T=12 and 24) following
the emergence date of a firm within its four-digit SIC industry), ftR is the 1-month T-bill return,
mtR is the CRSP value-weighted market index return, tSMB is the return on a portfolio of small
stocks minus the return on a portfolio of large stocks, and tHML is the return on a portfolio of
stocks with high book-to-market ratios minus the return on a portfolio of stocks with low book-
to-market ratios.
I also estimate the abnormal stock returns with the Carhart (1997) four-factor model,
where the fourth factor is a momentum factor (i.e., UMD, return on high momentum stocks
minus the return on low momentum stocks) included as an additional risk factor.
pttttftmtftpt mUMDhHMLsSMBRRbaRR )( (5)
The intercept (a) in the above two equations is the monthly abnormal equity return
measure. The standard errors are corrected for heteroskedasticity and autocorrelation using the
quadratic spectral kernel as recommended by Andrews (1991).
16
E. Measures of Industry Financial Performance
The final measure of industry effect is the change in industry financial ratios around the
emergence event. This is useful because it potentially improves on the stock market valuation
(which is a forward-looking measure of expected earnings) by employing actual measures of
financial performance over a period of time. Moreover, financial ratios reflect more dimensions
of financial performance, such as cost efficiency, gross profit margin, and overall financial
performance. Accounting measures have been used in previous studies to identify changes in
firm performance following leveraged buyouts, management buyouts, mergers, second-time
IPOs, reverse stock splits, and cross-country non-equity strategic alliances (Brown, Fee, and
Thomas, 2009; Kaplan, 1989; DeLong and DeYoung, 2007; Lian and Wang, 2009; Seoyoung,
Klein, and Rosenfeld, 2008; Chang, Chen, and Lai, 2008).
To measure industry financial performance, I use five financial ratios constructed from
the COMPUSTAT quarterly database. They are 1) Gross Profit Margin (sales minus cost-to-
sales), 2) Operating Income/Sales (operating income before depreciation-to-sales), 3) Operating
Income/Total Assets (operating income before depreciation-to-total assets), 4) ROE (return-on-
equity), and 5) Cost Efficiency (operating income-to-selling, general, and administrative
expenses). Data were collected for all firms in an industry that has a firm emerging from Chapter
11. To control for inter-temporal changes in financial performance caused by the overall
economic environment, I calculate the market-adjusted industry excess financial ratio by
subtracting the median financial ratio among all COMPUSTAT firms from the industry ratio.
The industry ratio for Gross Profit Margin is constructed as (sum of sales for firms in an industry
portfolio – sum of cost of goods sold for firms in an industry portfolio)/(sum of sales for firms in
an industry portfolio). The industry ratios for the other four measures are constructed in a similar
17
fashion. This approach, which essentially size weights the ratio, is adopted to capture the effect
on the industry as a whole.8
Following DeLong and DeYoung (2007), I proceed, in three steps, to measure the long-
term impact of emergence on an industry’s financial performance. First, post-emergence
industry financial ratios are calculated by subtracting the industry excess financial ratios at the
quarter of emergence from the industry excess financial ratios at the eighth quarter (t = 8) after
emergence. Second, pre-emergence industry financial ratios are calculated by subtracting the
industry excess financial ratios at the eighth quarter before emergence (t = 8) from the industry
excess financial ratios at the quarter of emergence. Third, the mean and median differences are
compared between post-emergence and pre-emergence performance, and the percentage of
negative differences is calculated. Owing to the requirement that all industry portfolios have both
post-emergence financial ratios and pre-emergence financial ratios for a pairwise comparison,
the sample for the analysis in this section is reduced to 221 observations.9
IV. Empirical Results
A. Short-Term Industry Stock Response Around a Firm’s Emergence from Bankruptcy
As a starting point, I examine the short run industry stock reaction to the event of firms
emerging from Chapter 11 bankruptcy using the standard event study method (i.e., the market
model). The principal results are presented in Panel A of Table II. The table reports the mean and
median of abnormal returns (AR) for each day of the [1, 1] event window, and cumulative
abnormal returns (CAR) over the three-day event window. The rightmost column reports the
percentage of negative returns.
8 I thank an anonymous referee for suggesting this approach. 9 I also repeat the analysis without imposing this requirement. The results are very similar.
18
Insert Table II about here.
The average three-day abnormal returns for the value-weighed industry portfolio are
0.3% and are significant at the 5% level. The median and percentage of negative returns are
consistent with the mean results. The average short-term negative returns are barely
economically significant, perhaps because emergence from bankruptcy is anticipated by the
market and provides limited additional information in the short term.
In practice, the effective emergence date is preceded by the plan confirmation date when
a firm’s plan of reorganization is approved by the U.S. Bankruptcy Court. Therefore, it is
interesting to examine whether short-term industry effects are stronger around the plan
confirmation date. To this end, I repeat the short-term event study with Day zero defined as the
date of plan confirmation. Results are reported in Panel B of Table II. The average three-day
CARs for the industry peer group are 0.04%, which is not significantly different from zero.
There is no significant industry effect for each day of the [-1, 1] event window around the
confirmation date.
The plan confirmation date appears to carry no short-term information value for industry
peers. One possible explanation is that most emerging firms do not make a formal news
announcement regarding plan confirmation dates. Rather, they announce the news of the
conclusion of bankruptcy when the plan becomes effective. The event of plan confirmation may
thus go unnoticed by market participants. Alternatively, the industry effect may take longer to
realize, which is why the paper later focuses on the long-term stock price responses and financial
performance of the industry peer group.
19
My subsequent analysis uses the effective emergence date as the event date (Day 0).
Because short-term industry effects around a firm’s emergence may vary significantly due to
industry or firm characteristics, as suggested by Lang and Stulz (1992) and Jorion and Zhang
(2007), I estimate cross-sectional regressions where the dependent variable is the three-day CAR
around the event date. The model is:
jjj
jjjjj
PREPACKAGEDUR
OPSALESLTAINDRTGHERFCAR
65
43210
where:
CAR is the dependent variable, defined as the cumulated abnormal stock returns for the
industry portfolio for the [1, +1] daily interval around the emergence event from a
market model,
HERF is the average industry Herfindahl index over the previous four quarters computed
as the sum of the squared fractions of each individual firm’s sales over total sales in the
industry (higher values mean more concentrated industries),
INDRTG is the industry average S&P ratings, with 2 indicating AAA and 24 indicating
C,
LTA is the natural logarithm of total assets of the firm emerging from bankruptcy,
OPSALES is the ratio of operating income before depreciation over sales proxying for
the profitability of the firm emerging from bankruptcy,
DUR is the natural logarithm of days between the bankruptcy date and the emergence
date, and
20
PREPACKAGE is a dummy variable that equals one if the firm’s Chapter 11 is a
prepackaged bankruptcy, and zero otherwise.
HERF and INDRTG are two measures of industry characteristics. Positive spillovers are
expected to be greater for industries that are less concentrated or that have a low Herfindahl
index. Rival firms are more likely to be hurt by the emergence of a competitor that dominates the
industry. As a result, the coefficient on HERF should be negative. Next, INDRTG is the average
rating of the industry portfolio. Industries with lower credit quality (greater INDRTG) are
expected to be more fragile and vulnerable to the competition effect. Therefore, the coefficient
on INDRTG should be negative.
LTA and OPSALES are two company-specific variables. LTA measures the size of the
emerging firm. The emergence of a larger firm may signal a reinvigorating industry and thus
generate positive spillover for the whole industry. Alternatively, the return to normal operations
of a large firm should be a large threat to competitors. Thus, the sign could be positive or
negative. Next, operating performance measure, OPSALES, is used to measure the profitability
of the firm. Presumably, the emergence of a “good” firm in terms of profitability and efficiency
should have a stronger competition effect on its industry peers. A negative sign is expected on
OPSALES.
Finally, I test whether the characteristics of the emergence event have an impact on
industry effects. The longer a firm was in Chapter 11 bankruptcy, the more protection it enjoyed.
Its emergence should be bad news for competitors and, as such, the sign for DUR is expected to
be negative. Next, a negative sign on PREPACKAGE is expected because a prepackaged
bankruptcy should have a more adverse effect on industry rivals.
21
Panel A and Panel B of Table III present summary statistics and Pearson correlation
coefficients of the main variables, respectively. The average HERF is 0.21, varying from 0.02-
0.95. The industry portfolio has an average rating of 13.86 and a median rating of 14
(corresponding to an S&P rating of B+). The total assets of the emerging firm are $1,906 million,
on average. The average OPSALES is -0.1, consistent with the fact that most companies in
bankruptcy have operating losses. The number of days in bankruptcy is 493 days. Only 3% of all
firms in the sample have a prepackaged bankruptcy. To facilitate interpreting loss of industry
market values later, I also report INDMVEQ, which is the total industry market value in millions
of dollars. The average of INDMVEQ of industry portfolios in the sample is $84.05 billion.
Insert Table III about here.
There are a couple of points worth mentioning regarding Panel B. HERF is positively
correlated with INDRTG. LTA is negatively correlated with DUR, suggesting that it takes longer
for a large firm to emerge from bankruptcy. DUR is negative related to PREPACKAGE as
expected.
The cross-sectional regression results are presented in Table IV. The dependent variable
is the cumulative abnormal stock returns for the industry portfolio from a market model for the
[1, +1] daily interval around the emergence event. The estimates are from an OLS regression
with heteroskedasticity robust t-statistics reported in parentheses. Model 1 does not include year
dummies, Model 2 includes year dummies, and Model 3 includes both year and industry
dummies, which are defined according to the first digit of the SIC industry code.
22
Insert Table IV about here.
As predicted, the coefficients on HERF are negative and significant at the 5% level,
indicating positive spillovers in the less concentrated industries. INDRTG is positive, but barely
significant in Model 1. It is not significant in Models 2 and 3. LTA has positive coefficients, but
is not significant. The OPSALES coefficient is negative and significant. Therefore, a “good”
firm with a higher profitability margin has an adverse effect on its industry rivals. The coefficient
on DUR is negative suggesting that longer bankruptcy protection is bad news for industry
competitors. Finally, the coefficient on PREPACKAGE is negative and significant at the 10%
level. The reduced precision may be due to fewer observations of prepackaged bankruptcies.
Overall, significant effects are in the predicted direction. Results are generally consistent across
all three models.
B. Long-Term Industry Stock Response after a Firm’s Emergence from Bankruptcy
A firm in Chapter 11 bankruptcy usually has shed its debt, improved its balance sheet,
obtained more loans or credits, and reduced its labor costs. Because a fight for market share
among rival firms is fierce given the “white-hot” competition in today’s business world, a
healthier emerging firm would pose a new threat to other firms in the industry. Presumably, it
may take time for the emerging firm to realize its advantages and for its industry peers to suffer
from the tougher competition.
Therefore, I attempt a longer term investigation of industry effects. The results based on
the market model provide preliminary evidence for my argument. As shown in the last row of
Panel A of Table II, the cumulative abnormal returns on equity for the industry portfolio during
23
the 200 days after the announcement are -6.07%, with a t-statistic of -4.15. The median is -4.50
for the full sample of 264 events.
1. Book-to-Market and Size-Matched (BMSM) Model
As argued by Barber and Lyon (1997), testing long run abnormal equity returns using the
market model is subject to the criticism that stock returns display mean reversion over time.
Therefore, I calculate abnormal returns using the book-to-market and size-matched model
(BMSM). Results are shown in Table V.
Insert Table V about here.
Panel A shows that the 200-day average cumulative abnormal return for the industry
portfolio is -6.7%, with a t-statistic of -2.44. Based on the average of total industry market value
at $84.05 billion, the abnormal returns translate into a significant loss of $5.6 billion (-6.7%
$84.05 billion = $5.6 billion) for equity shareholders for an average industry in the sample over
this period. This result based on the BMSM method is similar in magnitude to that based on the
market model. Thus, in the longer term, the industry portfolio appears to suffer from rather than
benefit from the emergence of a rival firm from bankruptcy.
An alternative explanation for negative returns to the industry portfolio is an industry-
wide slump. If the whole industry experiences negative cash flow news due to lower demand or a
higher cost of raw materials, for example, the industry may suffer low equity returns as well. To
exclude this explanation, I test the long-term abnormal equity returns of the industry portfolio
prior to the emergence announcement using the same matching-firm method. The results indicate
24
that for the [200, 1] window, the industry portfolio typically earns positive returns of 5.83%,
with a t-statistic of 1.81. The median return is 14.68%. Since the reorganization plan usually
predicts the approximate emergence date, which is within a month after the plan is confirmed by
the bankruptcy court, there can be information leakage within the event window [50, 1].
Therefore, abnormal equity returns for the [250, 50] period, which is a cleaner event window,
is examined. In this case, the positive returns are even stronger. The industry portfolio posts
average abnormal returns of 11.11% with a t-statistic of 3.57. The median is 14.75%, with only
37.5% of the sample having a negative abnormal return.
Panel B of Table V compares long-term industry abnormal returns before and after the
emergence of a rival firm. The mean difference of -12.53% for the [1, 200] and [200, 1] period
is significant at the 1% level. The median difference of -16.5% is also significant at the 1% level.
A comparison with the clean window [250, 50] yields a similar result. Thus, the longer term
equity performance of industry peer firms deteriorated dramatically after the rival firm’s
emergence from bankruptcy, consistent with the competition effects hypothesis.
2. Calendar-Time Portfolio Approach
Next, Table VI demonstrates the long-term abnormal equity return test results using the
calendar-time portfolio approach. Panel A presents the coefficient estimates and their p-values
for the intercept (a) and the risk factors (b, s, h) using the Fama and French (1993) three-factor
model. The monthly abnormal equity returns, as captured by the intercept in the equation, are -
0.39% for the [0, 12m] event period and -0.51% for the [0, 24m] event period. Both are
statistically significant at least at the 5% level. This can be translated into annualized post-event
abnormal returns of -4.54% and -5.95%, or significant losses of $3.8 billion (-4.54% $84.05
25
billion = $3.8 billion) and $5.0 billion (-5.95% $84.05 billion = $5.0 billion) for equity
shareholders for an average industry in the sample over a year, respectively. They are similar in
magnitude to the abnormal equity returns of -6.70% for the [1, 200] event window using the
BMSM model. The coefficient estimates for the risk factors are generally consistent with the
estimates reported in previous studies.
Insert Table VI about here.
Panel B of Table VI illustrates the finding using the Carhart (1997) four-factor model.
The results are similar to the three-factor model results reported in Panel A. Specifically, the
intercepts are significant and negative, which are -0.32% for the [0, 12m] event period, and -
0.41% for the [0, 24m] event period. These findings suggest that the main results are robust to
different methods to estimate long-term returns.
Furthermore, the results in Panel A confirm that for the [12m, 0] and [24m, 0] event
windows, the industry portfolio typically earns positive monthly returns of 0.67% and 0.40%.
This can be translated into annualized pre-event abnormal returns of 8.34% and 4.92%,
respectively. The results in Panel B with the Carhart (1997) four-factor model are similar.
Therefore, the post-event industry abnormal returns are substantially lower than the pre-event
industry abnormal returns. The evidence does not support that the low industry returns after a
firm’s emergence are due to a continuation of an industry-wide slump.
C. Cross-Sectional Analysis of Industry Rival's Long-Term Abnormal Equity Returns
26
In this section, I test which industry and firm characteristics can explain the cross-
sectional difference in long-term equity returns to the industry portfolio. The same regression
model as in Table IV is used, with the dependent variable being 200 days post-event abnormal
equity returns based on the BMSM method. The results are presented in Table VII.
Insert Table VII about here.
Consistent with the results in Table IV, HERF has a negative coefficient and is
significant at the 5% level. This supports the hypothesis that firms in a more concentrated
industry are more likely to be negatively affected by competition. The coefficient sign on
INDRTG is negative and significant indicating that an industry with lower credit quality (higher
INDRTG) tends to be hurt badly by the emergence of a firm from Chapter 11. This may occur
because the competitors’ higher leverage reduces their flexibility to compete with the firm that
emerges and makes them more vulnerable to any changes in industry capacity. The bankruptcy
protection may give bankrupt firms comparative competitive advantages. As expected, the filing
firm’s size is not significant in the regression. OPSALES is negatively related to abnormal
returns suggesting that the emergence of a more “efficient” firm is bad news for industry
competitors. However, it loses significance in Model 3. The duration of bankruptcy, DUR, is
negative, but barely significant. PREPACKAGE is not relevant for long-term industry effects.
Notably, the explanatory power of Model 3 increases to 19.66% from 4.47% in Model 1
suggesting that year dummies, and particularly industry dummies, are also relevant when
explaining the different industry effects. However, the significance of major variables, such as
HERF and INDRTG, remains.
27
D. Long-Term Financial Performance in an Industry after a Firm’s Emergence from
Bankruptcy
If a firm emerges from Chapter 11 as a healthier and leaner company, it will strengthen
its financial performance in a number of ways. First, such firms can afford to compete with peer
firms for market share by lowering their products’ prices. Other firms in the same industry may
also cut their prices in order to compete. For example, Ciliberto and Schenone (2008)
investigated the effects of financial distress in the airline industry on ticket prices. They found
that financially distressed firms in the airline industry dropped prices to generate cash, and their
closest competitors were forced to match those price decreases. Lower prices will lead to a lower
gross profit margin and other profitability measures. Thus, lower profitability for industry rivals
are expected after a firm’s emergence from Chapter 11. In addition, marketing and other sales
expenses may increase in the industry of a firm emerging from bankruptcy. This may arise from
tougher market competition between the emerging firm and its industry rivals. Therefore, cost
efficiency for industry rivals is expected to decline after the emergence of a firm from Chapter
11.
The long run change in financial performance is measured by comparing post-
emergence performance with pre-emergence performance for the industry portfolio on five
dimensions of performance: 1) Gross Profit Margin, 2) Operating Income/Sales, 3) Operating
Income/Total Assets, 4) ROE, and 5) Cost Efficiency. Using financial ratios allows one to
analyze actual industry performance from a number of viewpoints and identify sources of
deterioration in industry performance.
28
Panel A of Table VIII presents the mean and median for post-emergence performance
and pre-emergence performance for the five market-adjusted industry excess financial ratios,
where the market is proxied by the median ratio among all COMPUSTAT firms.10 The results
suggest that after controlling for the inter-temporal changes in the economy-wide financial
performance, industry post-emergence performance is significantly worse than industry pre-
emergence performance for all five financial ratios. Specifically, the industry pre-emergence
performance is positive and statistically significant on all five dimensions. In contrast, post-
emergence performance is negative for all five measures and statistically significant for Gross
Profit Margin, Operating Income/Sales, and ROE. The percentage of negative changes is above
50% for all five measures. Overall, the evidence suggests that an average industry suffers
deterioration in these performance dimensions during the two years following the emergence
event.
Insert Table VIII about here.
To formally test whether there are marked differences in pre- and post-emergence
market-adjusted industry performance, I calculate the mean and median differences between
pre-emergence performance and post-emergence performance. For all five financial ratios, the
mean differences are large and significant at least at the 5% levels. Median differences are
smaller in magnitude, but are all statistically significant. This further confirms that for the two
years following a firm’s emergence from Chapter 11 bankruptcy, its industry peer’s financial
performance declines in a variety of ways.
10 I repeat the analysis with the market financial ratio measured by the value-weighted financial ratios among all COMPUSTAT firms. The results are similar.
29
To shed more light on the reasons for the worsening of an industry’s financial
performance, I further calculate the post-emergence excess financial ratio changes for the
emerged firms.11 Following Delong and Deyoung (2007), the emerged firm’s financial ratio is
adjusted by the industry ratio to control for inter-temporal changes in financial performance
caused by industry-wide factors. Each firm’s industry-adjusted financial ratio is obtained by
subtracting the contemporaneous industry ratio from the firm’s ratio.12 This includes a sample of
120 emerged firms that have complete financial information in the [0, +8] post-emergence event
window. As reported in Panel B of Table VIII, the post-emergence financial performance for
the emerged firm is positive and statistically significant for Gross Profit Margin, Operating
Income/Sales, Operating Income/Total Assets, and ROE. Cost Efficiency has a positive mean,
albeit not statistically significant.
Collectively, the post-emergence performance of an industry portfolio exhibits signs of
deterioration. In contrast, the reorganized firm shows some signs of improvement in financial
performance. This comparison lends direct support to the explanation that the emerged firm uses
the advantage conferred by Chapter 11 to exploit its rivals.
V. Conclusion
This paper studies the effects on equity returns and financial performance of industry
rivals when a firm in the same industry emerges from Chapter 11 bankruptcy. The examination
of information spillovers sheds light on the nature of the information conveyed when a firm
emerges. A positive spillover effect would suggest that the emergence event contains industry-
11 I thank an anonymous referee for suggesting this analysis. 12 The industry ratio is calculated using the same method as discussed in Section III.E. As an additional check, I calculate the industry-adjusted ratio of the emerged firm by subtracting off the median industry ratio. The results are similar.
30
wide favorable news. A negative spillover would indicate that the emergence event carries
unfavorable news for industry rivals regarding the competitive structure of the industry. Using
the Book-to-Market and Size-Matched (BMSM) model and the calendar-time portfolio approach,
I find consistent evidence of long-term negative equity returns for industry portfolios following
the emergence of a firm from bankruptcy, supporting the hypothesis that negative competition
effects dominate positive spillover effects. In contrast, significant and positive industry equity
returns are found for the pre-emergence period, a result that does not support the alternative
industry distress explanation.
Furthermore, the cross-sectional analysis adds to the understanding of major industry,
firm, and event factors that contribute to stronger competition effects. Industry competitiveness
and industry credit quality are the two most important determinants of competition effects. In a
concentrated industry and when industry rivals have lower credit quality, equity holders of
competitors tend to be more adversely affected by a firm’s emergence. There is some evidence
indicating that the emergence from bankruptcy of firms with greater operating efficiency and
longer bankruptcy protection has stronger competition effects on industry rivals. A larger firm’s
emergence does not necessarily have negative competition effects.
This paper goes further to present evidence regarding chronically poor financial
performance of industry rivals and improving performance of the reorganized firm after the
firm’s emergence suggesting that the firm exploits its rivals with the advantage granted by
Chapter 11. This investigation could be extended to other micro-level analysis of industry effects
such as product market competition, labor cost management, and marketing and interest expense
cutting that may contribute to the deterioration of industry performance. It seems that Chapter 11
bankruptcies protect reorganized firms at the expense of its industry competitors. Such findings
31
are consistent with the notion of Jensen (1991) that "chronic inefficiencies" arise from certain
features of the reorganization process.
Policy makers should consider the negative industry competition effects arising from
Chapter 11 bankruptcy protection especially for concentrated and low-credit quality industries.
There may be a need to reconsider Chapter 11’s role in promoting competition and the allocation
of resources. The existing view holds that it is effective to allow viable firms to continue
operating because more choices usually enhance competition and benefit consumers. However,
this is true only when the costs of allowing these firms to survive do not outweigh the potential
benefits. In sum, this study provides information that should be useful in evaluating the existing
reorganization law and proposals for Chapter 11 reform.
32
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Table I. Distribution of Firms Emerging from Chapter 11 Bankruptcy This table describes the distribution of the final sample of firms emerging from Chapter 11 bankruptcies by year. The sample runs from 1999-2006 and includes 264 events. Panel A reports the number of firms emerging from bankruptcies, industry coverage in terms of 4-digit SIC code, the average book value of total assets in millions, and the average time spent in bankruptcy (measured as number of months from the bankruptcy announcement date through the emergence date) .
Panel A. Summary Statistics of Events
Year
Number of Firms
Emerging from
Bankruptcy
Number of Industries
Average of Total Assets
(mn)
Average Number of Months in
Bankruptcy
1999 7 7 474 15.53 2000 19 12 515 11.46 2001 26 24 940 13.65 2002 73 48 1,018 18.63 2003 55 37 2,882 23.91 2004 41 32 2,790 30.01 2005 23 21 2,800 36.84 206 20 15 2,706 40.72 Total 264 124 14,125 23.56
Panel B. Number of Peer Firms Within an Industry Portfolio
Year Number of
Events Mean Std. Dev. Min. Median Max.
1999 7 53 58 13 28 173 2000 19 68 59 1 38 188 2001 26 28 45 1 12 180 2002 73 39 56 1 15 302 2003 55 25 39 1 15 260 2004 41 24 27 2 14 93 2005 23 22 19 1 19 73
37
Table II. Short-Term Industry Abnormal Equity Returns around a Firm's Emergence from Chapter 11 Bankruptcy (Market Model)
The table presents the short-term industry effect after a firm emerges from Chapter 11 bankruptcy using the market model. An industry portfolio is a value-weighted portfolio of all other COMPUSTAT firms within the same 4-digit SIC code, for which equity returns are available. Panel A and B present the abnormal equity returns for the industry portfolios around the emergence effective date and the reorganization plan confirmation date, respectively. Reported are the mean and median of abnormal equity returns (AR) and cumulative abnormal returns (CAR), and the percentage of negative AR (CAR) for the industry portfolios across 264 events in the final sample. AR(CAR) is the market adjusted cumulative abnormal returns (in percent) for the industry portfolio, defined from a market model estimated over the (-250, -50) day interval. The market return is proxied by the CRSP value-weighted equity index.
Panel A. Abnormal Equity Returns of Industry Portfolio (Day Zero is the Emergence Effective Date).
Day Mean (%) t Median Percentage (<0)-1 0.19 2.04** 0.17 43.6 0 -0.05 -0.54 -0.08 53.4 1 0.16 1.69* 0.00 51.5 [-1, 1] 0.30 1.95** 0.16 46.2 [1, 200] -6.07 -4.15*** -4.50 55.3
Panel B. Abnormal Equity Returns of Industry Portfolio (Day Zero is the Plan Confirmation Date)
Day Mean (%) t Median Percentage (<0)-1 0.05 0.46 -0.09 51.8 0 -0.05 -0.60 -0.13 52.1 1 0.04 0.47 0.01 49.6 [-1, 1] 0.04 0.22 0.08 48.3 [1, 200] -3.91 -1.98** -3.51 53.1
The superscripts ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
38
Table III. Descriptive Statistics of Main Variables This table provides descriptive statistics for cross-sectional variables (N=264). Panel A reports the summary statistics for the main variables, and Panel B presents the Pearson Correlation Coefficients. HERF is a measure of industry concentration level with higher value indicating a more concentrated industry; INDRTG is the industry average S&P ratings with 2 indicating AAA and 24 indicating C; TA is the book value of total assets of the firm emerging from bankruptcy for the quarter before its bankruptcy filing in millions of dollars; OPSALES is the ratio of operating income before depreciation over sales for the quarter before its bankruptcy filing, proxying for the profitability of the firm emerging from the bankruptcy; DUR is the natural logarithm of days between the bankruptcy date and the emergence effective date; PREPACKAGE is a dummy variable that equals one if the firm’s Chapter 11 is a prepackaged bankruptcy, and zero otherwise; and INDMVEQ is the total industry market value of equity in millions of dollars.
Panel A. Summary Statistics of Main Variables Variable Mean Std. Dev. Min. Median Max.
HERF 0.21 0.15 0.02 0.17 0.95 INDRTG 13.9 2.5 7.0 14.0 24.0 TA (mn) 1,960.0 5,975.0 55.4 498.4 61,783.0 OPSALES -0.06 0.71 -6.22 0.00 6.44 DUR 6.2 0.9 3.6 5.9 7.5 PREPACKAGE 0.03 0.18 0 0 1 INDMVEQ (mn) 84,045.3 126,148.5 53.4 23,113.5 631,191.7
Panel B. Correlation Table HERF INDRTG LTA OPSALES DUR INDRTG 0.1757***
(0.0042) 1.000
LTA -0.0222 (0.7195)
-0.0136 (0.826)
1.000
OPSALES -0.0217 (0.7254)
-0.0030 (0.9617)
0.0737 (0.2329)
1.0000
DUR 0.0496 (0.422)
0.0156 (0.8015)
0.1555** (0.0114)
0.0252 (0.6838)
1.0000
PREPACKAGE 0.0099 (0.873)
0.0408 (0.5096)
-0.0026 (0.9668)
0.0101 (0.8697)
-0.3492*** (<.0001)
The superscripts ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
39
Table IV. Cross-Sectional Analysis of Industry Rival's Short-Term Abnormal Equity Returns This table presents the coefficient estimates of cross-sectional regressions for the short-term industry abnormal equity returns for a sample of 264 events. The dependent variable is the cumulative abnormal stock returns for the industry portfolio from a market model for the [-1, +1] daily interval, where Day 0 is the firm emergence date. LTA is the natural logarithm of TA. Definitions of other independent variables are the same as in Table III. The estimates are from an OLS regression with heteroskedasticity robust t-statistics reported in parentheses. Model 1 does not include year dummies. Model 2 includes year dummies. Model 3 includes both year dummies and industry dummies, where industry dummies are defined with the first digit of the SIC code.
Independent Variable
Expected Sign Model 1 Model 2 Model 3
Coefficient(t-stat.)
Coefficient (t-stat.)
Coefficient(t-stat.)
Constant
0.83 (0.55)
0.84 (0.48)
-0.45 (-0.14)
HERF -
-2.34 (-2.31)**
-2.14 (-2.10)**
-1.84 (-1.59)
INDRTG -
0.10 (1.71)*
0.09 (1.47)
0.10 (1.48)
LTA +/-
0.01 (0.04)
0.04 (0.32)
0.07 (0.51)
OPSALES -
-0.48 (-2.20)**
-0.52 (-2.41)**
-0.51 (-2.25)**
DR -
-0.26 (-1.32)
-0.24 (-1.16)
-0.23 (-1.07)
PREPACKAGE -
-1.61 (-1.80)*
-1.70 (-1.83)*
-1.75 (-1.84)*
R-square (%) 5.84 8.99 9.95 R-square adj. (%) 3.64 4.26 1.73 P-value for F-stat 0.0162 0.0306 0.2396 Year Dummies No Yes Yes Industry Dummies No No Yes
The superscripts ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
40
Table V. Long-Term Industry Abnormal Equity Returns before and after a Firm's Emergence from Chapter 11 Bankruptcy (BMSM Approach)
This table presents long-term abnormal equity returns for the industry portfolio for the sample of 264 emergence events using the book-to-market and size-matched (BMSM) model. The BMSM model calculates the abnormal equity returns for an industry portfolio in excess of the returns for a value-weighted matching portfolio constructed with BMSM matched firms. The BMSM matched firm is in the same size decile as the firm in the industry portfolio and has the closest book-to-market ratio. Panel A reports the mean and median of CAR and the percentage of negative CAR for industry portfolio before and after the emergence event. Panel B compares long-term industry effects before and after the emergence event. The pairwise t-test and two-tailed Wilcoxon Signed Rank test are used to test for statistical significance of mean and median differences, respectively.
Panel A. Abnormal Equity Returns for Industry Portfolio Event Period Mean (%) t Median Percentage (<0)
[1,200] -6.70** -2.44 -1.75 51.5 [-200, -1] 5.83* 1.81 14.68 42.8 [-250, -51] 11.11*** 3.57 14.75 37.5
Panel B. Comparisons of Long-Term Industry Effects Before and After a Firm's Emergence from Chapter 11 Bankruptcy CAR [1,200] vs. CAR [-200, -1] CAR [1,200] vs. CAR [-250, -51] Mean Median Mean Median Difference -12.53 -16.5 -16.50 -16.43 t Statistic (-4.29)*** (-2.96)*** Wilcoxon Statistic (-4.37)*** (-3.41)***
The superscripts ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
41
Table VI. Long-Term Industry Abnormal Equity Returns before and after a Firm's Emergence from Chapter 11 Bankruptcy (Calendar-Time Portfolio Approach)
The table presents long-term abnormal equity returns for the industry portfolio for the sample of 264 emergence events using the calendar-time portfolio model. Panels A and B report the coefficient estimates where the abnormal equity returns are estimated using the Fama and French (1993) three-factor model and the Carhart (1997) four-factor model, respectively. In the equations below, Rpt is the average raw industry portfolio returns in calendar month t (where an industry portfolio is included if month t is within the T-month period (T=12, 24, -12, -24) following the emergence date of a firm within its 4-digit SIC industry), Rft is the one-month T-bill return, Rmt is the CRSP value-weighted market index return, SMB is the return on a portfolio of small stocks minus the return on a portfolio of large stocks, HML is the return on a portfolio of stocks with high book-to-market ratios minus the return on a portfolio of stocks with low book-to-market ratios, and UMD is the return on high momentum stocks minus the return on low momentum stocks. The intercept (a) is the abnormal return measure. The standard errors are corrected for heteroskedasticity and autocorrelation using the quadratic spectral kernel as recommended by Andrews (1991). The p-values are reported in parentheses below each coefficient estimate. Fama French three-factor model: Carhart four-factor model:
Panel A. Fama and French (1993) Three-Factor Model
Event Period Intercept b s h [0, 12m] -0.386 0.978 0.433 0.425 p-value (0.036) (0.000) (0.000) (0.000) [0, 24m] -0.510 0.967 0.444 0.440 p-value (0.005) (0.000) (0.000) (0.000) [-12m, 0] 0.666 1.154 0.511 0.421 p-value (0.006) (0.000) (0.000) (0.000) [-24m, 0] 0.401 1.146 0.592 0.421 p-value (0.019) (0.000) (0.000) (0.000)
Panel B. Carhart (1997) Four-Factor Model Event Period Intercept b s h m
[0, 12m] -0.322 0.914 0.479 0.410 -0.110 p-value (0.019) (0.000) (0.000) (0.000) (0.003) [0, 24m] -0.414 0.899 0.487 0.413 -0.121 p-value (0.015) (0.000) (0.000) (0.000) (0.004) [-12m. 0] 1.032 0.966 0.641 0.341 -0.369 p-value (0.000) (0.000) (0.000) (0.000) (0.000) [-24m, 0] 0.521 0.972 0.697 0.336 -0.397 p-value (0.002) (0.000) (0.000) (0.001) (0.000)
ptt t ftmtftpt hHMLsSMBRRbaRR )(
pttt tftmtftpt mUMDhHML sSMBRRbaRR )(
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Table VII. Cross-Sectional Analysis of Industry Rival's Long-Term Abnormal Equity Returns This table presents the coefficient estimates of cross-sectional regressions for the long-term industry abnormal equity returns for a sample of 264 events. The dependent variable is the cumulative abnormal stock returns for the industry portfolio from the BMSM model for the [1, 200] daily interval around the emergence event. The definitions of independent variables are the same as in Table III. The estimates are from an OLS regression with heteroskedasticity robust t-statistics reported in parentheses. Model 1 does not include year dummies. Model 2 includes year dummies. Model 3 includes both year dummies and industry dummies, where industry dummies are defined with the first digit of the SIC code.
Independent Variables
Expected Sign Model 1 Model 2 Model 3
Coefficient(t-statistic)
Coefficient(t-statistic)
Coefficient(t-statistic)
Constant 59.57
(2.25)** 83.15
(2.73)*** -94.40
(-1.88)*
HERF - -44.56
(-2.49)*** -49.25
(-2.75)*** -45.42
(-2.44)**
INDRTG - -1.89
(-1.72)* -2.63
(-2.35)** -2.93
(-2.80)***
LTA +/- 0.60
(0.28) 0.47
(0.21) 1.24
(0.60)
OPSALES - -7.43
(-1.95)** -7.17
(-1.89)* -5.40
(-1.50)
DUR - -6.09
(-1.78)* -5.54
(-1.50) -5.54
(-1.58)
PREPACKAGE - -18.81 (-1.19)
-14.15 (-0.87)
-21.73 (-1.42)
R-square (%) 7.01 11.88 26.38 R-square Adj. (%) 4.47 6.92 19.66 P-value for F-stat 0.0089 0.0037 <.0001 Year Dummies No Yes Yes Industry Dummies No No Yes
The superscripts ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
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Table VIII. Long-term Financial Performance Before and After a Firm's Emergence from Chapter 11 Bankruptcy
This table presents long-term financial performance for the industry portfolio and the reorganized firm. Panel A consists of 221 industries with complete financial information for eight quarters before and after the emergence event. I proceed in three steps. First, industry post-emergence excess financial ratios are calculated as the difference between the industry excess financial ratios at the eighth quarter (t=8) after emergence and the industry excess financial ratios at the quarter of emergence. Second, industry pre-emergence excess financial ratios are calculated as the difference between the industry excess financial ratios at the quarter of emergence and the industry excess financial ratios at the eighth quarter before emergence (t=-8). Third, the mean and median differences are compared between post-emergence and pre-emergence performance using pairwise t-tests and two-tailed Wilcoxon Signed Rank test for statistic significance. Percentage (<0) reports the percentage of negative differences in financial ratios between pre-emergence change and post-emergence changes. Panel B presents the mean, median, t-statistics, and the percentage of negative values of industry-adjusted excess financial ratios for a sample of 120 reorganized firms that have complete financial information for the [0, 8q] event window. The industry excess financial ratio in Panel A is defined by subtracting the median financial ratio among all COMPUSTAT firms from the industry ratio. The excess financial ratio of the emerged firm in Panel B is defined by subtracting the industry ratio from the ratio of the emerged firm. The financial performance variables are defined for industry portfolios. Gross Profit Margin is the ratio of sales minus cost of goods sold over sales; Operating Income/Sales is the ratio of operating income before depreciation over sales; Operating Income/Total Assets is the ratio of operating income over total assets; Return on Equity is the ratio of operating income over total equity; and Cost Efficiency is the ratio of operating income over Selling, General, and Administrative Expenses (SGA).
Panel A. Long-Term Financial Performance of the Industry Gross Profit Margin Mean Median t Percentage (<0)
∆Pre-emergence [-8q, 0) 1.15 -0.06 1.97** ∆Post-emergence [0, 8q] -1.49 -0.40 -2.54*** Difference -2.64 -0.34 53.39
t-statistic (-3.19)*** Wilcoxon Statistic (-1.72)*
Operating Income/Sales ∆Pre-emergence [-8q, 0) 2.19 0.37 3.82*** ∆Post-emergence [0, 8q] -0.82 -0.45 -2.11** Difference -3.01 -0.82 61.54
t-statistic (-4.35)*** Wilcoxon Statistic (-3.13)***
Operating Income/Total Assets ∆Pre-emergence [-8q, 0) 0.51 0.36 4.50*** ∆Post-emergence [0, 8q] -0.02 -0.18 -0.27 Difference -0.53 -0.54 62.90
t-statistic (-3.77)*** Wilcoxon Statistic (-4.13)***
Return on Equity ∆Pre-emergence [-8q, 0) 2.30 0.96 4.04*** ∆Post-emergence [0, 8q] -0.78 -0.56 -2.21** Difference -3.08 -1.52 60.63
t-statistic (-4.60)*** Wilcoxon Statistic (-4.98)***
Cost Efficiency ∆Pre-emergence [-8q, 0) 19.59 2.84 2.01** ∆Post-emergence [0, 8q] -7.95 -1.61 -1.26 Difference -27.54 -4.45 59.73
t-statistic (-2.37)** Wilcoxon Statistic (-1.75)*
Panel B. Long-Term Financial Performance of the Emerged Firm [0, 8q] ∆Post-emergence [0,8q] Mean Median t Percentage (<0)
Gross Profit Margin 4.76 1.42 2.44** 39.17 Operating Income/Sales 4.51 0.16 2.23** 44.17 Operating Income/Total Assets 1.23 0.53 2.76*** 35.83 Return on Equity 7.83 2.63 2.72*** 33.33 Cost Efficiency 13.77 6.49 0.97 44.17
The superscripts ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.
