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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: zhangga@umsl.edu

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).

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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,

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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

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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,

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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

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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.

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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.

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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

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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.

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Panel A of Table Ipresents 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.

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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 [T1,T2]. 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 t i i

A R

A R

R

R

N

N

(1)

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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.

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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 cumulativeabnormal return for stock i that starts at the beginning of the event day t

as:

1 , 1 , ) (1 ) 1 ( ) , (           t H t j j b H t t j j i i t H R R CAR (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 i i CAR N ACAR 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

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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: pt t t ft mt ft pt R a b R R sSMB hHML R    (  )   (4)

where Rpt 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), Rft is the 1-month T-bill return,

mt

R is the CRSP value-weighted market index return, SMBt is the return on a portfolio of small

stocks minus the return on a portfolio of large stocks, and HMLt is the return on a portfolio of stocks with high 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.

pt t t t ft mt ft pt R a b R R sSMB hHML mUMD R    (  )    (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).

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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-cost-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

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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.

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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.

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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:

j j j j j j j j PREPACKAGE DUR OPSALES LTA INDRTG HERF CAR                 6 5 4 3 2 1 0 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

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 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.

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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

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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

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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

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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

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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.

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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.

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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.

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Panel A of Table VIII presents the mean and median for post-emergence performance and pre-emergence performancefor 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.

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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.11Following 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-emergenceperformance of an industry portfolioexhibits 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

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

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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

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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.

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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

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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.

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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.

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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.

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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.

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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) pt t t ft mt ft pt R a b R R sSMB hHML R    (  )   pt t t t ft mt ft pt R a b R R sSMB hHML mUMD R    (  )   

References

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Deleting an image using the sorl.thumbnail.ImageField will notify the Key Value Store to delete references to it and delete all of its thumbnail references and files, exactly like

The empirical model to be estimated in this paper makes use of a panel data set for Brazilian banks to implement the two-step approach described in the previous section. The vector

5 &#34;Offline vs. Online Shopping Boundaries Disappear for the Consumer,&#34; McKinsey &amp; Company, January