The Pennsylvania State University Schreyer Honors College

THE PENNSYLVANIA STATE UNIVERSITY SCHREYER HONORS COLLEGE

DEPARTMENT OF FINANCE

THE ROLE OF REAL ASSETS IN BANKRUPTCY RESOLUTION

TESSA MARIE STUBLER Spring 2011

A thesis submitted in partial fulfillment of the requirements for a baccalaureate degree in Finance with honors in Finance

Reviewed and approved* by the following:

Brent Ambrose Smeal Professor of Real Estate Thesis Supervisor

James Miles Professor of Finance Honors Adviser, Reader

* Signatures are on file in the Schreyer Honors College.

Abstract

This paper explores the impact that real assets have on corporate bankruptcy resolutions. I use a logistic regression model to predict the probability of reorganization based on the amount of real assets a company has on its balance sheet the year prior to filing. Following a similar study conducted by Barniv, Agarwal, and Leach (2002), I collected financial data and created a prediction model. Using data collected on 236 firms that were delisted from a national exchange in the past ten years due to bankruptcy issues, my analysis classifies the bankruptcy outcome for each firm as reorganized or liquidated during bankruptcy. The classifications are based upon public documents filed with the

Securities and Exchange Commission referring to the bankruptcy process and decisions reached by the court. The study also explores several case studies in which corporations filed for protection under Chapter 7 or Chapter 11 and reached either a reorganization or liquidation resolution. The regression results confirm that firms with more real assets have a higher probability of reaching a reorganization resolution.

Table of Contents

Introduction ...... 1

Literature Review...... 2

Case Study: Circuit City ...... 7

Case Study: Lehman Brothers ...... 8

Case Study: Trump Entertainment & Resorts ...... 9

Case Study: Ubrandit.com ...... 10

Hypothesis ...... 10

Research Methodology ...... 12

Data Collection ...... 13

Results 16

Table 1: Descriptive Statistics ...... 20 Table 2: Univariate Comparisons of Variables and Tests of Significance ...... 21 Table 3: Variable Logistic Regression Estimates Using Net Property Plant & Equipment ...... 22 Table 4: Variable Logistic Regression Estimates Using Property Plant & Equipment Real Estate ...... 23

Summary ...... 24

Conclusion ...... 24

Appendix ...... 26

Exhibit A: Sample ...... 26

References ...... 44

Introduction

The globalization of business over the past decade has led to the emergence of impressive enterprises, although many have fallen victim to cash flow and liquidity issues, leading to bankruptcy. Corporate bankruptcy filings have greatly increased, resulting from challenging economic times, poorly managed businesses, and unnecessary risk taking. As the economy struggled through a financial meltdown between 2006 and

2007, there was a 43.8% increase in the total number of corporate bankruptcy filings. 1

At the most basic level, when a company’s liabilities exceed its assets and it can no longer continue to pay principal and interest payments on its debt, bankruptcy becomes a likely and attractive solution. When a company files for corporate bankruptcy, it can do so under Chapter 11 or Chapter 7. Chapter 11 allows the firm to submit a reorganization plan whereas Chapter 7 immediately begins the liquidation process.

Several factors influence the outcome of a bankruptcy filing, such as the availability of financing, the firm’s cash position, and the strength of firms’ business model. The usual goal is to emerge from bankruptcy in a way that minimizes cost and allows the company to continue operating and return to profitability. This paper explores whether companies with real assets on their balance sheets prior to bankruptcy filing have a greater probability of emerging from bankruptcy or entering into liquidation. 2

1 See Bankruptcy Statistics (2011) 2 See Polinsky, A. Mitchell, and Steven Shavell (2007) 1

Previous studies have tried to predict bankruptcy resolution based upon several standard financial variables, but did not include property plant and equipment (a proxy for real assets) in their research. After developing variables used in previous studies, I introduced two new variables. The two new variables identify the amount of real assets, but are defined differently. Therefore, I estimate two separate logistic regressions in order to isolate the effect of real asset value.

Literature Review

Bankruptcy is a legal process by which financially distressed firms, individuals, and sometimes governments resolve their debts. When a firm decides to file for bankruptcy, the goal is to reduce the cost of defaulting on debts by either resolving some of the debt entirely or by reducing the debt to a manageable level. When a firm enters into corporate bankruptcy, it can do so under Chapter 11 or Chapter 7. Chapter 11 allows the firm to continue to operate and the pre-bankruptcy managers remain in control.3

When a firm chooses to file under Chapter 11, the firm continues to operate and a reorganization plan is created. Until the reorganization plan goes into effect, the firm ceases to make any principal or interest payments on unsecured debt, although it does continue to pay on secured loans. In order for the firm to successfully emerge from bankruptcy, it must submit a reorganization plan that resolves all debts of the firm. Rather than selling the firms’ assets, as is the case under Chapter 7, the company plans on future earnings of the company to resolve its debts. Firms in Chapter 11 are relieved of the

3 See Polinsky, A. Mitchell and Steven Shavell (2007) 2

obligation to pay taxes on debt forgiveness, which improves cash flow and makes debt repayment more feasible. If the firm chooses to file under Chapter 7, the company enters into a liquidation agreement whereby a trustee is appointed and all of the assets are sold with the proceeds distributed to the creditors.

The process of creating a reorganization plan requires significant time and negotiation. During the first four months after the company files, managers submit a plan and creditors are given the option to “take it or leave it”. Managers carefully consider whether the plan allows them to pay creditors the least amount possible and still lead creditors to successfully accept the plan. The plan is approved via a voting procedure, which requires a two-thirds margin in amount and one-half in the number of claims.

Two-thirds of equity holders must also vote in favor. The final step requires a bankruptcy judge to approve the reorganization plan. At this time, the plan becomes effective.

If the judge does not approve the reorganization plan, he or she can give creditors the option to replace managers, propose an entirely new plan, or require that the firm be sold as a going concern. While a company is reorganizing, it remains in control of all assets and continues to operate. The funds from future earnings are used to repay creditors. In reality, creditors will only be paid back a portion of what they were originally owed and equity holders will be granted new equity shares. The company must make sure to repay the creditors enough in order to retain reasonable access to credit in the future. Firms attempting to reorganize often seek debtor-in-possession financing. This type of financing is specific to firms undergoing the bankruptcy process and becomes

senior to all other securities. Firms use debtor-in-possession financing as “seed money” to begin anew and repay existing debt obligations. 4

While most corporations initially file under Chapter 11, firms also have the option to file under Chapter 7. Chapter 7 is the legal procedure used for companies in liquidation and involves the company ceasing operations, selling all assets, and distributing the proceeds to creditors. When a company files under Chapter 7, a trustee is appointed to manage the process to dissolve the corporation. Once the firm is liquidated and there is a cash infusion, there must be a standard that is used to distribute the proceeds. 5

Under Chapter 7, the absolute priority rule (APR) is used to distribute the funds to creditors. APR determines the claims that take precedence during the bankruptcy filing and requires that all creditors must be paid in full before equity holders receive anything.

APR also outlines that higher priority creditors be paid before lower priority creditors.

Essentially, the rule ranks the importance of the company’s liabilities and makes sure that those at the top receive payment first. As a result, creditors ultimately receive payment in full or nothing at all, excluding the lowest ranking creditors, which may receive partial payment. The highest priority automatically goes to administrative expenses such as filing fees, lawyer’s fees, and trustee’s fees, followed by statutory priorities, such as tax claims, rent claims, and some unpaid wage and benefit claims. Unsecured creditors such as bondholders and trade creditors are paid next. Equity holders are last priority. Secured creditors are outside the priority ordering and negotiate to receive a partial asset if the firm files, which is not included in the asset pool used to pay others. In the event that the

4 See Chapter 11 Fundamentals (2009) 5 See Polinsky, A. Mitchell and Steven Shavell (2007) 4

secured claims are equal to or greater than the value of all the firm’s assets, then creditors receive nothing. Creditors can leapfrog over each other in the APR process by renewing loans in return for liens on certain assets.6

Leapfrogging also occurs under Chapter 11, when new loans are filed after the firm has entered into bankruptcy so that they become administrative expenses. Therefore, the new loans receive first priority under APR at the expense of the pre-bankruptcy lenders. When a lender transfers from higher priority to lower priority during bankruptcy, it is referred to as a deviation from APR.

Managers rarely choose to file under Chapter 7, even though the set of all assets may often times be smaller in reorganization than in liquidation. This occurs because firms often spend a lot of time in Chapter 11 before they choose to liquidate, causing the total asset value to erode. Under Chapter 11, creditors must receive as much if not more than they would under Chapter 7. Lower priority creditors and equity holders prefer firms to file under Chapter 7 because they have a chance to receive some payoff.

When a firm decides to seek bankruptcy protection there are strict legal procedures that must take place. A voluntary case is officially opened when a company files a petition with the bankruptcy courts in its respective state. When the firm files, it is known as an “order for relief”. When a corporation files a petition, it must include financial information, including detailed asset and liability values, secured and unsecured debt, shares of common and preferred equity, and SEC file number. The petition must also include a brief business description and a list of all persons who have 5 percent or more ownership.

6 See Polinsky, A. Mitchell and Steven Shavell (2007) 5

If a company files under Chapter 11 and continues to operate business as a debtor- in-possession, it must be able to prove that it is able to meet its ongoing financial obligations while under bankruptcy code. Administrative obligations can be met by using existing cash flows, cash collateral when a secured lender has a lien in receivables, or debtor-in-possession financing. Cash collateral refers to cash, negotiable instruments, documents of title, securities, or other cash equivalents in which the estate and another entity both have interest.7

A prior paper written by Barniv, Agarwal, and Leach (2002) also examined bankruptcy resolution and was used as the basis for this study. Utilizing their regression model and accounting variables, I created a similar model. Their research used a ten- variable logistic regression model to predict bankruptcy resolutions including emergence, acquisition, or liquidation. The results showed that using an ordered logistic regression model accurately predicted the bankruptcy outcome 61.6% of the time when placing firms in one of the three groups.8

Firms analyzed in this study either reorganized or liquidated during bankruptcy proceedings. All companies that file under Chapter 7 enter into liquidation, but firms that file under Chapter 11 can reorganize or liquidate. The public filings provide insight into the resolution. The following case studies analyze a firm that liquidated under Chapter 7, two that liquidated under Chapter 11, and one that successfully reorganized under

Chapter 11.

7 See Polinsky, A. Mitchell and Steven Shavell (2007) 8 See Barniv, Agarwal, and Leach (2002) 6

Case Study: Circuit City

Vacant shopping centers across the nation highlight the financial devastation our economy has faced since 2007. Among the vacancies are the 700 Circuit City stores nationwide that closed their doors since the company filed for bankruptcy protection on

November 10, 2008 under Chapter 11 in Virginia.9 Circuit City is among the group of firms that attempted to have a reorganization plan approved under Chapter 11 but ended up liquidating.

Circuit City was the nation’s second largest consumer electronics retailer and was a supercenter for televisions, audio systems, and DVDs. However, the company ultimately closed its final doors in March 2009. Circuit City spent four months under bankruptcy protection attempting to formulate a plan of reorganization but was unable to obtain adequate financing and resorted to liquidation.

Initially, the firm began to downsize and close a select group of stores in hopes that it would be able to emerge from Chapter 11. The firm closed 150 stores and was able to secure $1.1 billion in commitments for debtor-in-possession financing that was intended to help provide Circuit City sufficient liquidity to continue operating during the reorganization period. The surrounding macro-economic environment worked in opposition to Circuit City and a disappointing holiday retail season in 2008 further pressured the company’s liquidity. Although the company put itself up for sale, no potential buyers emerged and the result was a loss of more than 30,000 jobs across the country.

9 See Llovio, L. (2010) 7

A trustee was appointed to the firm to help with liquidation and creditors began to be repaid 22 months after filing. The liquidation fund distributed $450 million to the creditors. With claims ranging between $1.8 billion and $2.0 billion, unsecured creditors only received 10 to 30 percent of what they were owed. Secured creditors got 100 percent of what they are owed, which was between $5 and $7 million. Similarly, all priority claimants were also paid 100 percent of the $3 to $16 million owed.10

Case Study: Lehman Brothers

Lehman Brothers opened its doors as a commodity broker dealer in cotton and eventually evolved into a powerhouse investment banking firm. In late 2002, Lehman

Brothers was even named “Bank of the Year”. During the early 2000’s Lehman Brothers was at the top of the league tables and recording healthy profits. These profits were fueled by the introduction of the mortgage backed security (MBS). Mortgages were packaged, securitized and sold as investments to banks. Banks were willing to invest because the return was substantial. As the MBS market erupted and led to what seemed like endless profits, Lehman Brothers became a big player. At one point Lehman had levered itself 30:1. Regional banks continued to lend to Americans that were not creditworthy at subprime rates, and the housing bubble began to build. In the aftermath of a weakened U.S. economy, housing prices unexpectedly began to fall, borrowers became unable to repay their debts and the value of MBSs plummeted. Banks such as Lehman

Brothers were forced to “write-down” their assets, but were still responsible for repaying

10 See Llovio, L. (2010) 8

their debts. Ultimately, Lehman Brothers became insolvent because they had insufficient cash flow to repay the interest on their debts.11

As a result, in September 2008, the company filed for bankruptcy. Initially, the firm filed under Chapter 7, but on one of the most important weekends in financial history, the firm decided to file under Chapter 11 protection instead. However, the firm was in no position to reorganize and was forced to sell itself to Barclays Capital for $1.7 billion.12

Case Study: Trump Entertainment & Resorts

In 2004, Trump Hotels & Casino Resorts (THCR) filed for bankruptcy under

Chapter 11 with $1.8 billion of debt. The following year, the company emerged from bankruptcy protection under a new name, Trump Entertainment & Resorts (TER). During the reorganization process, the company sold its World’s Fair site in Atlantic City for $25 million, along with other assets. The sale of such assets allowed TER to reduce its debt to

$1.25 billion and repay equity holders $17.5 billion in cash. TER is most famously known for the man who built an empire only to watch it collapse and rebuild it again,

Donald Trump. The final step in the reorganization process included appointing James

Perry as the new CEO of Trump Entertainment & Resorts. Although Donald Trump remained Chairman for almost four years after the filing, he officially resigned in 2009.

Not long after, the company would visit bankruptcy court for the second time in the

11 See Chapman, Peter (2010) 12 See Tibman, Joseph (2009) 9

twenty-first century. In 2009, the company once again filed under Chapter 11, but emerged in 2010 under a new CEO.13

Case Study: Ubrandit.com

Ubrandit.com capitalized upon the shift towards a virtual world, specializing in the development of online web sites for financial and ecommerce firms. However, the business model did not prove resilient and in 2002 the company filed for bankruptcy protection under Chapter 7. All business operations were terminated, and the trustee disposed of all assets. By 2005, the case was closed and Ubrandit.com ceased to file monthly reports with the SEC.14

As can be seen through the previous case studies, firms often times do not reach a bankruptcy resolution that is consistent with the Chapter under which they file. Many firms that file under Chapter 11 with hopes to reorganize are unable to do so and resort to liquidation. The remainder of this paper will examine a more empirical method to predict bankruptcy resolutions.

Hypothesis

This paper tests the hypothesis that real assets positively impact the probability of reorganization during a bankruptcy proceeding. Although an overwhelming majority of companies in the data set filed for bankruptcy under Chapter 11, we can question whether

13 See Schein, Amy (2011) 14 See “Ubrandit.com” (2011) 10

there is conclusive evidence that the presence of real assets is the differentiating factor between firms that reorganize and those that liquidate. Based on findings in the individual

8-k filings each firm submits to the Securities and Exchange Commission, many companies that were able to reorganize and emerge from bankruptcy did so by partially liquidating assets. Often times the assets included real assets such as buildings, machinery, or retail space.

Ambrose (1990), challenges the efficient market theory that real assets are not specialized assets. According to his study, real estate is location specific, has poor information availability, requires large information collection and transaction costs, and exists in a discontinuous market. Therefore, real estate is a unique and specialized asset compared to other assets. Ambrose (1990) studies the impact real estate has on takeovers and concludes that corporate real estate does impact takeovers. Similarly, this paper classifies real estate as a real asset that impacts bankruptcy resolution. Corporations can monetize real assets during bankruptcy proceedings and increase cash flow, which leads to an increased likelihood that the creditors will be repaid in full. In order to successfully reorganize under Chapter 11, it is necessary that 100 percent of claims be repaid.

Therefore, real assets have a positive impact on the probability of reorganization.

Under the null hypothesis, the expectation is that there is no difference in the average value of real assets for reorganized firms versus liquidated firms. Therefore, statistical significance would lead to rejecting the null hypothesis and concluding that there is a difference between the average values. The study is then able to confirm the hypothesis that real assets have a positive impact on the likelihood of reorganization.

Research Methodology

For this study, a logistic regression model is used to predict an outcome using a binary response variable. The y variable in the regression denotes two possible outcomes

(reorganization=1 and liquidation=0). A linear probability model predicts the probability of reorganization as a function of a set of variables. Therefore, the following equation was used.

( )

When β>0, Pr(y=1) increases as x increases and the sign of β determines the direction of the relationship (positive or negative).

Often times the odds ratio is used to interpret logistic regression models and to explain β. The odds ratio explains the association between the variable and the outcome.

An odds ratio indicates that a 1 unit increase in the x variable leads to an exponential change in the odds of the outcome being true.

Another statistic used to interpret the data results is the z-score. A z-score is used when the distribution is normal and shows what percentage of the data is above or below the mean. The z-score tells how many standard deviations a given value is above or below the mean and can be translated into a probability by calculating the area under the normally distributed curve. The z-score is calculated by taking the difference between the estimate and the population mean and dividing by the standard deviation.

The t-statistic is used in hypothesis testing to determine if the coefficient of a variable is significantly different than zero. For this study, the t-stat measures if the

difference between two means differs from zero. The computed t-stat is compared to a critical value that is based upon the number of independent data points in the sample, known as the degrees of freedom (df=n-1, where n=number of data points in the sample).

The t-stat must be larger than the critical value in order for the null hypothesis to be rejected. The t-stat is the difference between the sample mean and the population mean divided by the sample standard deviation.

Data Collection

The data collected was based on 236 firms that delisted from a national exchange over the past 10 years due to bankruptcy. CompuStat was used to generate the master list of firms and then I was able to pull year-end balance sheets for all years the company existed over the last ten years.15 The financial data pulled from the year prior to the delisting date was used in the regression model and all other financial data was disregarded. The data returned extensive data, but for the purposes of this study only net income, total assets, total liabilities, intangible assets, property plant & equipment (PPE) buildings, PPE construction, PPE land improvements, net PPE, secured interest bearing debt, and interest bearing debt were used. The GDP deflator was calculated using data from Federal Reserve Economic Data (FRED).16

GDP Deflator = Nominal GDP/Real GDP * 100

15 See Compustat (2011) 16 See “Federal Reserve Economic Data” (2011) 13

Using LexisNexus© Academic, I was able to collect information from public

Security and Exchange Commission (SEC) 8-k filings to research the Chapter under which each company filed, along with the outcome of the bankruptcy hearing. Then, each company was assigned a value representing one of two categories; reorganization (1) or liquidation (0). The binary variable was named “outcome” and each other variable was assigned a classification.

The 236 firms used as data points include all industries, but exclude companies listed as ADRs, which are not domestic firms. Based on the financial information gathered and Barniv, Agarwal, and Leach’s definitions, the following seven variables were identified and used in this study.17

NI/TA = Net Income/Total Assets. I expect a positive coefficient because firms

with greater net income are more likely to be able to return to profitability. Net

income is cash that can be used to directly pay down debt.

LNTA = The natural logarithm of Total Assets/GDP. This variable serves as a

measure of firm size. Larger firms are able to downsize and sell certain assets in

order to increase liquidity, leading to an increased likelihood to reorganize.

Therefore, I expect a positive coefficient.

INTA/S = Intangible Assets/Net Sales. A firm with more intangible assets on its

balance sheet becomes a more attractive acquisition target since buyers place

value on untapped potential. Therefore, I expect a positive coefficient for this

variable, indicating that as intangible assets increase, reorganization becomes

17 See Barniv, Agarwal, and Leach (2002) 14

more plausible. This can be attributed to the financing available to larger firms and the ability to downsize.

TD/TL = Interest Bearing Debt/Total Liabilities. I expect this coefficient to be negative because as the proportion of debt increases, the financial obligations of the firm increase. Since firms are required to pay all creditors in order to reorganize under Chapter 11, I would expect an increase in this variable to lead to a decrease in the probability of reorganization.

SED/TL = Secured Interest Bearing Debt/Total Liabilities. As a firm’s debt increases, it must meet greater credit obligations in order for the bankruptcy court to approve a reorganization plan. Thus, I expect a negative coefficient for this variable to show that as secured debt increases, the probability of reorganization decreases.

NETPPE/TA = Net Property Plant and Equipment/Total Assets. For firms that need to increase cash flow during bankruptcy, real assets can be liquidated in order to repay the creditors, increasing the likelihood of reorganization. The expectation is that the coefficient is be positive.

PPERE/TA = (Buildings + Construction + Land Improvements)/Total Assets.

This variable is similar to NETPPE/TA, except that it is comprised of strictly tangible assets. Therefore, this variable reflects an even greater opportunity to increase liquidity through the sale of assets. I expect the coefficient for this variable to be positive to show that the probability of reorganization increases along with real asset value.

Results

The results are consistent with my hypothesis and confirm that there is a strong correlation between the existence of real assets and the probability that a firm will be able to reorganize and emerge from bankruptcy. Table 1 summarizes the descriptive statistics for the given variables. The information shows that of the 236 firms that were used in this study, approximately half reached a reorganization outcome and the other half resorted to liquidation.18 I expect each variable to have a minimum value no less than 0 and a maximum value no greater than 1, since the results are reported as proportions of a whole. While this proved correct for most of the variables, some returned outliers. For instance, NI/TA was negative for some of the firms, which could indicate that some firms were operating at a substantial loss, leading to a negative net income value. When analyzing INTA/S, we see a maximum value over 40, which is much larger than expected. However, we can surmise that some firms overvalued goodwill on their balance sheets, leading to a value exceeding 1.

Table 2 analyzes the statistical significance of the data results using t-statistics and determines if there is statistically significant evidence to reject the null hypothesis.

The null hypothesis states that the difference between the mean for each variable when a firm reorganizes and when it liquidates is equal to zero. Therefore, a negative t-stat indicates that the variable was greater during a liquidation outcome.

Null hypothesis: Mean (1) – Mean (0) = 0

18 See LexisNexis Academic (2011) 16

For each t-statistic, there is a corresponding p-value, which indicates if the results are statistically significant. Using this approach, Table 2 shows that LNTA, TD/TL,

NETPPE/TA, and PPERE/TA all have a p-value less than .10 and are statistically significant values. Therefore, a conclusion about the role of real assets in bankruptcy resolution can be reached. For both NETPPE/TA and PPERE/TA, the statistically significant t-tests indicate that there is a difference in the average value of real assets when a firm reorganizes versus liquidates. STATA describes the “1” in a binary regression model, so the results reveal that the average real asset value for reorganization was higher than the average value for liquidation. NI/TA, SED/TL, and INTA/S are all insignificant, and therefore we fail to reject the null hypothesis and conclude that there is no difference between the means.

Regression analysis is a tool to determine the relationship between individual variables and a dependent variable, known as the outcome in this study (reorganization=1 and liquidation=0). Two separate regressions were run during this study. The first logistic regression was run using NETPPE/TA as a proxy for real assets. The second regression substituted the variable PPERE/TA for NETPPE/TA.

Table 3 summarizes the results for the first set of data. Based on p-values and a 10 percent significance level, LNTA and NETPPE/TA are significant. All other variables are insignificant. Focusing on the statistically significant variables, LNTA has a coefficient less than one. The estimated coefficient for LNTA indicates that the log of the odds of reorganization increases 0.5 times for every one unit increase in LNTA. This is to be expected considering that larger firms have the opportunity to downsize and sell off

assets in order to meet liquidity needs. NETPPE/TA has a positive coefficient larger than two. The estimated coefficient for NETPPE/TA indicates that the log of the odds of reorganization increases 2 times for every one unit increase in NETPPE/TA. This confirms my hypothesis that real assets have a positive impact on the probability of reorganization

Turning to Table 4, the results summarize the logistic regression model focusing on the variable PPERE/TA. The signs of all of the coefficients remain the same, which is to be expected, as the variables are meant to represent similar values. Based on p-values and a 10 percent significance level, LNTA and PPERE/TA are significant. All other variables are insignificant and have expected coefficients of zero. The coefficient of

LNTA is positive and slightly less than .5. Therefore, this regression also concludes that as firm size increases, so does the likelihood of reorganization. The coefficient for

PPERE/TA is large and positive with a value greater than 2. Therefore, this regression analysis confirms that there is a positive relationship between the amount of real assets a firm has on its balance sheet prior to bankruptcy filing and the likelihood of reorganization.

When comparing the results in Table 4 to those in Table 3, I conclude that there is a stronger positive coefficient when using PPERE/TA as a focus variable. PPERE/TA is defined as the sum of buildings, construction, and land improvements. Excluded from this variable are inputs such as natural resources, construction in progress, and leases at cost. Therefore, PPERE could be described as being more “tangible” than NETPPE, which includes all property plant & equipment. While owning real assets will help a firm

potentially emerge from bankruptcy, the presence of assets such as buildings is more impactful than assets that are less tangible.

Table 3 and Table 4 both report the odds ratio for each variable. The odds ratio in the study shows us that for every 1 unit increase in the given variable, the odds of reorganization increases by a factor “x”. The odds ratios are only relevant for the statistically significant variables; LNTA, NETPPE/TA, and PPERE/TA, which all have odds ratios greater than 1. This shows that as the values for these variables increases by 1 unit, the odds of reorganization increases. NETPPE/TA returns an odds ratio slightly larger than 8. Thus, when the odds ratio increases by 1 unit, or 1%, the odds of reorganization increases by 8 times. Even stronger results are produced by PPERE/TA.

With an odds ratio greater than 16, small increases in PPERE/TA lead to significant changes in the chances that a firm will be able to reorganize.

Table 1: Descriptive Statistics19

The following table shows the total of observations, mean, standard deviation, minimum and maximum values for each variable.

Standard Variable Observations Mean Minimum Maximum Deviation Outcome 236 .5424 .4993 0 1

NI/TA 236 -.3530 .6210 -5.3315 .1405

LNTA 236 .5044 .8329 -1.3404 3.7955

INTA/S 236 .3598 2.6653 0 40.6928

TD/TL 236 .1344 .2177 0 .8535

SED/TL 236 .1945 .2452 0 .9876

NETPPE/TA 236 .3205 .2521 0 .9210

PPERE/TA 236 .0948 .1605 0 .9171

19 See Stata: Statistics/Data Analysis (2011) 20

Table 2: Univariate Comparisons of Variables and Tests of Significance20

The following table divides the data into two groups: reorganization and liquidation. The t-stat is based on a null hypothesis that the mean difference between the two groups is equal to zero. The p-value then interprets the significance of each t-stat.

Variable Reorganization Liquidation T-Stat P-Value -.3833 -.3275 NI/TA -.6863 .4932 (.5111) (.7015)

.3276 .6535 LNTA -3.0472 .0026 (.8157) (.8211)

.5926 .1633 INTA/S 1.2339 .2185 (3.9243) (.2915)

.1072 .1573 TD/TL -1.7696 .0781 (.2064) (.2251)

.1979 .1920 SED/TL .1849 .8535 (.2595) (.2335)

.2549 .3759 NETPPE/TA -3.7744 .0002 (.2323) (.2557)

.0619 .1227 PPERE/TA -2.9455 .0035 (.0427) (.1936)

128 108 # of Firms

20 See Stata: Statistics/Data Analysis (2011) 21

Table 3: Variable Logistic Regression Estimates Using Net Property Plant & Equipment21

The following table uses the variable NETPPE/TA to represent real assets. The table shows the coefficient, standard error, z-score, p-value, and odds ratio for each variable.

Standard Variable Coefficient Z-Score P-Value Odds Ratio Error NI/TA -.1942 .2438 -.8000 .4260 .8235

LNTA .5151 .1906 2.7000 .0070 1.6738

INTA/S -.2868 .3545 -.8100 .4190 .7507

TD/TL .7372 .7156 1.030 .3030 2.0901

SED/TL -.9854 .6249 -1.5800 .1150 .3733

NETPPE/TA 2.0839 .6130 3.4000 .0010 8.0355

21 See Stata: Statistics/Data Analysis (2011) 22

Table 4: Variable Logistic Regression Estimates Using Property Plant & Equipment Real Estate22

The following table uses the variable PPRERE/TA to represent real assets. The table shows the coefficient, standard error, z-score, p-value, and odds ratio for each variable.

Standard Variable Coefficient Z-Score P-Value Odds Ratio Error NI/TA -.2767 .2440 -1.1300 .2570 .7583

LNTA .4934 .1883 2.6200 .0090 1.6379

INTA/S -.3293 .3597 -.9200 .3600 .7194

TD/TL 1.0866 .6908 1.5700 .1160 2.9641

SED/TL -.7325 .6079 -1.2000 .2280 .4807

PPERE/TA 2.7870 1.0889 2.5600 .0100 16.2321

22 See Stata: Statistics/Data Analysis (2011) 23

Summary

Using NETPPE/TA and PPERE/TA to represent real assets, the data results show that there is a strong positive correlation between real assets and reorganization during bankruptcy filings. With large odds ratios and statistically significant t-stats at the 10 percent level, the data is consistent and conclusive that real assets have an impact on firms that are attempting to emerge from bankruptcy and continue operations.

Conclusion

In conclusion, real assets play a key role in bankruptcy resolutions. Based on historical data over the past decade, it is evident that firms with more real assets on their balance sheets prior to bankruptcy filing are in a better position to emerge without liquidation.

Barniv, Agarwal, and Leach (2002) conducted a similar study using both financial and non-financial accounting variables. They concluded that financial variables have no impact on bankruptcy resolution. Instead, factors such as fraud, resignation by top management, and the number of major classes of bond holders are influential in predicting bankruptcy resolution.23 However, the study did not include a financial variable to account for real assets. This study confirms that financial accounting variables other than real assets and LNTA are insignificant when predicting bankruptcy outcomes.

However, this study expands upon the research done by Barniv, Agarwal, and Leach

23 See Barniv, Agarwal, and Leach (2002) 24

(2002) and includes NETPPE/TA and PPERE/TA, which account for the presence of real assets.

The study confirms the hypothesis that real assets increase the likelihood of reorganization when a firm is going through a bankruptcy filing. The results reveal a strong positive correlation between real assets and the probability of reorganization and are statistically significant, supporting the rejection of the null hypothesis. The results of this study are relevant and important to creditors, investors, and management. There is uncertainty surrounding bankruptcy filings, as financing can be difficult to obtain and reorganization plans are not always feasible. Creditors are interested to know if they will be paid, equity investors seek guidance on the future of the firm, and management attempts to restructure the firm in the most effective ways. With real assets, the firm is able to liquidate business segments or use the real assets as collateral when seeking debtor in possession financing. Firms are wise to diversify investments and seek some protection from bankruptcy liquidation by owning real assets.

Appendix

Exhibit A: Sample

Company Name Ticker Symbol CUSIP Date Delisted Outcome

3DO CO THDOQ 88553W204 2002 Liquidated

800 TRAVEL SYSTEMS INC IFLYQ 282506104 2001 Liquidated

ABC-NACO INC ABCRQ 000752105 2001 Liquidated

ABITIBIBOWATER INC ABH 003687209 2009 Reorganized

ABLE LABORATORIES INC ABLSQ 00371N407 2005 Liquidated

ACT MANUFACTURING INC AMNUQ 000973107 2001 Reorganized

ADVANCED TISSUE SCI -CL A ATISZ 00755F103 2002 Liquidated

ADVANTA CORP -CL B ADVBQ 007942204 2009 Reorganized

AEROVOX INC ARVXQ 00808M105 2001 Liquidated

Company Name Ticker Symbol CUSIP Date Delisted Outcome

AGRIBIOTECH INC ABTXQ 008494106 2000 Reorganized

ALLEGIANCE TELECOM INC 3ALGXQ 01747T102 2003 Liquidated

ALLIED PRODUCTS ADPC 019411107 2000 Reorganized

ALSIUS CORP ALUS 021211107 2009 Liquidated

ALTA GOLD CO ATGDQ 021271101 1999 Liquidated

ALTERRA HEALTHCARE CORP ATHCQ 02146C104 2003 Reorganized

ALYN CORP ALYNQ 022611107 2000 Liquidated

AMER AIRCARRIERS SUPPORT INC AIRSQ 023758105 2000 Reorganized

AMER BUSINESS FINL SVCS INC ABFIQ 02476B106 2005 Reorganized

AMERICAN CLASSIC VOYAGES CO AMCVQ 024928103 2001 Liquidated

AMERICAN HOME MTG INVT CORP AHMIQ 02660R107 2007 Liquidated

AMPEX CORP/DE -CL A AMPXQ 032092306 2008 Reorganized

AMRESCO INC 3AMMBQ 031909203 2001 Liquidated

ANCHOR GLASS CONTAINER CORP AGCCQ 03304B300 2005 Reorganized

Company Name Ticker Symbol CUSIP Date Delisted Outcome

ANTEX BIOLOGICS INC ANXB 03672W308 2002 Liquidated

APPLIEDTHEORY CORP 3ATHYQ 03828R104 2002 Liquidated

ARMSTRONG HOLDINGS INC 3ACKH 042384107 2002 Reorganized

ASSISTED LIVING CONCEPTS INC ALC 04544X300 2001 Reorganized

ASYST TECHNOLOGIES INC ASYTQ 04648X107 2008 Liquidated

ATHEROGENICS INC AGIXQ 047439104 2008 Liquidated

AUSPEX SYSTEMS INC ASPXQ 052116100 2003 Liquidated

AVIZA TECHNOLOGY INC AVZAQ 05381A105 2009 Reorganized

BALDWIN PIANO & ORGAN CO BPAOQ 058246109 2001 Reorganized

BASIN WATER INC BWTRQ 07011T306 2009 Liquidated

BEYOND.COM CORP BYNDQ 08860E309 2001 Reorganized

BIOTRANSPLANT INC BTRNQ 09066Y107 2002 Liquidated

BORDEN CHEM&PLAST -LP COM 3BCPUE 099541203 2001 Liquidated

BREED TECHNOLOGIES INC BDTTZ 106702103 1999 Reorganized

Company Name Ticker Symbol CUSIP Date Delisted Outcome

BROOKE CORP BXXXQ 112502109 2008 Reorganized

BURLINGTON INDUSTRIES INC 3BRLGQ 121693105 2001 Reorganized

BUSH INDUSTRIES -CL A BINDQ 123164105 2003 Reorganized

CARAUSTAR INDUSTRIES INC CSARQ 140909102 2009 Reorganized

CARBIDE/GRAPHITE GROUP INC CGGIQ 140777103 2001 Liquidated

CELLNET DATA SYSTEMS INC CNTDQ 15115M101 1999 Liquidated

CHAMPION ENTERPRISES INC CJHBQ 158496109 2009 Liquidated

CHARTER COMMUNICATIONS INC CHTR 16117M305 2009 Reorganized

CHS ELECTRONICS INC CHSWQ 12542A206 1999 Liquidated

CIRCUIT CITY STORES INC CCTYQ 172737108 2007 Liquidated

CIT GROUP INC CIT 125581801 2009 Reorganized

CLASSIC COMMUNICATIONS INC 3CLSCQ 182728204 2001 Reorganized

COMMERCE ONE INC CMRCQ 200693208 2004 Reorganized

COMPUTER LEARNING CTRS INC CLCXQ 205199102 2000 Liquidated

Company Name Ticker Symbol CUSIP Date Delisted Outcome

CONE MILLS CORP CJML 206814105 2003 Liquidated

CONSTAR INTERNATIONAL INC CNST 21036U206 2009 Reorganized

COOPERATIVE BANKSHARES INC COOPQ 216844100 2009 Liquidated

COVANTA ENERGY CORP CVGYQ 22281N103 2002 Reorganized

CREDIT STORE INC CDSEQ 22539C107 2002 Liquidated

CROSS MEDIA MARKETING CORP CMKC 22754R201 2003 Liquidated

CYBERCASH INC CYCHZ 232462101 2000 Liquidated

DAIRY MART CONVENIENCE STRS 3DMCSQ 233860303 2001 Reorganized

DAISYTEK INTL CORP DZTKQ 234053106 2002 Liquidated

DAYTON SUPERIOR CORP DSUPQ 240028308 2009 Reorganized

DELPHI CORP DPHIQ 247126105 2005 Reorganized

DELTA FINANCIAL CORP DFCLQ 247918105 2007 Liquidated

DIVINE INC -CL A DVINQ 255402406 2002 Liquidated

DT INDUSTRIES INC DTIIQ 23333J108 2004 Liquidated

Company Name Ticker Symbol CUSIP Date Delisted Outcome

DVI INC DVIXQ 233343102 2003 Liquidated

EAGLE GEOPHYSICAL INC EAGL10 269524104 1999 Reorganized

EASYRIDERS INC EYRDQ 277848107 2001 Liquidated

EDDIE BAUER HOLDINGS INC EBHIQ 071625107 2009 Liquidated

EDGE PETROLEUM CORP EPEXQ 279862106 2009 Reorganized

EDISON BROTHERS STORES EDBR. 280875303 1998 Liquidated

ENRON CORP ENRNQ 293561106 2001 Reorganized

ETOYS INC 3ETYSQ 297862104 2000 Liquidated

EVOLVE SOFTWARE INC EVLVQ 30049P708 2003 Liquidated

FACTORY CARD OUTLET & PTY CP FCPO 303051106 1999 Reorganized

FAIRPOINT COMMUNICATIONS INC FRCMQ 305560104 2009 Reorganized

FAMILY GOLF CENTERS INC FGCIQ 30701A106 2000 Reorganized

FANSTEEL INC/DE FELI 307260307 2001 Reorganized

FIBERMARK INC 3FMKIQ 315646109 2004 Reorganized

Company Name Ticker Symbol CUSIP Date Delisted Outcome

FIBROCELL SCIENCE INC 3FCSC 315721100 2009 Reorganized

FINOVA GROUP INC 3FNVG 317928109 2002 Liquidated

FIRST ALLIANCE CORP/DE FACOQ 317936102 2000 Liquidated

FIRST STATE FINANCIAL CP/FL FSTF 33708M206 2009 Reorganized

FLAG TELECOM GROUP LTD FTGLF G3529X106 2002 Reorganized

FLEMING COMPANIES INC FLMIQ 339130106 2002 Liquidated

FLYI INC FLYIQ 34407T104 2005 Liquidated

FOAMEX INTERNATIONAL INC FMXLQ 344123203 2005 Liquidated

FOURTHSTAGE TECHNOLOGIES INC 3FOURQ 35112T107 2001 Reorganized

FRESH CHOICE INC SALDQ 358032100 2004 Reorganized

FRONTLINE CAPITAL GROUP FLCGQ 35921N101 2002 Reorganized

GC COMPANIES INC GCCXQ 36155Q109 2001 Reorganized

GENERAL GROWTH PPTYS INC GGP 370023103 2009 Reorganized

GENERAL MOTORS CO GM 37045V100 2009 Liquidated

Company Name Ticker Symbol CUSIP Date Delisted Outcome

GENESIS DIRECT INC GDIRQ 371935107 1999 Reorganized

GENON ENERGY INC GEN 37244E107 2003 Reorganized

GENUITY INC GENUQ 37248E202 2002 Liquidated

GLOBAL ENERGY HOLDINGS GRP GLNHQ 37991A100 2009 Reorganized

GLOBAL POWER EQUIPMENT GROUP GLPW 37941P306 2006 Reorganized

GOLDEN MINERALS CO AUMN 381119106 2009 Reorganized

GRAND COURT LIFESTYLES INC GCLIQ 385379102 1999 Reorganized

GST TELECOMM INC 3GSTXQ 361942105 2000 Liquidated

HANCOCK FABRICS INC HKFI 409900107 2007 Reorganized

HAYES LEMMERZ INTL INC HAYZQ 420781304 2009 Reorganized

HEALTHCENTRAL.COM HCENQ 42221V403 2001 Liquidated

HEILIG-MEYERS CO HMYRQ 422893107 2000 Liquidated

HMG WORLDWIDE CORP HMGCQ 404235103 2001 Reorganized

HOUSE2HOME INC 3HTHEQ 44183S105 2001 Liquidated

Company Name Ticker Symbol CUSIP Date Delisted Outcome

HYDROGEN CORP HYDGQ 44887Q108 2008 Liquidated

IBEAM BROADCASTING CORP IBEMQ 45073P408 2001 Liquidated

ILX RESORTS INC ILXRQ 449661503 2010 Liquidated

IMPERIAL SUGAR CO IPSU 453096208 2001 Reorganized

INSPIRE INS SOLUTIONS INC 3NSPRQ 457732105 2002 Reorganized

INTEGRATED TELECOM EXPRESS 3ITXIQ 45817U101 2002 Liquidated

INTERMET CORP INMTQ 45881K104 2004 Reorganized

INTERNET COMMERCE & COMMUNIC ICCXQ 46061G103 2001 Reorganized

INTERSTATE BAKERIES CORP IBCIQ 46072H108 2004 Reorganized

IRWIN FINANCIAL CORP IRWNQ 464119106 2009 Liquidated

ITC DELTACOM INC 3ITCD 45031T872 2002 Reorganized

JACOBSON STORES JCBSQ 469834105 2001 Liquidated

JORE CORP JOREQ 480815109 2001 Reorganized

JOY GLOBAL INC JOYG 481165108 1999 Reorganized

Company Name Ticker Symbol CUSIP Date Delisted Outcome

JUST FOR FEET INC FEETQ 48213P106 1999 Reorganized

KEVCO INC KVCO 492716105 2000 Liquidated

KRAUSES FURNITURE INC KAUSQ 500760202 2001 Reorganized

LEADIS TECHNOLOGY INC LDIS 52171N103 2009 Liquidated

LEAR CORP LEA 521865204 2009 Reorganized

LEHMAN BROTHERS HOLDINGS INC LEHMQ 524908100 2008 Liquidated

LTV CORP LTVCQ 501921100 2000 Reorganized

LUMENON INNOVATIVE LIGHTWAVE LUMM 55024L109 2003 Reorganized

LUMINANT WORLDWIDE CORP LUMTQ 550260103 2001 Liquidated

MAGNA ENTERTAINMENT CORP MECAQ 559211305 2008 Reorganized

MARCHFIRST INC MRCHQ 566244109 2000 Liquidated

MCLEODUSA INC -CL A MCLD 582266706 2002 Reorganized

MERUELO MADDUX PROPERTIES MMPIQ 590473104 2009 Reorganized

MICROAGE INC MICAQ 594928103 2000 Reorganized

Company Name Ticker Symbol CUSIP Date Delisted Outcome

MIDWAY AIRLINES CORP MDWYQ 598126100 2001 Reorganized

MIDWAY GAMES INC MWYGQ 598148104 2009 Liquidated

MPC CORP MPCCQ 553166109 2008 Liquidated

NATIONAL VISION INC NVI 63845P101 2000 Reorganized

NEIGHBORCARE INC NCRX 64015Y104 2000 Reorganized

NESCO INC NESCQ 640825105 2001 Reorganized

NET2000 COMMUNICATIONS INC NTKKQ 64122G103 2001 Liquidated

NEWCARE HEALTH CORP NWCA 651053100 1999 Reorganized

NEWCOR INC NER.3 651186108 2002 Reorganized

NORTEL NETWORKS CORP NRTLQ 656568508 2009 Reorganized

NORTH AMERICAN SCIENTIFIC NASMQ 65715D209 2009 Liquidated

NORTHFIELD LABORATORIES INC NFLDQ 666135108 2008 Liquidated

NORTHWEST AIRLINES CORP NWA 667280408 2005 Reorganized

NORTHWESTERN CORP NWE 668074305 2003 Reorganized

Company Name Ticker Symbol CUSIP Date Delisted Outcome

NOVA BIOSOURCE FUELS INC NBFAQ 65488W103 2009 Reorganized

NTELOS INC 3NTLOQ 67019U101 2003 Reorganized

NX NETWORKS INC NXWXQ 629478108 2001 Reorganized

OAKWOOD HOMES CORP OKWHQ 674098207 2002 Liquidated

OGLEBAY NORTON CO OGBY 677007205 2004 Reorganized

OWENS CORNING OC 690742101 2002 Reorganized

PAGING NETWORK INC PAGE 695542100 2000 Reorganized

PAUL HARRIS STORES PAUHQ 703555201 2000 Reorganized

PENN TRAFFIC CO PTFCQ 707832309 2003 Liquidated

PHOTOELECTRON CORP 3PECN 719320103 2002 Liquidated

PILGRIM'S PRIDE CORP PPC 72147K108 2008 Reorganized

PILLOWTEX CORP PWTXQ 721506103 2000 Liquidated

PLANET HOLLYWOOD INTL INC PHWDQ 72702Q102 1999 Reorganized

PLASTIC SURGERY CO 3PSUG 727557100 2001 Reorganized

Company Name Ticker Symbol CUSIP Date Delisted Outcome

PLUMA INC PLUAQ 729272104 1999 Liquidated

POLYMER GROUP INC 3POLGA 731745204 2002 Reorganized

PROBEX CORP 3PRBX 742670201 2003 Liquidated

PROLIANCE INTERNATIONAL INC PLNTQ 74340R104 2009 Liquidated

PROXYMED INC PILLQ 744290305 2008 Liquidated

QUESTRON TECHNOLOGY INC QUSTQ 748372208 2001 Liquidated

RAILWORKS CORP RWKSQ 750789109 2001 Reorganized

RECOTON CORP RCOTQ 756268108 2002 Reorganized

ROADHOUSE GRILL INC 3GRLL 769725102 2002 Reorganized

SAFETY-KLEEN CORP SKLNQ 78648R203 2000 Reorganized

SAMES CORP SMCO 79587E104 2001 Liquidated

SANTA FE GOLD CORP 3SFEG 80201E108 2003 Reorganized

SCHLOTZSKY'S INC BUNZQ 806832101 2004 Liquidated

SCIENT INC SCNTQ 808649305 2002 Liquidated

Company Name Ticker Symbol CUSIP Date Delisted Outcome

SEABULK INTERNATIONAL INC SBLK.1 81169P101 1999 Reorganized

SEARS HOLDINGS CORP SHLD 812350106 2002 Reorganized

SECURITY BANK CORP SBKCQ 814047106 2009 Liquidated

SHARPER IMAGE CORP SHRPQ 820013100 2007 Liquidated

SHELDAHL INC SHELQ 822440103 2002 Reorganized

SIMON TRNSPT SVCS INC -CL A SIMNQ 828813105 2002 Liquidated

SIRENA APPAREL GROUP INC SIRNQ 82966Q102 1999 Reorganized

SOLUTIA INC SOA 834376501 2003 Reorganized

SONICBLUE INC SBLUQ 83546Q109 2003 Liquidated

SOUTH TEXAS OIL CO STXXQ 84055V109 2009 Reorganized

SOUTHWESTERN LIFE HLDGS INC SWLH 845606102 2000 Reorganized

SPECIAL METALS CORP SMCXQ 84741Y103 2002 Reorganized

SPECTRASITE INC SSI.2 84761M104 2002 Reorganized

SPINNAKER INDS INC -CL A SNNKA 848926101 2001 Reorganized

Company Name Ticker Symbol CUSIP Date Delisted Outcome

STAGE STORES INC SSI 85254C305 2000 Reorganized

STANDARD AUTOMOTIVE CORP SAUC 853097103 2001 Reorganized

STM WIRELESS INC STMIQ 784776106 2002 Liquidated

STONE & WEBSTER INC SWBIQ 861572105 2000 Reorganized

STROUDS INC SOUDQ 863451100 2000 Liquidated

SUNTERRA CORP SNRR.1 86787D208 2000 Reorganized

SYNTAX-BRILLIAN CORP BRLCQ 87163L103 2008 Liquidated

T & W FINANCIAL CORP TWFCQ 87215N107 1999 Liquidated

TANDYCRAFTS INC TACR 875386104 2000 Liquidated

TARRAGON CORP TARRQ 876287103 2008 Reorganized

TEAM FINANCIAL INC TFINQ 87815X109 2008 Reorganized

TELSCAPE INTERNATIONAL INC TSCPQ 87971Q104 2001 Liquidated

TETON ENERGY CORP TECJQ 881628101 2009 Liquidated

TLC VISION CORP TLCV 872549100 2009 Reorganized

Company Name Ticker Symbol CUSIP Date Delisted Outcome

TMCI ELECTRONICS INC TMEIQ 872933106 1998 Reorganized

TOKHEIM CORP THMC 889073201 2000 Reorganized

TORCH OFFSHORE INC TORCQ 891019101 2004 Reorganized

TOWER AUTOMOTIVE INC TWRAQ 891707101 2005 Liquidated

TRANSTEXAS GAS CORP 3TTXGQ 893895201 1999 Reorganized

TREND-LINES INC -CL A TRNDQ 894859107 2000 Reorganized

TROPICAL SPORTSWEAR INTL CP TSICQ 89708P102 2004 Liquidated

TRUMP ENTERTAINMENT RESORTS TRMPQ 89816T103 2009 Reorganized

TULTEX CORP TLTXQ 899900104 1999 Reorganized

TVI CORP TVINQ 872916101 2008 Reorganized

TXCO RESOURCES INC TXCOQ 87311M102 2009 Reorganized

UBRANDIT.COM 3UBDT 90347Q105 2001 Liquidated

ULTIMATE ELECTRONICS INC ULTEQ 903849107 2004 Liquidated

UNAPIX ENTERTAINMENT INC UPXE 904270105 2000 Liquidated

Company Name Ticker Symbol CUSIP Date Delisted Outcome

UNIROYAL TECHNOLOGY CORP UTCIQ 909163107 2002 Liquidated

UNITED COS FINANCIAL CORP UCFNQ 909870107 1999 Liquidated

UNITEL VIDEO INC UTLV 913253100 1999 Liquidated

UNIVERSAL ACCESS GLOBAL HLDG 5069B 91399W933 2004 Liquidated

URSUS TELECOM CORP UTCCQ 917287104 2000 Reorganized

USA BIOMASS CORP UBMSQ 90333F105 2001 Liquidated

VERASUN ENERGY CORP VSUNQ 92336G106 2008 Liquidated

VERILINK CORP 3VERL 923432306 2006 Liquidated

VERSO TECHNOLOGIES INC VRSOQ 925317208 2008 Liquidated

VLASIC FOODS INTERNATIONAL 3VLFIQ 928559103 2001 Liquidated

WARNACO GROUP INC WRC 934390402 2001 Reorganized

WASHINGTON GROUP INTL INC WNG 938862208 2001 Reorganized

WHITE RIVER CAPITAL INC RVR 96445P105 2002 Reorganized

WHX CORP WXCO 929248607 2005 Reorganized

Company Name Ticker Symbol CUSIP Date Delisted Outcome

WINN-DIXIE STORES INC WINN 974280307 2005 Reorganized

WORLDSPACE INC WRSPQ 981579105 2008 Liquidated

WSB FINANCIAL GROUP INC WSFGQ 92933E108 2009 Liquidated

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Academic Vita of Tessa Stubler

Name: Tessa Stubler

Address: 582 Mosteller Road Trout Run, PA 17771

E-Mail Address: [email protected]

Education: Major: Finance Honors: Finance

Thesis Title: The Role of Real Assets in Bankruptcy Resolution Thesis Supervisor: Professor Brent Ambrose

Work Experience:

Date: Summer 2009 Title: Advisory Group Analyst Description: Prepared financial analysis for clients, attended client conference calls and presentations on estate planning and hedge funds Institution/Company: Convergent Wealth Advisors Supervisor: Ken Handy

Date: Summer 2010 Title: Summer Analyst Description: Prepared pitches addressing current market outlook, firm credentials, syndicate structure, and the initial public offering process Institution/Company: Goldman Sachs Supervisor: Andrew Flahive

International Education:

Date: Summer 2008 Title: Volunteer Description: Traveled to India in summer 2008 to teach English as a second language to Indian children and participated in community projects Institution/Company: HOINA Service Learning Program