Thesis Seminar Finance
Research Question:
Which firms use pre-packaged bankruptcies in the US?
Name: Iliyana Nikolova Supervisor:
This document is written by Iliyana Nikolova who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it. The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
Abstract: The characteristics of US companies are examined to determine the relationship between them and the choice of reorganization. Moreover, the company performance after filing for bankruptcy is investigated and its relationship with the specific option for capital restructuring. Both of these are studied with the consideration of the introduction of the new law in the US Bankruptcy Code: The Bankruptcy Abuse Prevention and Consumer Protection Act of 2005. Hence, the relevant periods compared are between 2000 and 2005 and 2005 through 2010. The results show that BAPCPA greatly influenced the number of firms to file for bankruptcy, it also achieved its aim to reduce the duration of time spent in bankruptcy. However, with the rates of refiling consequently rising.
Keywords: bankruptcy, BAPCPA, Chapter 11, company, pre-packaged bankruptcy
- 1 - Contents
1. Introduction...-2-
2. Bankruptcy Abuse Prevention and Consumer Protection Act (BAPCPA) of 2005…..-4-
3. Literature Review...-6-
4. Hypotheses...-8-
5. Methodology...-9-
6. Data and Descriptive Statistics...-11-
6.1 Data...-11-
6.2 Descriptive Statistics...-11-
6.3 Summary Statistics: Hypothesis 1...-13-
6.4 Summary Statistics: Hypothesis 2...-15-
7. Analysis...-16- 7.1 Hypothesis 1...-16- 7.2 Hypothesis 2...-18- 8. Robustness Check...-19- 8.1 Hypothesis 1...-19- 8.2 Hypothesis 2...-21- 9. Conclusion...-22- 10. References...-24- 11. Appendix...-26-
- 2 - 1. Introduction
Bankruptcy and the right to declare for it are crucial not only for a company but for a country. The evidence being the state of Puerto Rico, a US unincorporated territory. A year ago, it came to light that Puerto Rico is in a huge debt crisis and it is unable to repay it. To be more precise, it owes its creditors around $70 billion. It all started when the US government exempted US companies with operations in Puerto Rico from paying income corporate taxes. Thus, because of this 936 Section of the US tax code, Puerto Rico became a fast-growing economy and an attractive place for investors and US companies to locate their subsidiaries there. This, in turn, led to the economy relying more on foreign investment than on domestic. Hence, having high corporate taxes for domestic companies to offset the low ones for the US ones. However, with the repeal of this Section in 2006, this led to a recession due to a lack of foreign investment and inability of domestic companies to match the consequent losses.
Moreover, another reason for the deepening of the economic decline were the “triple tax-exempt” bonds, where these financial instruments were exempted from state, country and federal taxes. Thus, making them very attractive to all investors and sustaining this characteristic even after 2006. Investors kept on buying them and the Puerto Rican national debt rose significantly. This leads us to recent months, when the government should have introduced laws and implemented strategies on improving the state of its economy. To start with, having been able to declare bankruptcy and restructure its debt. In theory, this could have been done through filing for Chapter 9, part of the US Bankruptcy Code that allows local governments to reorganize its debt. However, due to an amendment in 1984, Puerto Rico was excluded from Chapter 9.
Today, instead of reorganizing, Puerto Rico is being sued and is facing future lawsuits due to its inability to repay the principals and the interests on these bonds. Furthermore, the government is also introducing nation-wide cuts in education and health care and higher VAT charges, in order to reduce spending and raise tax revenue. It also faces increasing emigration rates as people move to mainland US. Last but not least, most of these cheap bonds are held by “vulture” funds, funds looking to invest in poorly performing companies or countries, so as to make quick profit by getting what they are owed by taking legal action against these entities.
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A severe problem such as this, impacts not only creditors and the government, but the ordinary people who live there. They have to struggle with job losses, significantly worse quality of healthcare and education, flight of skilled workers, and even public unrest. Hence, the case of Puerto Rico shows how important it is for a company or a country to have the appropriate tools to deal in case of a bankruptcy, to have an opportunity for re-organization and plan the right way to get back on its feet.
Two of these tools can be filing for Chapter 11 or restructuring through a pre-packaged bankruptcy. According to Chatterjee et al. (1996), the company’s restructuring is related to 4 factors: how much the firm is leveraged, how severe its liquidity crisis is, if there is creditor coordination problem, and how much the firm is in economic distress.
Under the US Bankruptcy Code, when a company files for Chapter 11 it is allowed to restructure its debt instead of liquidating its operations. According to Annabi et al (2012), the Chapter 11 re-organization provides the company with some breathing space from its creditors while it decides on a plan for dealing with its debt. This happens through negotiations across claimholders in separate bargaining rounds, where every group of claimholders has to submit a restructuring plan. The first plan is provided by the management within 180 days. If it is approved by all of the classes of claimholders, then it is consequently accepted by the judge. If, on the other hand, the plan is not approved by all claimholders, then another class takes turn in proposing a plan. Additionally, if this procedure takes too long and is too expensive, then the judge has the right to invoke a cramdown plan. In the end, the company is facing liquidation only in the case, where consensus between all claimholders has not been reached.
Moreover, another choice could be the pre-packaged bankruptcy, which can be seen as a hybrid between Chapter 11 and an out-of-court workout (Tashjian et al 1996), where the latter results in the firm agreeing on a reorganization plan with its creditors out of court. To elaborate, in a prepack the debtors and creditors have decided on the restructuring plan out-of-court, but it still needs to be approved by the court. Hence, the company still needs to file for a Chapter 11.
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However, most of the literature has focused primarily on Chapter 11. There is not so much written on the firm characteristics which determine if a company uses a prepack. In addition, the periods that have been looked at are mainly in the 1980s and 1990s, there is much less evidence for the 2000s and especially after the introduction of the Bankruptcy Abuse Prevention and Consumer Protection Act of 2005. Also, whether the company has an improved post-bankruptcy performance compared to a traditional Chapter 11.
In this thesis, some of these questions are going to be tackled in order to gain more knowledge on the firms’ reactions in the midst of bankruptcy. This can prove helpful for investors and creditors, to make an informed decisions about whether or not investing in a particular company is the right choice. Thus, this will lead to lower costs for debtors and creditors, and the society as a whole. Additionally, the Puerto Rico case presented earlier applies not only to countries but to companies as well. The failure of one company can lead to a downward spiral of other bankruptcies as was the case in the financial crisis. Due to the globalisation and interconnectedness of countries and companies, it is clear how fragile economies and financial markets are. Hence, the topic of bankruptcies has never been more relevant and will continue to be so.
There are the following sections: Section 2 looks at the Bankruptcy Abuse Prevention and Consumer Protection Act and how it affects bankruptcy filings, Section 3 presents the relevant literature review, in Section 4 the hypotheses are formulated. In addition, Section 5 exhibits the methodology implemented, Section 6 discusses the procurement of data and analyses the summary statistics. Finally, in Section 7 the results are presented, followed by Section 8, in which robustness checks are undertaken to verify the results, and Section 9, which summarises the main findings and includes suggestions to be considered in future research.
2. Bankruptcy Abuse Prevention and Consumer Protection Act (BAPCPA) of 2005
The recent change in the US Bankruptcy Code, also known as Bankruptcy Abuse Prevention and Consumer Protection Act (BAPCPA) of 2005, could have an effect on the firm’s capital structure and its creditors. It came into effect on 17th October, the same year. The law was mainly aimed at consumers but it had an impact on businesses as well. To elaborate, its purpose was to make it harder for debtors who filed for Chapter 11 to extend their exclusivity
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period (where exclusivity period refers to the right of a company to more time to establish a plan of reorganization) (Teloni 2015). Hence, the new law strives to be more creditor friendly. Mazur (2015) finds that this characteristic of the new law can have an effect on non-bankrupt companies, making them more disciplined. In other words, these firms prefer to invest more when there is higher demand or divest in times of lower demand.
As observed by Bak et al (2008), the bankruptcy filings due to the change in the US Bankruptcy Code in October 2005 were numerous because consumers and enterprises could not evaluate the direct consequences of the new law. However, they were followed by a considerable reduction in filings. The authors find that the changes in the bankruptcy law are significant and estimate that the resulting fall in filings could also be due to the higher fees.
Moreover, Coehlo (2010) finds in his research that there are a lot of similarities between companies’ reactions before the introduction of the BAPCPA and the 1978 Bankruptcy Reform Act. His results also show that business entities that file for a bankruptcy after October 2005 suffer from a loss on their value of around 13.3% in the first two weeks after the filing. Last but not least, he concludes that the introduction of the Bankruptcy Abuse Prevention and Consumer Protection Act has made it more challenging for companies to restructure under Chapter 11.
In addition, Teloni (2014) investigates what the effects are of the introduction of the BAPCPA on the refiling rates and the bankruptcy duration for firms, which reorganize through Chapter 11, prepacks and pre-negotiated bankruptcies. Due to the fact that firms could extend their exclusivity period pre-BPCPA, Chapter 11 filings are viewed as costlier and not only for creditors but for debtors as well. The articles’ findings conclude through univariate analysis that companies that file for Chapter 11 after the revision in the US Bankruptcy Code spent 32% less time in bankruptcy. Hence, the number of the prepacks and pre-negotiated bankruptcies increased, which means that the new law had the desired effect and firms were required to be more considerate of their reorganization plan. However, the less time spent in duration has a negative effect on the company’s success, or the BAPCPA is associated with significantly positive effect on the refiling rates.
- 6 - 3. Literature Review
To begin with, Tashjian et al. (1996) investigate whether the pre-packaged bankruptcy reorganization option can be a better substitute for a Chapter 11 or an out-of-court restructuring. Even though, that the results are not conclusive, they find that a prepack is a closer substitute for an out-of-court restructuring. What is more, the direct costs as measured by a fraction of total assets associated with a prepack on average are lower than the ones for a Chapter 11 reorganization. The duration time, during which a company is in bankruptcy is also lower. Another finding is that the firms recover faster when choosing to reorganize through a prepack compared to those, filing for Chapter 11. The former also provide the stakeholders of the company with the opportunity of tax savings. The authors also point out that a prepack can be a better way of reorganizing because it resolves the freerider/holdout problem, in which creditors refuse to substitute their old shares for the new ones because of the more unfavourable conditions.
Capareto (2005) confirms some of the findings by Tashjian et al (1996) by looking at the bargaining process between companies that opt for Chapter 11 and those that choose prepacks. This is investigated by introducing a model with two players, who have complete information. Also, there is an outside option, which if used, can end the process of bargaining or the negotiations. Player 1 are the equity holders and Player 2 are the unsecured creditors, respectively. Hence, Player 1 moves first but Player 2 has the advantage of an outside option such as liquidation. Two cases are discussed, the first one: where the players are impatient to reach an understanding. If they do not, then, the outside option is exercised and the equity holders are left with nothing. The second case, there is a chance that the negotiations might fail due to added pressure. Thus, secured creditors are promised to receive more than what is expected from liquidation. The results suggest that the first case refers to pre-packaged bankruptcies, whereas the second to Chapter 11 reorganizations. Hence, the equity holders can exert more power while bargaining compared to unsecured creditors. Moreover, it is clear that companies that use pre-packaged bankruptcies are less prone to liquidate and it is more likely for debtors and creditors to come to an agreement. In the second case, the unsecured debtors demand to be compensated for the extra costs that are borne by opting for Chapter 11.
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The main findings of Chatterjee et al. (1996) are that companies who choose a pre-pack have a significantly lower long-term debt to total assets ratio. However, they have higher amount of current debt outstanding. Also, they are in less financial distress than companies that choose Chapter 11. They have a more immediate need for cash and other liquid securities. Furthermore, the companies are of higher quality as measured by their EBITD (earnings before interest, taxes and depreciation) to total assets and sales. Another important conclusion is that the companies that use pre-packaged bankruptcies are smaller than those that choose an out-of-court workout.
Betker (1995) goes on to conclude in his empirical study of prepacks that companies that choose that type of restructuring are less financially distressed and pay most of the expenses before filing for bankruptcy. Moreover, there are higher tax savings involved than with comparable companies that choose Chapter 11. Betker (1997) confirms the results in his previous research. In his paper, he is estimating the administrative costs related to debt restructuring. The results show that firms that re-organize by prepacks have incurred lower direct costs – 3.93% and 2.85% of total assets prior bankruptcy for Chapter 11 and prepacks, respectively. What is more, direct costs to total assets ratio is positively related to the fraction of the debt that is being restructured.
Another important determinant of how firms will restructure in the future is debt structure. Asquith et al. (1994) conclude that a workout is more likely between a combination of private debt and public debt in the company, where mergers are more likely to be implemented when Chapter 11 is to be avoided.
In addition, LoPucki and Whitford (1991) find that the choice of a venue for a bankruptcy case can be detrimental to its outcome, forum shopping. Hence, some firms choose certain courts because of the higher chances that the judges will rule out in their favour. Moreover, companies that engage in forum shopping want to avoid exclusivity or high fees. This could also have an impact to some states, in that they will attract bigger and international corporations to settle their cases.
Additionally, Rose-Green and Lovata (2013) look at the company’s characteristics before filing for Chapter 11 and after the bankruptcy outcome (liquidation or reorganization). Their results show that three year before filing, the more leveraged firms are more likely to be
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restructured than firms with lower long-term debt to total assets ratio. They also find that two years before filing, business entities that have higher liquidity ratio (cash plus receivables/ current liabilities) are again more likely to reorganize. Also, size (measured as natural log of total assets) and solvency (as measured by the coverage ratio) are positive and significant. Furthermore, they also concluded that the aforementioned characteristics have the same positive and significant effect one year before filing.
James (2016) is investigating some new characteristics that affect the company’s propensity to bankruptcy filing. Intangible assets is one of those and it increases the probability of a firm, filing for Chapter 11. She finds that 363 asset sales behave contrary to the expectation and have negative and significant effect. Hence, these sales are more likely to appear outside of bankruptcy. Moreover, intangible assets and 363 asset sales are linked to a shorter duration or time spent in bankruptcy.
Turning to firm performance, Coehlo (2015) investigates the latter by examining bankruptcy stocks that are still trading on the main exchanges after filing for Chapter 11. He confirms that there is a considerable price drop in these shares pre and post the news of bankruptcy. Overall, however, the market is slow in reacting in the wake of a bankruptcy.
Jory and Madura (2010) look at stock of firms emerging from bankruptcy. They do that by calculating the buy and hold abnormal returns of these stocks using the Fama-French (1992) model. Their main results include that these stocks perform similarly to the ones of companies with close market-to-book ratio and size. Also, these companies should be viewed as more attractive by investors as they provide more opportunities for lucrative investment. Important findings are that business entities perform worse post-emergence if they filed for bankruptcy in the Delaware court or changed their name, better if they re-organized through a prepack or spent more time in bankruptcy.
4. Hypotheses
Hence, this leads to the research questions – which firm characteristics determine the choice of a prepack, how much has the company’s post-bankruptcy performance improved?
As mentioned in the introduction, most of research has been focused on the 1980s and 1990s, but not as much on the 2000s and especially after the introduction of the BAPCPA.
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To check whether the same characteristics make the firms choose prepacks, the following will be tested:
Hypothesis 1. The firms that choose prepacks are of higher quality and less financial distress
as compared to those that file for Chapter 11 before and after the introduction of the BAPCPA. Moreover, another important aspect of prepackaged bankruptcies is to show whether firms that use them perform better and by how much. Thus:
Hypothesis 2. Firms that choose prepacks perform better after bankruptcy filing than
companies which choose Chapter 11. 5. Methodology
To test the first hypothesis a logit regression will be specified due to the fact that the dependent variable can take only two values, 0 or 1:
Pr(𝑝𝑟𝑒𝑝𝑎𝑐𝑘𝑖 = 1) = 𝛼𝑖 + 𝛽1𝑚𝑎𝑟𝑘𝑒𝑡 − 𝑡𝑜 − 𝑏𝑜𝑜𝑘 𝑠𝑎𝑙𝑒𝑠 𝑖 + 𝛽2𝑚𝑎𝑟𝑘𝑒𝑡 − 𝑡𝑜 − 𝑏𝑜𝑜𝑘 𝑡𝑜𝑡𝑎𝑙𝑎𝑠𝑠𝑒𝑡𝑠 𝑖 + 𝛽3 𝐸𝐵𝐼𝑇 𝑡𝑜𝑡𝑎𝑙𝑎𝑠𝑠𝑒𝑡𝑠𝑖 + 𝛽4𝑠ℎ𝑜𝑟𝑡 − 𝑡𝑒𝑟𝑚𝑑𝑒𝑏𝑡 𝑡𝑜𝑡𝑎𝑙𝑎𝑠𝑠𝑒𝑡𝑠 𝑖 + 𝛽5𝑙𝑜𝑛𝑔 − 𝑡𝑒𝑟𝑚𝑑𝑒𝑏𝑡 𝑡𝑜𝑡𝑎𝑙𝑎𝑠𝑠𝑒𝑡𝑠 𝑖 + 𝛽6𝑠𝑡𝑎𝑡𝑒𝑖 + 𝛽7𝑖𝑛𝑡𝑎𝑛𝑔𝑖𝑏𝑙𝑒𝑎𝑠𝑠𝑒𝑡𝑠𝑖+ 𝛽8363𝑠𝑎𝑙𝑒𝑖 + 𝛽9𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛𝑖+ 𝜀𝑖 The methodology is similar to Chatterjee et al (1996), however, out-of-court workout will not be tested. Moreover, there are additional characteristics included, following James (2016), as she mentions the need for investigating whether intangible assets, 363 asset sales and duration have the same effect on firms after the introduction of BAPCPA. In addition, the sample will be divided into two periods before and after 2005.
Thus, 𝑃𝑟𝑒𝑝𝑎𝑐𝑘𝑡 is a binary variable that is equal to 1 if the company reorganized by a pre-packaged bankruptcy and 0, if it used Chapter 11. State is a dummy variable equal to 1 for if the company filed in the state of Delaware and 0, otherwise. Duration is a continuous variable for years, spent in duration. 363 sale is a dummy variable equal to 1 for the execution of a fire sale and 0, otherwise. The rest of the variables are straightforward, ratios of market-to-book to total sales, market-to-book to total assets, EBIT to total assets, short-term debt to total liabilities, long-term debt to total liabilities.
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Following Jory and Madura’s (2010) methodology, the second hypothesis can be tested: 𝐵𝐻𝐴𝑅𝑖 = 𝛼𝑖+ 𝛽1𝑝𝑟𝑒𝑝𝑎𝑐𝑘𝑖 + 𝛽2𝑠𝑡𝑎𝑡𝑒𝑖 + 𝛽3𝑛𝑎𝑚𝑒𝑖 + 𝛽7𝑟𝑒𝑓𝑖𝑙𝑖𝑛𝑔𝑖 + 𝛽8𝑚𝑒𝑟𝑔𝑒𝑟𝑖
+ 𝛽9𝑑𝑢𝑟𝑎𝑡𝑖𝑜𝑛𝑖 + 𝛽10𝑖𝑛𝑡𝑒𝑟𝑎𝑐𝑡𝑖𝑜𝑛𝑠𝑖 + 𝜀𝑖
However, this time it will be tested by how much the performance of a company has improved by choosing a prepack for reorganizing. BHAR stands for buy and hold abnormal returns and is computed by following the methodology in Jory and Madura (2010). Prepack is a dummy variable equal to 1 if the company reorganized by a pre-packaged bankruptcy and 0, if it used Chapter 11. State is a dummy variable equal to 1 for if the company filed in the state of Delaware and 0, otherwise. Duration is a continuous variable for years, spent in duration. Refiling is a dummy variable equal to 1 if the company refiled for bankruptcy and 0, otherwise. Merger a dummy variable equal to 1 if the company merged with or was acquired by another company and 0, otherwise. Interactions is generalised, however, it includes several terms which represent the interactions between prepack and the rest of the variables. Again the period is separated before and after the introduction of BAPCPA. Also, the companies to be looked at are only the ones who eventually emerge from bankruptcy.
Moreover, to calculate the buy and hold abnormal returns, an event study will be undertaken. The related methodology from MacKinlay (1997) will be followed. The event in question is the filing for Chapter 11. Because a firm should file a plan for re-organization in 180 days, for the purposes of this research, the pre-event day is the 180th day before filing and the post-event day is the 180th day after filing. Thus, this will be the event window, consisting of 361 days (pre-event days + event day + post-event days). Furthermore, the estimation window will be 100 days before the -180th day, so that the estimation window does not overlap with the pre-event days. The buy and hold abnormal returns are calculated as the difference between the market (Capital Asset Pricing Model) buy and hold return and the expected buy and hold return conditional on the event’s absence over the event window:
𝐵𝐻𝐴𝑅𝑖(𝜏1,𝜏2) = ∏ (1 + 𝑅𝑖,𝑡) 𝜏2 𝑡=𝜏1 − ∏ (1 + 𝐸(𝑅𝑖,𝜏|𝛺𝑖,𝜏) 𝜏2 𝑡=𝜏1 ,
Where 𝜏1, 𝜏2 is the event window, 𝑅𝑖,𝑡 is the market return, 𝛺𝑖,𝜏 is absence of filing for Chapter 11. For the estimation of R (the return), the market model is chosen (Event Study
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Methodology). The market model is going to be used instead of the Fama-French (1992) model as Jory and Madura (2010) do.
6. Data and Descriptive Statistics 6.1 Data
Data will be collected through Compustat and Event Study in the Wharton Research Database, and the UCLA-LoPucki Bankruptcy Research Database, obtained from dhr. Valdimir Vladimirov. The period will include data from 2000 to 2010, before and after the introduction of the BAPCPA in 2005.
The original dataset from UCLA-LoPucki BRDB includes 987 firms in total that filed for Chapter 11 in the period from 1980 to 2013. However, in this case, the desired period will be restricted to 2000-2010. What is more, due to the unavailability of a variable that refers to whether a company chose to restructure by Chapter 11 or a prepack, it is generated from the variable XPrepackaged. If the latter shows a value of “no”, then it means that the firm opted for Chapter 11. If, on the other hand, the value is “yes”, then, the company decided to re-organize via a pre-packaged bankruptcy. Finally, if the value is “prenegotiated”, then, it is dropped in Stata. This leaves a sample of 443 companies in total, 404 that restructured their debt through Chapter 11 and 39 through a pre-packaged bankruptcy. It is interesting to notice that the firms with prepacks constitute around 10% of the number of Chapter 11 firms.
6.2 Descriptive Statistics
Figure 1 plots the number of firms that filed for Chapter 11 on the vertical axis and the period on the horizontal axis. As can be seen, there are significantly more firms, whose preference is Chapter 11 reorganization, compared to those that chose pre-packaged bankruptcies. The peak is at 2001 when 76 companies opt for the former way of restructuring, whereas there are not any companies to restructure through a prepack. The highest number of firms that choose pre-packaged bankruptcy is in 2002 and 2010, 7, respectively for each year. In addition, it can be noticed that there is a downward trend for both types of re-organization before 2006 and an upward trend after 2006. This could be interpreted as there is a rise in the number of distressed enterprises due to the “dot com” bubble in the 2000s and the subsequent financial crisis in 2008.
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This is confirmed by Table 1: Panel A, where the number of companies for each year are clearly stated. There is a slight rise of companies opting for Chapter 11 in 2005, which can be contributed to the introduction of the BAPCPA. There are 0.68% more firms filing for Chapter 11 in 2005 compared to 2004. This can be connected to the fact that unfamiliarity with the new law and its requirements can lead to an increased filings as was the conclusion of Bak et al (2008). However, in 2006, it can be seen that this trend is offset by a subsequent decline of 3.16. Then, with the occurrence of the 2008 financial crisis, numbers spike again, this time there are 66 companies that restructured in total.
Looking at Table 1: Panel B, at first glance, it is observed that the most distressed firms are in the manufacturing sector, 154 to be precise. Followed by firms operating in the transportation, communications, electric and gas industry, 86. The other two significant numbers representing the finance, insurance and real estate sector and the services industry, 70 and 63, respectively.
Moreover, there are some differences between the firms that choose to re-organize through Chapter 11 and a prepack before 2005 and after 2005 as can be seen in Table 2. There are 141 more firms that were distressed in the period 2000-2005. This is reflected mostly by the firms that filed for Chapter 11, they are significantly more, 271. While in the second period, 2005-2010, the number of companies decreased by approximately 50%. Hence, this can act as a confirmation of what Bak et al (2008) observe in their paper, that firms have not familiarized themselves yet well enough with the new Act or do not meet the new requirements, so fewer of them file for Chapter 11.
Another important characteristic to notice is that the amount of Total Assets and Total Liabilities for firms that apply for Chapter 11 is considerably higher to that for pre-packaged bankruptcies in Table 2. Additionally, the return on assets, ROA, is measured by dividing EBIT by the total assets and it shows how much the return is from the assets. Overall, the trend is that firms that restructure via a prepack have a higher ratio than their counterparts with preference for a Chapter 11. The lowest ROA for the first period is 0.25 for prepacks and -0.99 for Chapter 11. This means that both types of companies whose ROA ratios correspond to these numbers are operating at a loss. These results are confirmed by Chatterjee et al (1996) as they show that a pre-packaged bankruptcy is usually chosen by firms of higher
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quality. Moreover, another thing that can be concluded is that firms that choose Chapter 11 are bigger in terms of total assets and total liabilities and that remains true for both periods.
Finally, Figure 2 presents the mean buy-hold abnormal returns for companies before 180 and after 180 days they file for bankruptcy. There is a gradual drop in the mean BHAR return, between the 180th day and on the same day the company filed for bankruptcy. That is around 80% drop in the mean BHAR. After that, between day 0 and day 100 this trend is followed by another steeper drop in the average buy-hold abnormal return, 200% down from the beginning of the event window. Then, after the first 100 days after filing, the return jumps back and is around 50% lower than at -180th day, the average return gains back value. Furthermore, a bit after day 100 after filing and the end of the event window, the mean BHAR stays more or less on the same level.
6.3. Summary Statistics: Hypothesis 1
In Table 3 are shown the summary statistics for the variables that will be used in the logit regression. The statistics include the mean, median, standard deviation, 25th and 75th percentile, and the number of observations. In Panel A, the period between 2000 and 2005 is looked at, in Panel B – the period of 2005 through 2010, and Panel C is combining the two periods, from 2000 to 2010. It is worth mentioning that in the second period there are considerable less observations, around 75% less, which can lead to some bias when estimating the logit regressions.
In Table 3. Panel A, the average rate of firms choosing prepacks is 0.117021, thus, more companies are attracted to Chapter 11 re-organization. Next, turning to the market-to-book to total sales is significantly low but positive. This can be interpreted as that the market value of shares is declining compared to the historical values, while total sales are still high. However, this is not true for the third variable where total assets is declining very fast, making the average market-to-book to total assets ratio positive and high. Furthermore, the mean of EBIT to total assets is negative, which means that the return on operating profit generated from total assets is negative. Current liabilities to total assets’ mean constitutes around 40%, whereas long-term liabilities to total assets only roughly 13%. The duration of time spent in bankruptcy in years is on average 1.40 years. This group exhibits a mean rate of intangible
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assets of 257.2883. From 94 firms, around 11% engage in fire sales and approximately 36% file for a bankruptcy in the state of Delaware.
Continuing with Panel B, the average rate of companies that opt for a pre-packaged bankruptcy is close to that presented in Panel A, 0.090909. The market-to-book to total assets ratio is still low but positive. One of the differences between Panel A and Panel B that immediately stands out that the market-to-book to total assets has a negative mean in Panel B, equal to -0.0195583. While in Panel A, it is positive and substantially higher, 3.101144. The latter is true for its standard deviation as well. The return on assets, however, is higher and positive, so that the operating profit that is coming from total assets is non-negative. The rates of current liabilities to total assets and long-term liabilities to total assets are close to those exhibited in Panel A, 0.3781998 and 0.1491484, respectively. Also, the duration spent in bankruptcy for this group (2005-2010) is lower and less than a year, on average 0.880199. What is more the level of intangible assets is significantly higher, 2300.374, which is about 10 times bigger than the level in the first period. Last but not least, the second group has a higher rate of engaging in fire sales and the proportion of companies that file for a bankruptcy in the state of Delaware is equal to that of companies filing outside of it.
Turning to Panel C of Table 3, it has similar characteristics to Panel A. As in the other two panels, the average rate of pre-packaged bankruptcies is approximately 11%. The level of market-to-book to total sales is similar to Panel A, as well. However, the market-to-book to total assets’ average is 20% lower and the EBIT to total assets’ mean - lower by 6% compared to the first group. The current liabilities to total assets and long-term liabilities are roughly the same as in between 2000-2005. The duration is slightly lower, companies spend on average 1.29804 years in bankruptcy. The level of intangible assets is higher but has not reached the level of Panel B. Again, firms that choose to have a fire sale are about 11% and the rate of business entities which choose that state of Delaware is around 40%. Moreover, the staggering difference in the number of observations between Panels A and B can be interpreted as being due to the change in the US Bankruptcy Code. This confirms Coehlo (2010) results as the introduction of BAPCPA makes re-organization for firms more challenging.
- 15 - 6.3 Summary Statistics: Hypothesis 2
For Hypothesis 2, the summary statistics are presented in Table 5. Again the period looked at is from 2000 to 2010. The data from the Bankruptcy Research Database is merged together with data from WRDS. More specifically, the buy and hold abnormal returns computed for companies after the bankruptcy filing. This is done through an event study software on WRDS. This, however, results in having only 78 companies and after the exclusion of liquidated ones, there are 41 firms in total. Later, this can impact the regression specification and output. The name of the company is considered the same if it has changed from LLC to INC, for example. It is thought of as different if it is completely unrecognisable or added words in it, e.g. from “Adelphia Business Solutions, Inc” to “Telcove”.
Starting with Table 5. Panel A, it is evident that most of the observations, thus, more of the companies that filed for bankruptcy are from the period 2000-2005, 27. This can be explained as more companies, not being influenced by the introduction of the new law in that period. The average buy and hold abnormal returns for the latter are negative. Moreover, 3.7% of the companies choose to restructure through a prepack, most of them opt for Chapter 11. In this sample, around 11.1% is the rate of refiling of the firms, hence, less than 25% tend to refile for bankruptcy. In addition, the average duration length for company in bankruptcy is approximately a bit over one year (1.357113), with most of them spending at least one year (the 50th percentile is 1.216438). Also, more firms file for Chapter 11 in state other than Delaware, as Panel A shows. In addition, less companies are acquired by or have merged with others, the rate is around 44.4%. What is more, most of the entities keep their name after a bankruptcy, the average rate of changing a firm’s name is 40.7%.
Moving to the second period in Panel B, 2005-2010, it has 14 observations. Thus, a lot less companies filed for a Chapter 11 which again confirms results from previous research (Bat et al (2008); Coehlo(2010)). However, this will cause a bias in the estimation of the regressions for Hypothesis 2 due to the lack of observations. The buy and hold abnormal returns for this period are still negative, with the average being -0.78142. Also, important fact to mention is that there are more recorded pre-packaged bankruptcies as observed by Teloni (2014), the average rate being around 28.6%. Moreover, the rate of refile according to the summary statistics is 0.1428571. The duration of years spent in bankruptcies is significantly lower, less than one year (0.6015982), also confirmed in the research of Teloni (2014). The rate of firms
- 16 -
to file for a bankruptcy in Delaware is on average 50%, half of them file in Delaware and the other outside of it. Last but not least, most of the firms in this period are operating under the same name and are not merging or being acquired by another business enterprise.
Finally, looking at Panel C, the summary statistics for the combined period, 2000-2010, are presented. The average buy and hold abnormal returns are still negative. In comparison to Panel A, the amount of companies choosing pre-packaged bankruptcies is three times as much, 0.12195121. The refiling rate is, on the other hand, close to the one in Panel A, higher by about 0.01%. It is clear that most companies manage to emerge from a bankruptcy without falling back into it on average. The duration of time spent in bankruptcy is lower than Panel A but higher than Panel B, 1.118529. In addition, less than 50% of the firms file for a bankruptcy in the state of Delaware. A lower rate than the one in Panel A is exhibited for business entities that are merging or being acquired by others (0.3902439). Additionally, only about 32% of firms are changing their names after emerging from bankruptcy.
7. Analysis
7.1 Hypothesis 1
For the first hypothesis, the data from the BRDB is merged together with data from Compustat. After taking account of the period in question, 2000-2010, there are 116 observations left, for which the aforementioned methodology will be implemented.
Because of collinearity between some of the variables, five separate models will be used to see which one explains the best the characteristics of a firm that chooses a prepack rather than Chapter 11. It turns out that the 363 sale cannot be fitted in the model. Possible explanations for this that there are too few observations in the sample, high collinearity between the dependent variable and the regressor. Hence, in this case fire sales cannot be considered as a characteristic for the explanation of the re-organization choice.
To start off, the first period in question that is looked at is 2000 and 2005, Table 4. Panel A. There are five models that are considered. Model 1 will be the baseline regression. As it can be seen, only two of the independent variables in that regression are significant, these are market-to-book to total sales and long-term liabilities to total assets. The first one is significant at 10% and the second – at 1%. This can be interpreted as an increase in the market-to-book to total sales will lead to a decrease in the probability of a company
- 17 -
restructuring through a pre-packaged bankruptcy. However, this is not the case for long-term liabilities-to-total assets, where a unit increase in this measure, will result in a lower probability of the firm restructuring through a Chapter 11. Furthermore, it is important to mention that a unit increase in the standard deviation of long-term liabilities to total assets will lead to 1.43264 units increase in the standard deviation of prepack (0.2258264*6.344).
Looking at the other 4 models, we can conclude that the aforementioned variables, market-to-book to total sales and long-term-liabilities to total assets have similar effects on the dependent variable. In Model 2, intangible assets is added to the logit regression but it can be noticed that it is not significant. However, the pseudo R-squared and the log-likelihood have increased by 0.095 and 2.95, respectively.
Moving to Model 3, it is estimated through the variables in the baseline regression plus intangible assets and state. State is positive and significant at the 10% level, which means that if a company chooses to file for bankruptcy in the state of Delaware has a higher probability to restructure through a pre-packaged bankruptcy than Chapter 11. However, in comparison to Model 3, Model 4 has increased its pseudo R-squared by 0.222 and its log-likelihood 5.311, respectively. Also, with regards to the latter measures of Model 4 – this is the regression with the highest pseudo R-squared, 0.672, and lowest log-likelihood probability, -7.809. In this model the market-to-book to total assets and the operating income to total assets are both positive and significant at the 10% level, which means that if they increase by a certain amount, this will lead to a lower probability of Chapter 11 re-organization. Duration, on the other hand is negative and significant at 1%, meaning that less time spent in bankruptcy will lead to the company having higher chances on reorganizing through a prepack. Continuing with Model 5, it is estimated with the variables used in the baseline regression and state. The most considerable difference in this model is that market-to-book to total sales is insignificant.
In contrast, in Panel B of Table 4, there is considerable less observations, hence, that creates a problem with the logit regression estimation. This affected the inclusion of fire sale and state in the regressions. Also, it is highly likely that this lack of observations resulted in collinearity between the variables, thus, they were dropped by Stata. The lower number of companies can be explained by the BAPCPA coming into effect. Model 6 and Model 7 differ from the models in the first panel, in that the current liabilities to total assets is significant
- 18 -
and negative at 5% and 1%, respectively. Whereas in all five models in Panel A, current liabilities to total assets is insignificant and positive. It means that decreasing this ratio would lead to a rise in the probability of a company to re-organize through a prepack, as confirmed by Chatterjee et al (1996). Furthermore, Model 9 is the one with the most observations, 21, the highest pseudo R-squared, 0.678, and the highest log-likelihood, -2.128. It is interesting to notice that in Model 10, market-to-book to total sales has the opposite effect compared to Panel A, it increases the probability of a pre-packaged bankruptcy re-organization. What is more, not only is it positive but significant at 1% level. Another difference with Panel A, specifically, in Model 8 and Model 10, is that intangible assets become significant at the 10% level.
Finally, examining Panel C, which combines the two periods, from 2000 to 2010, it is clear that there a lot of similarities between it and Panel A. Most of the coefficients have the same sign as in Panel A. Although, some of the variable have increased their significance, as in Model 14, market-to-book to total sales and long-term liabilities to total assets are now statistically significant at 1% and market-to-book to total assets is significant at 5%. However, in Model 13, state is no-longer significant at 10%.
7.2 Hypothesis 2
For Hypothesis 2, data from BRDB will be merged with data from Event Study by WRDS. This will lead to the calculation by the event study software of 78 companies for which information is stored. From these firms, 37 are dropped due to the fact that they are later liquidated. Hence, this leaves 41 observations in total. There are three periods specified, thus, three panels, and each panel has five models.
Table 6. Panel A includes data from 2000 to 2005, so that at the end there are 26 observations. In the original regression there are interaction terms included. However, due to the fact that the latter are interaction terms between binary variables in a regression with additional dummy variables, this leads to a multicollinearity problem. Specifically, because the interaction term can be generated by multiplying two dummy variables, it can have a perfect linear relationship between one or both of the dummy variables. Hence, the interaction terms cannot be included in the regressions but have to be dropped or be incorporated as a stand-alone regressor. This results in only one regression that has an
- 19 -
interaction term. Moreover, there are only 26 observations in the sample that are used, which means that this could result in a potential bias. Also, the lack of observations can be a reason for the absence of significant results. Turning to Model 1 in Panel A, it is clear that the latter statement has some merit. None of the chosen variables for this model are significant. This remains true for the rest of the models, with the exception of Model 5. In it, interaction term between prepack and duration is significant at the 10% level. Thus, if the firm re-organizes by a pre-packaged bankruptcy this will increase the buy and hold abnormal returns, holding everything else constant. Furthermore, the effect on BHAR of restructuring through a prepack and spending more time in bankruptcy is going to be positive.
Going to Panel B of Table 6, there are around twice as less number of observations, 12. Hence, the results might be biased. As in Panel A, there not more significant variables in the models presented, apart from Model 8. In it prepack and the interaction term between prepack and duration are both significant. However, prepack has a positive effect on BHAR, holding everything constant, while the interaction term has a negative effect. Thus, their combined effect will be negative (2.857-29.62=-26.763). Hence, this confirms the results of Teloni (2014), so that after the introduction of BAPCPA the effect of prepacks is positive while increasing duration has a negative impact. It should be noticed that in this panel, the Rs-squared are the highest, even though that the number of observations is the lowest. This inevitably means that the results may be biased.
Finally, in Panel C, almost all of the estimated models are exhibiting insignificant results. Thus, there are may be some bias in the output in this panel, again because of the lack of observations. The only significant variable is refile in Model 14, it has negative and significant effect on the buy and hold abnormal returns (10% significance level). This can be interpreted as that if a firm refiles for Chapter 11, this will bring its returns down by 0.328.
8. Robustness Check
8.1 Hypothesis 1
To determine whether or not the results derived for Hypothesis 1 are robust, total assets variable will be derived from BRDB. In the original analysis, total assets was derived from Compustat and it was used in the construction of the variables: market-to-book to total assets, EBIT to total assets, current liabilities to total assets and long-term liabilities to total
- 20 -
assets. This time, however, total assets is going to be taken from BRDB and the latter four variables will be constructed anew and this will help identify if there are any deviations from the original results. Additionally, the period to be examined is from 2000 to 2010.
Now, the results from Table 3. Panel C and Table 7. Panel A will be compared. They represent the summary statistics for the variables in the first hypothesis. Naturally, they are no differences in the statistics reported for prepack, market-to-book to total sales, intangible assets and state. However, there is a considerable difference in market-to-book to total assets, its mean is approximately 10 times smaller in Panel A of Table 7, even though still positive. In addition, in the latter, operating income to total assets has a positive average, which can be interpreted as the operating income generated from the total assets is non-negative. The current liabilities to total assets’ mean value is about 0.17% lower than the one exhibited in Table 3. Panel C. Similarly, long-term liabilities to total assets is smaller by around 0.05%. Interestingly enough, duration has not the same statistics, there are 98 observations reported for which there was information. Thus, it is higher than the one in Panel C, it is 1.536455. Companies spend more time on average in bankruptcy, around 1.5 years.
Turning to the multivariate logit regression analysis in Table 7. Panel B, there are some differences worth mentioning. Some of the variables, which appeared significant in the regression models in Table 4. Panel C, are insignificant and vice versa. As is the case for market-to-book to total assets, it has turned significant in all models apart from Model 14 (Table 7. Panel B). Thus, raising this ratio will lead to increasing the probability of a company to re-organize through a pre-packaged bankruptcy. Moreover, it is noticeable that long-term liabilities to total assets is still significant at 5%. The same cannot be said for operating income to total assets and current liabilities to total assets, they remain insignificant in all models. Model 14 in Table 7 Panel B differs from the one in Table 4. Panel C, because current liabilities to total assets, long-term liabilities to total assets and intangible assets are excluded from the model. This is because if they are included, the pseudo R-squared will be equal to 1 and log-likelihood equal to 9, hence, this means that the model will be perfectly complete. However, this is not the case, and the aforementioned variables are dropped. At the end, only duration is left and it has a negative and significant effect at 10%. In both Panels, when duration is included it yields the highest pseudo R-squared and lowest log-likelihood, which means that
- 21 -
duration can be strongly correlated to the choice of re-organization. The longer the duration, the smaller the chances of the firm to opt for a prepack.
8.2 Hypothesis 2
To establish if the results for the second hypothesis are robust, the Fama-French model will be used instead of the market model when running the event study software. This leaves 77 observations and after dropping the liquidated firms, 40. The period to be looked at is the general one from 2000 to 2010. The event window and the estimation period will remain the same, 361 and 100 days, respectively.
Looking at the summary statistics in Table 5 Panel C and Table 8 Panel A, the buy and hold abnormal return’s mean value is lower in Table 8 Panel A, -0.65072. The average rate of prepacks is equal to the rate of refiling in both panels. Moreover, the duration in Panel A is a bit higher by 0.022 years. The rate of companies that prefer to file for bankruptcy in Delaware is similar (around 1% lower in Table 8 Panel A compared to Table 5 Panel C). A similar statement can be made for the rate of companies which choose to merge with or be acquired by another companies (approximately 1% lower). Finally, more companies from this sample tend to change their name, at least about 3%.
Next, Table 8 Panel B exhibits a lot more significant results in comparison to Table 6 Panel C. The R-squared is also higher but there are smaller number of observations. For example, in Model 11 prepack, duration and merger are all significant. The first one is positive and significant at 5%, hence, re-organizing through a prepack will raise the BHAR by 0.595 basis points. The effect of the duration is also positive but at 10%, spending a year longer in bankruptcy will increase BHAR, ceteris paribus. What is more, higher buy-hold abnormal returns will have a firm which merged with or was acquired by another one. In Model 12, these same variables have similar effects on the buy and hold abnormal returns, with duration staying significant at 5% and merger at 10%. In Model 13, however, refile is exhibiting significance at 10%. Thus, a company relapsing into bankruptcy will get lower abnormal returns. Also, the interaction term between prepack and duration is non-negative and significant at 10%. Last but not least, in Model 14 refile is still significant and negative at 10%. The results acquired from the multivariate regression analysis in Table 8 Panel B show the importance of choosing a model for calculating abnormal returns. While in the original
- 22 -
analysis, the capital asset pricing model was used, it is clear that the Fama-French model yields more significant results. Possibly, it is because it assumes that the intercept is equal to zero and considers the effect of expected return between stock of small and big firms and expected difference between high and low book to market ratios (Fama-French (1992)).
9. Conclusion
To conclude with, this thesis has examined what are the characteristics that determine the relationship between them and the choice of reorganization. Moreover, the company performance after filing for bankruptcy is investigated and its relationship with the specific option for capital restructuring. Both of these are studied with the consideration of the introduction of the new law in the US Bankruptcy Code: The Bankruptcy Abuse Prevention and Consumer Protection Act of 2005. Hence, the relevant periods compared are between 2000 and 2005 and 2005 through 2010.
The results show that BAPCPA greatly influenced the number of firms to file for bankruptcy. This was also confirmed by the research of Bak et al (2008), who found that the introduction of the new law encouraged more filings for Chapter 11 before it came into power. The findings in this thesis proved that the BAPCPA also achieved its aim to reduce the duration of time spent in bankruptcy. However, this caused the rates of refiling consequently rising as was also exhibited in this analysis and confirmed by Teloni (2014).
In terms of company characteristics, pre- and post-BAPCPA results show that firms which choose pre-packaged bankruptcy to re-organize have more liquidity issues, thus their current liabilities to total assets are higher. With that said, on the other hand, their long-term liabilities to total assets are lower than the ones exhibited by companies opting for Chapter 11 as is found by Chatterjee et al (1996). Also, the choice of bankruptcy court also determines the choice of restructuring (LoPucki and Whitford (1991)), as exhibited filing in the state of Delaware increases the chances of a prepack.
Moving to company performance, it was established that companies filing for bankruptcy after 2005 have lower BHAR, which is also confirmed by Coehlo (2010). Most of the results obtained, regarding company performance were insignificant. However, this changed when another model in the robustness check was used: Fama-French instead of the market model. Then, some of the previous findings of other studies were confirmed, such as the fact that
- 23 -
duration yields higher abnormal returns and the rate of refiling leads to lower BHAR (Teloni (2014)). The former remains true for companies which are acquire by or merge with another firm.
In the beginning, there were 987 bankrupt firms from the period 1980 to 2013. However, after subsequent drop of observations from 1980 to 2000 and 2010 to 2013, and the merging of data from Compustat and Event Study, there were around 116 and 41 observations left to test the first and the second hypothesis, respectively. Thus, this posed one of the first limitations for this research, the lack of data. This proved difficult when estimating regression equations and interpreting the results output, which might be biased. In addition, the lower number of observations, especially in the second hypothesis, might have led to the scarcity of significant results. Also, the lack of bankruptcy firms in this period which subsequently emerge from bankruptcy and for which there is available information on their BHAR (only 2 companies), prevented from investigating further the relationship between abormal returns and the emergence from bankruptcy.
Another limitation can be the estimation of an appropriate model to be used. This was again confirmed in the robustness analysis of the second hypothesis, where the use of the Fama-French model yielded more significant results than the chosen in the original market (CAPM) model. Hence, this point can be made for all research, choosing an appropriate model might lead the results into a different direction.
Finally, this thesis sheds further light on firm characterstics and performance with regards to bankruptcy after the introduction of BAPCPA. There is a further need of research concerning this law, especially today when there is a considerable interconnectedness between countries and companies. Thus, a change in the law may bring about changes in a company operating at an international level, which in turn will cause problems not only for domestic but international investors.
- 24 - 10. References
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Journal of Economic Dynamics and Control 36(12), pp. 1867-1887.
Asquith P., Gertner R., Scharfstein D. 1994. Anatomy of Financial Distress: An Examination of Junk Bond Issuers. Quarterly Journal of Economics 109(3), pp. 625-658.
Bak, T., Golmant, J., Woods, J. 2008. A Comparison of the Effects of the 1978 and 2005 Bankruptcy Reform Legislation on Bankruptcy Filing Rates. Emory Bankruptcy Development
Journal, pp. 1-25.
Betker, B. L. 1995. An Empirical Examination of Prepackaged Bankruptcy. Financial
Management 24(1), pp. 3-18.
Betker, B. L. 1997. The Administrative Cost of Debt Restructuring: Some Recent Evidence.
Financial Management 26(4), pp. 56-68.
Capareto, M. 2005. Bankruptcy bargaining with outside options and strategic delay. Journal
of Corporate Finance 11(4), pp. 736-746.
Chatterjee, S., Upinder, S. D., Ramírez, G. G. 1996. Resolution of Financial Distress: Debt Restructurings via Chapter 11, Prepackaged Bankruptcies and, Workouts. Financial
Management 25(1), pp. 5-18.
Coehlo, L. M. S. 2010. Bankruptcy Abuse Prevention and Consumer Protection Act: Friend or Foe? Social Science Research Network, pp. 1-25.
Coehlo, L.M.S 2015. Bad news does not always travel fast: evidence from Chapter 11 filings.
Accounting and Finance. 55(2), pp. 415-442.
Fama, E. and French, K. 1992. The Cross-Section of Expected Stock Returns. The Journal of Finance. Vol. 47, No. 2, pp. 427-465.
James, S. 2016. Strategic bankruptcy: A stakeholder management perspective. Journal of
Business Research 69(2), pp. 492-499.
Jory, S. R. and Madura, J. 2010. The long-run performance of firms emerging from Chapter 11 bankruptcy. Applied Financial Economics 20(14), pp. 1145-1161.
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Lopucki L. M., Whitford W. C. 1991. Venue Choice and Forum Shopping in the Bankruptcy reorganization of large, publicly held firms. Wisconsin Law Review 1991 (1), pp. 11-63. MacKinlay, A.C. 1997. Event Studies in Economics and Finance. Journal of Economic Literature 35, pp. 13-39.
Mazur, J. (2015) Can Stricter Bankruptcy Laws Discipline Capital Investment? Evidence from the US Airline Industry. Job Market Paper, pp. 1-65.
Rose-Green, E. and Lovata, L. 2013. The Relationship between Firm’s Characteristics in the Periods Prior to Bankruptcy Filing and Bankruptcy Outcome. Accounting and Finance Research 2(1), pp. 97-109.
Tashjian, E., Lease, R. C., Mc Connell, J. J. 1996. An empirical analysis of prepackaged bankruptcies. Journal of Financial Economics 40(1), pp. 135–162.
Teloni, F. 2014. Chapter 11 Duration, Preplanned Cases, and Refiling Rates: An Empirical Analysis in the Post-BAPCPA Era. American Bankruptcy Institute Law Review. Forthcoming in 2015
Williams Walsh, M. 2016. Puerto Rico Fights for Chapter 9 Bankruptcy in Supreme Court. The
New York Times. March, 2016. [Online]. Available at:
http://www.nytimes.com/2016/03/23/business/dealbook/puerto-rico-fights-for-chapter-9-bankruptcy-in-supreme-court.html?_r=0
Greenberg, S. and Ekins, G. 2015. Tax Policy Helped Create Puerto Rico’s Fiscal Crisis [Online]. Available at: http://taxfoundation.org/blog/tax-policy-helped-create-puerto-rico-s-fiscal-crisis
Event Study Methodology at Event Study Metrics [Online]. Available at:
- 26 - 11. Appendix
Figure 1. Number of Firms Undertaking Re-Organization between 2000 and 2010
Notes. Number of firms that re-organize through Chapter 11 and pre-packaged bankruptcy between 2000 and 2010. The number of companies is presented on the y-axis and the years – on the x-axis. Chapter 11 firms are represented in blue and Prepack firms are shown in red.
0 10 20 30 40 50 60 70 80 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Figure 1
Chapter 11 Prepack- 27 -
Figure 2. Mean Cumulative Return by Day Relative to Bankruptcy
Notes. This figure represents the mean cumulative buy and hold return by day relative to bankruptcy. This is done for 78 companies, for which data is present. On the vertical axis there is the return and on the horizontal there is the number of days relative to the event. The blue line is the mean buy-hold abnormal return. This figure was obtained from WRDS.
- 28 -
Table 1. Panel A: Sample Distribution over the Period 2000-2010
Year Chapter 11 Prepack Total Percentage of Sample 2000 63 4 67 15.12 2001 76 0 76 17.16 2002 51 7 58 13.09 2003 44 4 48 10.84 2004 16 4 20 4.51 2005 21 2 23 5.19 2006 9 0 9 2.03 2007 9 3 12 2.71 2008 33 2 35 7.90 2009 60 6 66 14.90 2010 22 7 29 6.55 Total 404 39 443 100.00
- 29 -
Panel B: Sample Distribution of Re-organized Firms across Industries between 2000 and 2010
Notes. Number of firms that re-organize through Chapter 11 or a Prepack. This table looks at the period between 2000 and 2010. Panel A looks at the distribution of companies and the percentage of firms to restructure in a year compared to the total number of re-organized companies. Whereas Panel B shows the sample distribution of the latter over the 10-year period across different industries. SIC (standard industrial classification) is the industry code, which ranges from to 8, its corresponding industry can be found in the second column under Industry Description.
Year
SIC-code Industry Description 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 Total
0 A: Agricultural Production Crops 1 1 2
1 B: Mining 3 1 2 6
2 C: Construction 2 1 2 1 1 7
3 D: Manufacturing 23 23 17 21 10 8 5 2 10 29 6 154 4 E: Transportation, Communications, Electric, Gas 9 22 21 11 5 8 1 1 4 1 3 86
5 F: Wholesale Trade 3 6 2 3 14
6 G: Retail Trade 11 6 2 3 3 3 2 5 5 1 41
7 H: Finance, Insurance, And Real Estate 4 3 4 3 1 3 1 5 10 22 14 70
8 I: Services 16 16 7 5 1 1 2 2 3 6 4 63
- 30 -
Table 2. Panel A: Summary Statistics of Distressed Firms before 2005
Type of
reorganization Variable N Mean S.D. Min Max
Chapter 11 Total Assets 271.00 3,495.31 11,171.29 273.00 134,780.00 Total Liabilities 271.00 2,981.88 7,642.19 65.43 67,044.83 ROA 246.00 -0.04 0.13 -0.99 0.15
Prepack Total Assets 21.00 860.95 494.84 289.00 2,153.00 Total Liabilities 21.00 1,007.38 622.01 329.48 2,710.42 ROA 19.00 -0.03 0.10 -0.25 0.12
Total Total Assets 292.00 3,305.85 10,783.01 273.00 134,780.00 Total Liabilities 292.00 2,839.88 7,380.80 65.43 67,044.83
ROA 265.00 -0.04 0.13 -0.99 0.15
Panel B: Summary Statistics of Distressed Firms after 2005
Type of
reorganization Variable N Mean S.D. Min Max
Chapter 11 Total Assets 133.00 13,044.98 70,901.87 241.00 737,795.00 Total Liabilities 133.00 13,190.46 69,437.01 155.58 713,784.18 ROA 114.00 -0.02 0.10 -0.41 0.33
Prepack Total Assets 18.00 5,732.78 20,273.76 368.00 86,922.00 Total Liabilities 18.00 5,494.88 18,129.49 290.64 78,095.62 ROA 13.00 0.06 0.10 -0.05 0.33
Total Total Assets 151.00 12,173.33 66,903.37 241.00 737,795.00 Total Liabilities 151.00 12,273.11 65,470.82 155.58 713,784.18
ROA 127.00 -0.01 0.10 -0.41 0.33
Notes. This table shows the summary statistics for the type of firms that choose Chapter 11 versus the ones that opt for a pre-packaged bankruptcy. ROA is used for the return on assets, which is the ratio of EBIT/Total Assets, where EBIT is the earnings before interest and tax. Total Assets and Total Liabilities are measured in current dollars, or the amount of total assets (liabilities) is multiplied by the most recent consumer price index of the BRDB to the consumer price index at the time of bankruptcy. N is the number of companies, Mean stands for the mean, S.D. is the standard deviation, Min and Max for the minimum and the maximum amount, respectively, for Total Assets, Total Liabilities and ROA.
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Table 3. Panel A: Summary Statistics for the Variables in Hypothesis 1 for the period 2000-2005
Notes. These are the summary statistics for the variables used in Hypothesis 1, where prepack is a binary variable equal to 1 for a pre-packaged bankruptcy or 0 for a Chapter 11 bankruptcy. The rest of the variables are the ratios of market-to-book value to total sales, market-to-book value to total assets, operating income to total assets, current liabilities to total assets, long-term liabilities to total assets, duration, intangible assets and state. Where state is a binary variable equal to 1 if the company filed for bankruptcy in the state of Delaware and 0, otherwise. Duration, a continuous variable, equal to the years in which the company was in bankruptcy. Mean stands for the mean of the variable; p25, p50 and p75 stand for the 25th, 50th and 75th percentile; s.d. – for the standard deviation and N – the number of observations. The data covers the period between 2000 and 2005. Statistics Prepack Market-to-Book/Total Sales Market-to-Book/Total Assets EBIT/Total Assets Current Liabilities/Total Assets Long-Term Liabilities/Total Assets Duration Intangible Assets Fire sale State Mean 0.117021 0.0000677 3.101144 -0.3184065 0.3952803 0.1276944 1.395832 257.2883 0.10638 0.3617 p50 0 8.24E-10 0.0009114 0.0035503 0.2132538 0.0295908 1.223288 45.9075 0 0 s.d. 0.323169 0.0005853 18.9655 2.265108 0.6414644 0.2258264 1.376819 576.0063 0.30998 0.48307 p25 0 -3.26E-08 -0.0518934 -0.0910078 0.1283118 0 0.186301 0 0 0 p75 0 4.41E-08 0.0730587 0.0498524 0.4729762 0.131755 1.928767 210.838 0 1 N 94 76 76 83 77 83 94 76 94 94
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Panel B: Summary Statistics for the Variables in Hypothesis 1 for the period 2005-2010
Statistics Prepack Market-to-Book/Total Sales Market-to-Book/Total Assets EBIT/Total Assets Current Liabilities/Total Assets Long-Term Liabilities/Total Assets Duration Intangible Assets Fire sale State Mean 0.090909 6.07E-08 -0.0195583 0.0166062 0.3781998 0.1491484 0.880199 2300.374 0.13636 0.5 p50 0 8.39E-11 0.0001743 0.025196 0.3152241 0.0063652 1.171233 66.4 0 0.5 s.d. 0.294245 6.26E-07 0.8040631 0.0915222 0.2550818 0.2826293 0.630192 9836.083 0.35125 0.51177 p25 0 -1.55E-08 -0.0112774 0.0879122 0.2341507 0.0017844 0.115069 3.406 0 0 p75 0 6.82E-08 0.0492703 0.0469955 0.384717 0.1348507 1.339726 271.536 0 1 N 22 19 19 21 17 21 22 21 22 22
Notes. These are the summary statistics for the variables used in Hypothesis 1, where prepack is a binary variable equal to 1 for a pre-packaged bankruptcy or 0 for a Chapter 11 bankruptcy. The rest of the variables are the ratios of market-to-book value to total sales, market-to-book value to total assets, operating income to total assets, current liabilities to total assets, long-term liabilities to total assets, duration, intangible assets and state. Where state is a binary variable equal to 1 if the company filed for bankruptcy in the state of Delaware and 0, otherwise. Duration, a continuous variable, equal to the years in which the company was in bankruptcy. Mean stands for the mean of the variable; p25, p50 and p75 stand for the 25th, 50th and 75th percentile; s.d. – for the standard deviation and N – the number of observations. The data covers the period between 2005 and 2010.
