Data Envelopment Analysis of Corporate Failure for Non- Manufacturing Firms using a Slacks-Based Model

Transcription

1 Data Envelopment Analysis of Corporate Failure for Non Manufacturing Firms using a SlacksBased Model By D Andre Wilson Supervised by Dr. Joseph C. Paradi A thesis submitted in conformity with the requirements for the degree of Masters of Applied Science The Centre for Management of Technology and Entrepreneurship Graduate Department of Chemical Engineering and Applied Chemistry University of Toronto Copyright 2012

2 Data Envelopment Analysis of NonManufacturing Firms Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms using a SlacksBased Model Abstract D Andre Wilson Masters of Applied Science Department of Chemical Engineering and Applied Chemistry University of Toronto 2012 The purpose of this work was to study the ability of the SlacksBased Model of Data Envelopment Analysis in the prediction of corporate failure of nonmanufacturing companies as compared to Altman s Z score model. This research looks at nonmanufacturing firms specifically and attempts to classify companies without looking at the asset size of the firm. A DEA model based on the Altman s Z score financial ratios was created as well as a revised DEA model. The overall accuracy of the models showed the revised DEA model to be more accurate than the original DEA model as well as the Altman Z score. This indicated that bankruptcy could be predicted without the use of total assets or liabilities as variables. This also showed the ability of an SBM DEA model to predict bankruptcy. ii

3 Abstract The purpose of this work was to study the ability of the SlacksBased Model of Data Envelopment Analysis in the prediction of corporate failure of nonmanufacturing companies as compared to Altman s Z score model. DEA had been tested for corporate failure before, however the DEA model used was a BCC model and this was tested against Altman s original Z score model, which is an assetdominated model. This research looks at nonmanufacturing firms specifically and attempts to classify companies without looking at the asset size of the firm. Data was collected on nonmanufacturing companies that filed for bankruptcy between 2000 and 2006 for up to five years before bankruptcy. Nonbankrupt companies were found to match these, based on the SIC codes of the companies. The following data was collected for each company from the financial statements: Current Assets, Current Liabilities, Working Capital, Retained Earnings, Operating Income, Book Value of Equity, Total Assets, Total Liabilities, Number of Employees, and Number of Shareholders. The means of these variables were compared and this showed that the nonbankrupt companies had larger averages for all of these variables, except for the number of employees. The data was split into two groups, one to create the DEA model and determine a cutoff point and the other to test that cutoff point. The Altman Z model used the following formula. iii

4 The EBIT was replaced with Operating Income for this study due to lack of data. The first DEA model utilised the ratios from the Z score and made all numerators outputs and all denominators inputs. A second DEA model was created to exclude total assets and liabilities, to show that bankruptcy can be determined without the total asset size of the firm. The working capital was split into current assets and current liabilities for this second model and two new variables were also added, the total number of employees as an input and the total number of shareholders as an output. Altman s model classified more companies as bankrupt than DEA, whereas DEA classified more as nonbankrupt. Altman had a lower type I error in the first and second years before bankruptcy but DEA had lower type I error in the preceding years. DEA had a lower type II error overall. The overall accuracy of the models showed the revised DEA model to be more accurate than the original DEA model as well as the Altman Z score. This indicated that bankruptcy could be predicted without the use of total assets or liabilities as variables. This also showed the ability of an SBM DEA model to predict bankruptcy. All models had an increase in accuracy as the time before bankruptcy increased, though the revised DEA model had the highest accuracy in the th year before bankruptcy, showing it as a better model for predicting bankruptcy further before the date of bankruptcy. iv

5 Acknowledgements I would first and foremost like to thank Dr. Joseph Paradi, for all of his guidance and advice, as well as for doing everything possible to help me succeed. I truly could not have completed this without him and I am very grateful to him for sticking with me through the ups and downs of this past year and a half. I would like to thank all of the members of CMTE who helped me along the way and for all being so supportive when I needed them, in particular Angela Tran for making herself available when I needed her, as well as Haiyan Zhu for her advice. I would like to thank my family at the National Society of Black Engineers, the Engineering Outreach Office, and the Leaders of Tomorrow for helping to enrich my time at the University of Toronto. I would especially like to thank Nnaziri Ihejirika, for his constant support throughout my journey, for putting up with all of my mood swings and for always being there for me when I needed him. I would like to thank my parents, Larry Wilson and Fran DilletWilson, as well as my siblings Chauntez, Chadeau and Jazz for always supporting me no matter what. Most of all, I would like to thank God, because through Him all things are possible. v

6 Table of Contents Abstract... iii Acknowledgements... v Table of Contents... vi Table of Figures... ix List of Tables... x List of Symbols... xi Chapter 1: Introduction... 1 Thesis Structure... 2 Chapter 2: Literature Review... 4 Beaver s Univariate Study... 4 Altman s Multivariate Model... 6 Subsequent models... 8 Data Envelopment Analysis in Bankruptcy prediction Summary of Literature Review Chapter 3: Data Envelopment Analysis Charnes Cooper Rhodes model BankerCharnesCooper model SlacksBased Model... 2 Advantages/Disadvantages of DEA Chapter 4: Model Development Dealing with Negative Values Model Revision Chapter : Data Acquisition Chapter 6: Results and Discussion Univariate Analysis Second dataset Altman Z results Second group vi

7 DEA Model Revised DEA Model... Comparison of models... 8 Comments on DEA scores Chapter 7: Conclusion and Recommendations Conclusions Recommendations for Future Work... 6 References Appendix A: List of companies Group Group Appendix B: Financial Data Group year before bankruptcy years before bankruptcy years before bankruptcy years before bankruptcy... 9 years before bankruptcy Group year before bankruptcy years before bankruptcy years before bankruptcy years before bankruptcy years before bankruptcy Appendix C: List of paired companies Appendix D: List of Altman Z Scores Group Group Appendix E: List of DEA Scores for Original Model vii

8 Group Group Appendix F: List of DEA Scores for Revised Model Group Group Appendix G: Tstatistics for comparison of means Group Group viii

9 Table of Figures Figure 1: Simak Model Figure 2: Simak Model Figure 3: Simak Model Figure 4: DEA Example Figure : DEA BCC vs CCR efficient frontier Figure 6: Slacks Based Model Figure 7: Profile Analysis of Variables in first DEA model Figure 8: Profile Analysis of remaining variables for revised DEA model Figure 9: Percent error of Altman Z'' Score Figure 10: Classification accuracy of Altman Z'' Model... 4 Figure 11: Classification accuracy of Altman Z'' model including grey area Figure 12: Total classification within each zone of Altman Z'' model Figure 13: Cutoff point determination on year 1 results Figure 14: Closer look at cutoff point for year Figure 1: Cutoff point on year Figure 16: Cut off point for up to years before bankruptcy... 2 Figure 17: Average DEA scores for group 1 first DEA model... 3 Figure 18: Cutoff points for up to years before bankruptcy for revised model... 6 Figure 19: Average DEA scores for group 1 in model Figure 20: Comparison of overall accuracies... 9 Figure 21: Comparison of bankrupt and nonbankrupt classification accuracies... 9 Figure 22: Comparison of type I and type II error Figure 23: Comparison of percent classifications ix

10 List of Tables Table 1: DEA Example Table 2: Number of companies in group Table 3: Number of companies in group Table 4: Number of companies in group 1 used in revised model Table : Number of companies in group 2 used in revised model Table 6: Profile analysis of group 1 bankrupt companies Table 7: Profile Analysis of group 1 nonbankrupt companies Table 8: Profile Analysis of bankrupt companies in group Table 9: Profile Analysis of Nonbankrupt companies in group Table 10: Results of Altman Z'' Model on group Table 11: Results of Altman Z'' model on group Table 12: Cutoff points for first DEA model... 4 Table 13: Classification accuracies of group 1 used to determine cutoff... 4 Table 14: Classification accuracies of cutoff point for model 1 tested on group 2... Table 1: Intersection points for each year of model Table 16: Cutoff points for revised DEA model... 7 Table 17: Classification accuracies used to determine cutoff points for revised model... 7 Table 18: Classification accuracies of cutoff points for revised model on group x

11 List of Symbols Z Altman's Z Score Z" Altman's Z" Score for nonmanufacturing firms EBIT Earnings before Income and Taxes WC Working Capital RE Retained Earnings OI Operating Income BVE Book Value of Equity TA Total Assets TL Total Liabilities CA Current Assets CL Current Liabilities SH Number of Shareholders EM Number of Employees Efficiency CCR DEA Score Input Oriented CCR DEA Score Output Oriented Coefficient for DEA output Coefficient for DEA input DEA output DEA input BCC DEA Score input oriented DEA Score dual multiplier form Vector of DEA inputs Vector of DEA outputs Row vector with all elements unity BCC DEA Score output oriented Vector of coefficients for DEA inputs and outputs Negative DEA slacks Positive DEA slacks DEA Score for Slacks Based Model xi

12 Chapter 1: Introduction When an individual or group is managing a company or investing in a company, something of great importance is the corporate health of that company. A very valuable piece of information would be if that company is headed for corporate financial stress or failure. In the past, many have attempted to predict corporate failure before it occurs. One of the most prevalent methods is to use financial ratios to determine the health of a company. A number of studies have been done to use the information from financial statements, particularly financial ratios to predict failure [BEAV67]. A prominent method of predicting bankruptcy is the Altman Z score [ALTM68]. Edward Altman used Multiple Discriminant Analysis to create a model that uses basic financial ratios in a linear formula to give a score. This score is used to determine whether a company is at risk of corporate stress or failure, whether they are healthy or whether they are classified in an undetermined zone or grey area. Later, in 1997, Paul Simak and Joseph Paradi attempted to compare this method to another analysis type, DEA or Data Envelopment Analysis [SIMA97]. This method used the BCC version of the DEA model and showed that this model was preferable to Altman ZScore for predicting bankruptcy of companies up to 3 years before the bankruptcy date. The problem with these methods is that they were generalized and in particular, did not look at firms that were nonmanufacturing, i.e. there was a large focus on the assetsize of the firms involved [GRIC01B] [HILL04]. In more recent times, more companies are nonmanufacturing Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 1

13 and are more service oriented and thus have less focus on the overall assetsize of the company [WORL09]. In response to the focus on manufacturing companies in his original model, Altman created another model, the Altman Z score [ALTM02]. With this score he tested nonmanufacturing firms and redeveloped the coefficients and cutoff points of the original model to suit. This model however, is still substantially based on assets, though more companies are becoming focused on service, are people driven, and do not have a large asset base [WORL09]. Thus an investigation of the Altman Z score for nonmanufacturing firms was done here. This research tests the following: The feasibility/accuracy of DEA as a predictor of bankruptcy in nonmanufacturing firms as compared to the Altman Z model The effectiveness of a SlacksBased DEA model as opposed to a BCC DEA model for bankruptcy prediction The accuracy of bankruptcy prediction without regard to the total asset size of a company and to add a 'human capital' value that focuses on the serviceoriented side of nonmanufacturing firms. The effectiveness of both Altman and DEA in the prediction of bankruptcy for up to five years before the bankruptcy date. Thesis Structure This thesis is structured as follows: Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 2

14 o Chapter 2 outlines key papers that have contributed to bankruptcy prediction literature with the use of financial statements. o Chapter 3 outlines Data Envelopment Analysis as an efficiency measurement technique, giving an overview of different DEA models as well as mathematical formulas, key terminology and appropriate examples. o Chapter 4 outlines the method for developing the model and the reasoning behind the innovations that were made to the model. o Chapter outlines the method for data acquisition and summarizes the quality of the data used. o Chapter 6 presents the results of the analysis and comments on the findings. o Chapter 7 summarizes the key findings and suggests recommendations and potential areas for further research. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 3

15 Chapter 2: Literature Review Having an understanding of the studies related to bankruptcy prediction and analysis techniques are imperative to create a model that is not redundant in literature and that can contribute to the existing knowledge of analysis of corporate failure. This section outlines some of the most relevant findings with bankruptcy prediction and summarizes the implications of this research based on those findings. Beaver s Univariate Study One of the first attempts to predict insolvency or bankruptcy was done by William Beaver in 1967 [BEAV67]. Beaver defined failure as the inability of a firm to pay its financial obligations as they mature and a financial ratio as a quotient of two numbers, where both numbers consist of financial statement items. He also introduced a third term predictive ability which is essentially the usefulness of a data item in identifying an event before it occurs [BEAV67]. Beaver collected data from Moody s industrial manual between 194 and 1964, inclusive. Each failed firm from Moody s was compared to a nonfailed firm in the same industry of the same asset size. At the time there was statistical reason to believe that a larger of two firms will have less probability of failure even if they have identical financial ratios. Therefore he believed that firms of different assetsizes could not be accurately compared [ALEX49]. Beaver compiled 30 ratios and showed 14 to be the most effective: Cash flow/total debt Net income/total assets Total debt/total assets Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 4

16 Current assets/total assets Quick assets/total assets Working capital/total assets Cash/total assets Current assets/current liabilities Quick assets/current liabilities Cash/current liabilities Current assets/sales Quick assets/sales Working capital/sales Cash/sales Beaver s results ultimately showed cashflow to total debt ratio as the best predictor, with total debt to total assets as second best. He noted that the most crucial factor was the net liquid asset flow supplied to the reservoir while the size of the reservoir was the least important factor. Beaver also visited the concept of likelihood ratios. The likelihood ratio (LR) is the ratio of these to values. ( ) ( ) (1.1) Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms

17 ( ) ( ) A likelihood ratio could be found for every interval for each of the financial ratios in each year before bankruptcy. However, Beaver was inconclusive in his analysis of the likelihood ratios. He stated that in the year before failure the likelihood ratio mirrored the financial ratio, however in years before that the results varied greatly [BEAV67]. He also stated that though his work was univariate, it would be valuable to consider a multivariate approach. This is where Altman stepped in [ALTM68]. Altman s Multivariate Model In 1968, Edward Altman attempted the first multivariate approach to bankruptcy prediction. The analysis technique that he adopted was MDA, multiple discriminant analysis. In Altman s time, MDA was not as popular as regression analysis and was used mainly in biological and behavioural sciences [ALTM68]. MDA is a statistical technique used to classify an observation into one of several a priori groupings dependent upon the observation s individual characteristics. [ALTM68]. It is usually used to classify the variable into a qualitative group e.g. male or female, bankrupt or nonbankrupt [ALTM68]. The process used for MDA was first to establish groups, which could be more than two, and then collect data for objects within each of those groups. Then a linear combination is created from the data collected that will best discriminate between the groups. This is done by assigning coefficients to each piece of data. For the case of bankruptcy, a coefficient is assigned Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 6

18 to each financial ratio chosen and the output of the linear combination is a number that can assign a firm into bankrupt or nonbankrupt. MDA allowed for the entire profile of variables to be analyzed simultaneously rather than individually [ALTM02]. To develop the model Altman took a sample of 66 corporations with 33 firms in the bankrupt group and 33 in the nonbankrupt group. All bankrupt firms were manufacturers that filed a bankruptcy petition under Chapter 11 of the National Bankruptcy Act between 1946 and 196. The nonbankrupt firms were selected by a paired sample method (similar to Beaver) [BEAV67]. A list of 22 potential ratios was compiled which were split into five standard ratio categories: liquidity, profitability, leverage, solvency and activity ratios. From the list of 22, five ratios were selected to be able to do the best overall job at collectively predicting bankruptcy. These were selected based on: (1) statistical significance of various potential functions while determining the relative contribution of each individual variable, (2) the intercorrelation between the variables, (3) the predictive accuracy of various profiles and (4) judgement of the analysis. [ALTM68] The final model was: (1.2) Where: (1.2a) (1.2b) Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 7

19 (1.2d) (1.2e) With cutoff zones: Altman found a classification accuracy of 83.% for his model and he showed that his model could predict bankruptcy up to three years before the bankruptcy date. Subsequent models Later in 1972, Edward Deakin revisited Beaver s analysis. [DEAK72] He used the 14 ratios that Beaver found to be most effective and attempted to use a discriminant analysis similar to Altman s. Deakin also attempted to look at data up to years before the date of bankruptcy. In his analysis he found that the significance for each ratio changed across the years. And he found that he was only able to get significant prediction result for up to 3 years before the date of bankruptcy. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 8

20 In 1980, James Ohlson attempted an alternative method of bankruptcy prediction using a probabilistic approach [OHLS80]. Ohlson looked at data between 1970 and Essentially for this method he looked at the vector of financial ratios and determined a vector of parameters for those ratios and looked at the probability of bankruptcy for those ratios and parameters. He then attempted to find a cutoff probability point between zero and one for bankruptcy and nonbankruptcy. The ratios that Ohlson employed were: Size = log(total assets/gnp pricelevel index) TLTA = Total liabilities divided by total assets WCTA = Working capital divided by total assets CLCA = Current liabilities divided by current assets ONENEG = One if total liabilities exceeds total assets, zero otherwise NITA = Net income divided by total assets FUTL = Funds provided by operations divided by total liabilities INTWO = One if net income was negative for the last two years, zero otherwise CHIN (NIt NIti)/(INItI + INItil), where NIt is net income for the most recent period. The denominator acts as a level indicator. The variable is thus intended to measure change in net income. Ohlson, however did not find promising results with this model as compared to Altman s model and thus is not commonly used today. In 1984, Zmijewski explored the potential methodological drawbacks of the previous bankruptcy prediction techniques [ZMIJ84]. His main issue was that previous studies that had used nonrandom samples, i.e. bankrupt and nonbankrupt groups had predelineated before modeling. Zmijewski attempted to use random sampling and incorporated a probit model to Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 9

21 test bankruptcy. The firms chosen for this study were from the American and New York Stock Exchanges with SIC codes of less than 6000 and were obtained between 1972 through What Zmijewski did was to create a variable B where if B>0 then the company is at risk of bankruptcy. His model is below: ( ) ( ) (1.3b) Where : o ROA = Net Income to Total Assets (Return on Assets) o FINL = Total Debt to Total Assets (Financial Leverage) o LIQ = Current Assets to Current Liabilities (Liquidity) o u = Normally Distributed Error term. However, Zmijewski found that his results were qualitatively similar to those that use nonrandom sampling and that there was no apparent improvement on the overall classification rates [ZMIJ84]. In the 90s there were many critiques of bankruptcy prediction. In 1993, SuJane Hsieh, criticized methods for determining the cutoff point of bankruptcy [HSIE93]. Some issues that were pointed out were the fact that the cutoff point is determined by trial and error not by statistics and that the cutoff point is determined without considering the relative loss for Type I and Type II errors. Hsieh derived a modified Bayesian decision model to estimate an optimal cutoff point for bankruptcy prediction models. A function was added in this model to account for the error costs of Type I and Type II errors and attempted to minimize these costs and not simply the probability of the error. However though Hsieh came up with this method for determining Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 10

22 the cutoff point, it has never actually been applied to previous bankruptcy models to determine its effectiveness vs. the common trial and error approach. In 2001, John Grice and Michael Dugan noted another drawback that models may not be as effective outside of the time period in which that model was created [GRIC01A]. That same year Tyler Shumway attempted to create a bankruptcy prediction method using a hazard model to account for changes over time [SHUM01]. He collected data for over 31 years and used the same ratios that Altman had used in his ZScore model. Shumway model, though it showed results better than Altman s in the first year before bankruptcy, had a significant decline in accuracy before the second year before bankruptcy. It can be seen that many bankruptcy models have used Altman s model as a benchmark for bankruptcy prediction. In 2001 another study was done by John Grice, along with Robert Ingram to look at the generalizability of the Altman Z score model [GRIC01B]. Grice looked at data between 1988 and 1991 and again showed that Altman s model was not as accurate during that time as it was on the time that it was developed. It was also shown that Altman s model was significantly more effective at predicting bankruptcy at a sample of specifically manufacturing firms than for a general dataset of companies. In 2004, a study was then done by Sudheer Chava and Robert Jarrow to look at the industry effects in bankruptcy prediction [CHAV04]. Data was collected from 1962 to 1999 and firms were taken from the AMEX, NYSE and NASDAQ listings. This study looked at both yearly and monthly intervals and showed that monthly intervals had the potential of being better predictors of failure if the data can be collected. A hazard model was run on the variables from Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 11

23 Altman s model [ALTM68], Zmijewski s model [ZMIJ84] and Shumway s model [SHUM01] and showed that industry groupings had a significant effect on the slope and intercept coefficients in these models. In 2004, Stephen Hillegeist, Elizabeth Keating, Donald Cram and Kyle Lundstedt attempted to use an options pricing model to look at the probability of bankruptcy [HILL04]. However again this model looked only at manufacturing firms to compare to Altman s Z score [ALTM68] and Ohlson s model [OHLS80] and it was suggested by the authors that the coefficients should be updated for industry adjustments. From the literature review it can be seen that industry is a factor in bankruptcy prediction. One of the most wellknown authors of bankruptcy prediction, Edward Altman also made another model based from his Altman Z score, specifically for nonmanufacturing firms [ALTM02]. This model is shown below. (1.4) Where: (1.4a) (1.4b) (1.4c) (1.4d) With cutoff zones: Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 12

24 As shown in studies such as the 2001 study by Grice [GRIC01B], Altman s original model is very much focused on manufacturing firms as are other models. Altman model for nonmanufacturing firms, called the attempts to look at an alternate industry set, but has not been studied much in subsequent papers and should be explored further. Data Envelopment Analysis in Bankruptcy prediction Comparing Altman Z score model to results from Data Envelopment Analysis (DEA) was done in 1997 by Simak and Paradi. The idea was to show that DEA would also be a valid model in the prediction of corporate failure [SIMA97]. Simak stated that when planning to invest in a company, most would like to know if the risk is acceptable for the return [SIMA97]. This is one of the main reasons why early warning indicators for corporate distress are widely researched. One of the more common methods is regression based multivariate ratio analysis, however it has many shortcomings associated with it so there are needs to explore other methods. Simak chose to use Data Envelopment Analysis (DEA) for several reasons. The advantages of DEA are outlined in the following chapter. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 13

25 In Simak s model, the focus was on the Z score model. He split this model into a DEA model by making all of the denominators into DEA inputs and all of the numerators into outputs. He had the following model: Figure 1: Simak Model 1 Simak made two revisions to this model. First he changed the market value of equity to the book value. This allowed for his DEA model to be applied to private companies whose shares did not have market values, and also that the book value was more readily available on the balance sheet of a company [SIMA97]. This gave the model shown in Figure 2. Figure 2: Simak Model 2 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 14

26 Simak also changed the EBIT, Earnings before Interest and Tax, with Operating Income [SIMA97]. The EBIT is defined as follows: Whereas the operating Income is defined as: The NonOperating Income is mostly miscellaneous income such as interests on notes receivable, capital gains/losses etc. Replacing EBIT with Operating Income, the model focuses more on the management s competence at running the normal operations of the firm. This model is shown in Figure 3. Figure 3: Simak's Model 3 Simak collected data from New Generation Research Inc. [NEWG11] for companies that applied for bankruptcy between 1991 and 199, which included 426 companies with assets between $1 million and $1 billion. From these a random sample of 43 companies was selected. These 43 companies were matched with nonbankrupt counterparts based on their SIC numbers. The companies had to have the first 3 digits of the SIC number match. Data was classified into January 1993, January 1994 and January 199, and each company s data was allocated to these groups based on the closest proximity to their fiscal year end. Three groups were created from Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 1

27 that dataset, one year before bankruptcy, two years before bankruptcy and 3+ years before bankruptcy. The Jan 1993 data was used to determine a cutoff point for the models and this was tested on the other years. Trial and Error determined 0.66 as a cutoff point and Simak found 7% accuracy at classifying bankrupt firms, 6% accuracy at classifying nonbankrupt firms and an overall accuracy of 63%. Simak commented on the low classification accuracy of nonbankrupt firms and that the walking wounded should be considered, i.e. companies that were near bankruptcy but still survived [SIMA97]. He also noted that narrowing down to more specific industries could likely increase the accuracy of the results. Simak used the BCC Inputoriented DEA model for all of his analyses. He noted that he tried some studies using the outputoriented BCC and obtained similar results, however he stated that perhaps using different DEA models for analysis could be of interest [SIMA97]. Another study was done in 2004 by Anja Cielen, Ludo Peeters, and Koen Vanhoof on bankruptcy prediction using DEA [CIEL04]. This study compared a linear programming approach, a customized data envelopment analysis model and a rule induction/decision tree model. This study looked specifically at companies in Belgium that declared bankruptcy between 1994 and 1996, inclusive. These authors selected 11 ratios that they had found from previous literature. These ratios were: Equity ratio Equity/total assets Retained earnings/total assets Retained earnings/total assets Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 16

28 Expired taxes Tax and social security charges/short term debt Cash ratio Cash/restricted current assets Inventories Work in progress, finished goods and contracts in progress/ current assets Financial debtratio Amounts payable within one year at credit institutions/short term debt Gross return Operating cash flow before taxes/total assets Coverage of debt Cash flow before dividends/total debts Net return Operating profit/loss after depreciation before financial charges and taxes/total assets Current ratio Current assets/short term debts Quick ratio Amounts receivable within one year + Investments + cash/ amounts payable within one year Leverage or debt ratio External liabilities/total assets Ratios that were found to have a positive correlation with bankruptcy were inputs to the model and ratios that with a positive correlation were made to be outputs to the model. This study also showed DEA to be a better predictor of bankruptcy than the LP model or the decision tree model. DEA has not been studied extensively as a predictor of bankruptcy but from the studies that have been done there seems to be potential for this analysis technique. Summary of Literature Review There is a rich literature on bankruptcy prediction using financial data and we included here only the most relevant to our work. Some have included many different models, from univariate analysis [BEAV67] [MERW42] to MDA [ALTM68][DEAK72], to DEA [SIMA97] [CIEL04] Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 17

29 as well as with other forms such as neural networks [BACK94], and hazard models [SHUM01], yet Altman seems to be referenced the most frequently [SIMA97][DEAK72][CIEL04][SHUM01]. Though Altman s original model has been modified to apply to different areas of industry, i.e. the Altman Z model [ALTM77] [ALTM02] this model has not been studied in much detail. Similarly with DEA, the few papers using this technique for bankruptcy prediction has shown it to be a promising method [SIMA97] [CIEL04] but not much has been done in this aspect particularly with respect to nonmanufacturing firms. This paper attempts to look at that niche of bankruptcy prediction for nonmanufacturing firms using data envelopment analysis. It also attempts to focus less on asset size, as do many other models [ALEX49] and attempt to incorporate the human capital [WEAT03]. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 18

30 Chapter 3: Data Envelopment Analysis The purpose of this section is to give brief overview of DEA and to provide a description of the main DEA models used as well as the model that will be used in this research. Data envelopment Analysis, DEA, is a fractional linearprogramming model used to measure the relative efficiency of different DecisionMaking Units (DMUs) [COOP78]. Efficiency in the most simplistic sense is just the measure of the output of a certain thing (DMU) relative to the input to that DMU. It is calculated with equation (3.1). (3.1) This calculation can be used to measure different DMUs against each other. These DMUs can be anything that has certain characteristics that can be used as inputs and outputs to determine the efficiency of said DMU. For example, we can look at the efficiency of a hospital, it might measure its efficiency by the number of patients it treats per number of doctors. The hospital is the DMU, the number of patients treated is the output and the number of doctors is the input. If we had data for a number of different hospitals we could compare them using theirs efficiency. For example: Table 1: DEA Example Hospital Doctors Patients Efficiency (Patients/Doctors) Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 19

31 Patients By comparing these institutions we can see that Hospital or DMU 3 is the most efficient. In DEA, this DMU would be the most efficient and apparently would have nothing to improve. The other DMUs would be compared to this one in terms of efficiency i.e. all other DMUs would want to reach an efficiency of 1 to be similar to DMU 3. If we plot this on a graph we can visualize this [COOP07] Doctors Figure 4: DEA Example The line passing through the point (3,3 and 0,0) corresponds to the efficiency of 1 which is what DMU 3 has. This line is known as the efficient frontier. All other DMUs are enveloped by this frontier. DEA states that all DMUs that fall on the efficiency frontier are said to be efficient whereas all others below it are inefficient. But DEA takes this a step further. Suppose you have more than one input or more than one output. You would have to come up with another way to measure efficiency. DEA does this by making a virtual input and virtual output, keeping the equation for efficiency similar [COOP07]. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 20

32 (3.2) We can take the virtual input as a sum of the inputs and the virtual output would be defined similarly. Going back to the hospital example, suppose another input was the number of nurses. The efficiency would then be: However there is a flaw in this. One could argue that a doctor would be a more important input than a nurse. Perhaps for this hospital a nurse can only treat 1 in every 4 patients that comes in, and a doctor must be present to treat the other 3. Thus the doctor should be treated as a more important input. We could say that the importance or weight of the doctor is three times that of the nurse. Therefore the efficiency becomes: The virtual input now depends upon the weighted sum of the inputs. This is the case in DEA. The virtual input is defined as a weighted sum of the inputs and likewise the virtual output is defined as a weighted sum of the inputs. (3.3) (3.4) Where v and u are the weights for the inputs and outputs respectively. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 21

33 It is not always straightforward to determine the weight of a particular input or output, especially when there are a large number of inputs/outputs. Linear programming is used to solve this. DEA has two main models using a fractional LP to calculate the frontiers, the CCR and the BCC model. Charnes Cooper Rhodes model The CCR model or Charnes Cooper Rhodes model, used a constant returnstoscale (CRS) approach [COOP07]. It is designed to find the optimal weights for each input and output for each DMU. If we take the equation for the efficiency that was shown above we can solve with the following formula. Subject to: ( ) (3.) We can change this into a linear programming model by simply splitting the efficiency ratio into the following: Subject to: ( ) (3.6) Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 22

34 Patients This would be an outputoriented model. This can also be done in another way, called an input oriented model [COOP07]. As shown in Equation (3.7). Subject to: ( ) (3.7) BankerCharnesCooper model The BCC or BankerCharnesCooper model uses a variable returnstoscale (VRS) approach [BANK84]. To visualize the difference between CRS and VRS let us go back to the hospital example. The efficient frontier outlined in Figure 4 would be a CRS efficient frontier. However for a VRS model, the efficient frontier would be as shown in Figure Doctors Figure : DEA BCC vs. CCR efficient frontier Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 23

35 Notice now there are three points on the efficient frontier rather than DMU3 only. Portions of the VRS frontier shows different returnstoscale behaviour, which is variable depending of the size of the DMU's scale, and allows for more efficient DMUs and also that the inefficient DMUS are now closer to the frontier and are thus more efficient. In CRS there is a constant returns to scale and the frontier is simply a straight line this means that for a unit of input the output production is always the same while the VRS frontier is piecewise linear and has different returns to scale depending on the DMU's scale. The BCC model also has output and input oriented models. The input model is shown below: Subject to: (3.8) Where is a scalar. and are the vectors of the inputs and outputs respectively for all DMUs, and and are the vectors for the DMU being optimized.. is a vector of the coefficients for the inputs and outputs with all elements nonnegative and is a row vector with all elements unity [COOP07]. The dual multiplier form is: Subject to: (3.9) Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 24

36 , free in sign Again and are the vectors of the inputs and outputs respectively, but here rather than, and are the vectors of variables for the inputs and outputs, similar to the example of virtual efficiency above. The output version is: Subject to: (3.10) The dual form is: Subject to: (3.11), free in sign SlacksBased Model Another model is the SlackBased Model, or SBM, which is an Additive DEA model. Additive DEA models, rather than establishing the radial projection onto the efficient frontier (input or output directions), it optimises the positive and negative slacks and with the projection is not radial that is, the suggestion for improvements show both input reductions and output Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 2

37 Output augmentations thus the nonradial projections. The radial approaches look at the changes that occur proportionally, i.e. a projection from an origin, whereas SBM looks at the nonradial projection, or inputs that change nonproportionally, i.e. looks at each point as its own origin. This is shown in Figure s s Input Figure 6: Slacks Based Model The is the negative slack and the is the positive slack. SBM uses the values of these slacks to calculate the efficiency of the units, unlike the BCC or CCR models which would use a ratio of the distance from the point to the efficient frontier with the distance to the origin. SBM models are units invariant but not translation invariant [COOP07]. The model for SBM is shown below. Input oriented: Subject to: Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms (3.12) 26

38 Output Oriented model: Subject to: (3.13) Advantages/Disadvantages of DEA DEA has several advantages over other analysis techniques, such as regression. DEA does not take an average it looks at an efficient frontier and attempts to project DMU performance onto that. It does not have fixed coefficients for the variables, i.e. it is nonparametric; it varies the coefficients of each DMU to maximize their efficiency. In this way it also focuses on individual inputs/outputs by analyzing the coefficients produced for each one. One of the biggest advantages of DEA is the ability to handle multiple inputs and outputs simultaneously, Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 27

39 unlike regression, and these inputs and outputs can have different units and still be analyzed together. A disadvantage of most DEA models is that they do not account for random error and assume that all deviations from the frontier are due to lack of efficiency of the DMU [TRAN07]. While there are stochastic and fuzzy DEA models, they are seldom used because the error distribution in the variables for the former and the fuzzy membership functions for the latter are hard to ascertain for real data sets. DEA has decreased accuracy with small sample sizes and must typically have a minimum number of DMUs. The rough rule of thumb is: { ( )} (3.14) Where n, m and s are the number of DMUs, inputs and outputs, respectively [COOP07]. Some DEA models cannot process negative numbers and allowances must be made for that. Also most DEA models, because they assign coefficients based on maximizing efficiency, can end up giving some coefficients zero or near zero values to make the DMU look the best it can be and often this is not acceptable, but there are techniques in DEA that can address this aspect satisfactorily. Lastly, DEA only looks at relative efficiency, comparing DMUs to other DMUs in their set, and does not look at the actual optimal efficiency. Some work has been done on this through the creation of a theoretical frontier [CHAR94]. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 28

40 Chapter 4: Model Development The section outlines the method used to develop the two DEA models used in this study, the first is based on Altman s Z model and the second is a revised model attempting to test the dependence on asset size and the relevance of human capital. The first phase of this model is to compare it with the most popular bankruptcy prediction model, the Altman Zscore. Edward Altman had three main Z score models; the original general Zscore, the Z model and the Z model, the latter of which is the model of interest here [ALTM78]. The Z score was designed for nonmanufacturing firms [ALTM02] and is defined as follows: (4.1) Where: (4.1a) (4.1b) (4.1c) (4.1d) With cutoff zones: Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 29

41 This Z score was used as the basis to create the first DEA model. The ratios were split and recombined into virtual inputs and outputs for the DEA model. All of the numerators were made to be outputs and the denominators were inputs in the model. The ratios were not inputted directly as it has been shown that ratios used as inputs or outputs in DEA can affect the accuracy of the model [SIGA09]. Due to data availability, EBIT was substituted for operating income. It has been shown that Operating Income is also a valuable indicator of corporate health in DEA [SIMA97]. Therefore the model consisted of: Outputs: Working Capital (WC) Retained Earnings (RE) EBIT or Operating Income (OI) Book Value of Equity (BVE) Inputs: Total Assets (TA) Total Liabilities (TL) And a virtual efficiency of: (4.2) Dealing with Negative Values The DEA model chosen was a SBM, or slacksbasedmodel, due to lack of previous work on corporate failure being done with this DEA model type. However, there is the issue with negative values. Many bankrupt companies will have negative values for their working capital, Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 30

42 retained earnings, operating income and even equity. However, negative values cannot be computed with this model. Not much research has been done to accommodate negative values in certain DEA models [KERS09], thus some sort of solution would have to be found to accommodate them. Hence the model was revised. Each output was split into a positive and negative measure. For example, Working capital (WC) was split into WC+ and WC, WC+ being the positive values for Working Capital and WC being the negative. The absolute value of the negative variable would be taken but it would then be placed in the model as an input rather than an output, i.e. WC would be an input, whereas WC+ would be an output. This method is essentially saying that Working Capital is an output, and therefore should be made as large as possible to optimize the efficiency. Working Capital is an inflow to the company. However a negative Working Capital can be view as an outflow from the company and therefore should be minimized. Thus it would be defined as an input, as the inputs to the model are minimized. Therefore we get a new visualization of the virtual efficiency. (4.3) Model Revision The model was then revised to accomplish one of its main purposes, to see how accurately bankruptcy can be measured regardless of assetsize. Hence the model was run again without total assets in the model. The total liabilities input variable was also removed and working capital was split into current assets and current liabilities. In an attempt to test the relevance of human capital, which is important to smaller, nonmanufacturing firms to the model, the number of employees and the number of shareholders was added to the model. Number of Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 31

43 employees was added to introduce the measure of individuals as a contributor to the efficiency of a company. The number of shareholders was added because for many smaller nonmanufacturing firms the shareholders have decisionmaking power and invest both time and money that contribute to the success of a firm. The number of shareholders investing in a business can also be seen as a reflection of the financial wellbeing of a company as viewed by the public. The revised model had the following virtual efficiency. (4.4) Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 32

44 Chapter : Data Acquisition One of the most critical components of this research is the collection of viable data. This was not an easy task as there are few databases for bankrupt companies. This section will discuss how the data collection was done, how companies were selected and which data was collected on each company. One of the first tasks was to find a list of bankrupt companies. This is difficult as once a company has reached failure it is usually removed from most exchange listings and other databases. However a list of bankrupt companies was found at New Generation Research Inc. [NEWG11], which was also used by Simak [SIMA97]. This site has filings for companies that date back to 1986 listed in alphabetical order of the company name. This list was narrowed down to those that could be classified as nonmanufacturing or servicebased firms. These companies must also have filed for bankruptcy between the years of 2000 and The reason for these dates was that more recent filings would be more easily obtained, and more easily compared to current companies. Bankruptcy filings from 2007 to present were not selected due to the economic recession taking place. During that time, significantly more bankruptcy filings took place than in previous years and it was considered that data from this time period could skew the results. The companies filing bankruptcy during that period would be more so for external reasons, which is not what is being studied in this project. This list consists primarily of companies filing for bankruptcy in the United States. No other extensive list of bankrupt companies was found and thus the focus of this work will be on the United States and Canada. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 33

45 Once the list from bankruptcydata.com was examined the names of companies were crossreferenced with the Mergent Online database [MERG08]. From that database the SIC, NAICS and incorporation date of the companies were extracted. The companies were also confirmed to be resident firms in the US or Canada and that they were publicly traded. Data was also collected on the exchange that the company was listed on before its bankruptcy. However, most of these companies were primarily traded overthecounter or through National Bulletin Board trading. For each of these bankrupt companies, financial data was compiled for up to years before the date of bankruptcy being filed, as it was shown that there is potential to predict bankruptcy up to years in advance [BEAV67] [MERW42]. Some companies did not have a full years of data and thus only had the number of years before bankruptcy collected. Whenever it was possible to identify them, the companies that had filed for bankruptcy but did not fail were excluded from the study. Many of these companies filed for bankruptcy for reasons other than complete insolvency, some liquidated due to legal issues, and others though they were suffering financial distress, filed in an attempt to reorganized and restructure their corporate strategy and alleviate some debt. These companies, similarly to the walking wounded described by Simak [SIMA97] are difficult to properly classify. Data from the full Balance Sheets, Income Statements, Cash Flow Statements and Retained Earnings were collected. From the Balance Sheet, current assets, total assets, current liabilities, total liabilities, retained earnings and shareholders equity values were extracted. From the Income Statement, the operating profit was calculated using the formula Net Sales Cost of goods Expenses. The number of employees and number of shareholders were also collected for the second model. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 34

46 Once the data was collected for the bankrupt companies, healthy companies, or nonbankrupt companies, were then found. A healthy company was chosen for every bankrupt company based on SIC number and on the years of health. Healthy companies had to be in existence at least years after the bankruptcy of their bankrupt counterpart. Healthy companies also must not have filed for bankruptcy during the time that they are being compared to the bankrupt counterpart. The same financial data was collected for the healthy company as the bankrupt counterpart within the same years. For example, if a bankrupt company filed bankruptcy in 2002, financial data was collected for The healthy company would have to have been in existence and not to have filed for bankruptcy between the years of 1996 to In some cases a suitable healthy match could not be found and thus the number of bankrupt companies exceeds the number of nonbankrupt ones. Data was organized by year before bankruptcy, up to years before bankruptcy. The companies collected was split into two groups, one which would be used to create the model and determine the cutoff point for the DEA model, and the second which would be used to test the model. The number of companies used in the Altman test and in the first model is the same, however in the second/revised model; some companies were omitted due to lack of data on the number of employees or shareholders. The numbers are shown Table 2. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 3

47 Table 2: Number of companies in group 1 Year before Bankruptcy Number of Bankrupt Companies Number of Healthy Companies The number of companies analyzed decreased each year due to the lack of financial data available. Table 3 shows the numbers for the second group, used to test the model. Table 3: Number of companies in group 2 Year before Bankruptcy Number of Bankrupt Companies Number of Nonbankrupt Companies Though the same companies were run through both the first and second models, the number of companies was not the same due to lack of data, particularly in the number of employees and shareholders. The total number of bankrupt companies are analyzed is shown in Table 4. Table 4: Number of companies in group 1 used in revised model Year before Bankruptcy Number of Bankrupt Companies Number of Healthy Companies Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 36

48 The companies used in this second data set were the same used in the second data set for the first model. However, again due to the lack of information, the number of companies varied. The number of companies from the second data set that were analyzed using the second model is shown in Table. Table : Number of companies in group 2 used in revised model Year before Bankruptcy Number of Bankrupt Companies Number of Nonbankrupt Companies Of both of the groups a total of 49 different SIC numbers were included, all from industries that were nonmanufacturing/ service based. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 37

49 Chapter 6: Results and Discussion This chapter shows the results of the data collected in the Altman Z model, the first DEA model based on the Altman Z model, as well as the revised DEA model. First a univariate analysis was done on each of the inputs and outputs. Univariate Analysis Over the five years before bankruptcy there were a number of inputs and outputs collected for each company, both bankrupt and healthy. First, these values were analyzed on their own. Tables 6 and 7 show the averages of each of these values over the five years, with measurements in thousands of dollars except shareholders and employees which are in number of shareholders and employees, respectively. Table 6: Profile analysis of group 1 bankrupt companies Bankrupt Year 1 Year 2 Year 3 Year 4 Year Current Assets 131, , ,68 113,96 119,184 Current Liabilities 206, ,072 9,76 69,104 72,676 Working Capital 74,238 23,378 36,109 44,861 46,08 Retained Earnings 83,01 19,862 3,184 1,649 9,676 Operating Income 38,36 72,711 4,7 17,319 19,48 Book Value of Equity 37,317 94,934 9,18 64,370 47,830 Total Assets 360,30 414, ,76 271, ,06 Total Liabilities 324, , , ,941 22,207 Employees 17,740 18,263 18,823 19,60 23,938 Shareholders 1,272 1,12 1,334 1,444 1,89 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 38

50 Table 7: Profile Analysis of group 1 nonbankrupt companies Nonbankrupt Year 1 Year 2 Year 3 Year 4 Year Current Assets 387,839 33,12 274,79 24, ,683 Current Liabilities 260, , ,219 17, ,942 Working Capital 127, ,862 93,360 97,447 93,741 Retained Earnings 339, ,14 198, ,0 122,330 Operating Income 1,76 139, ,228 84,331 70,180 Book Value of Equity 31,714 49,193 36, , ,823 Total Assets 1,030, , ,34 614,072 30,739 Total Liabilities 498, , , , ,428 Employees 11,777 11,193 9,42 8,29 7,707 Shareholders 8,793 8,89 6,829 6,222,426 If we plot these over the five years we can compare the averages of bankrupt and nonbankrupt companies. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 39

51 Figure 7: Profile Analysis of Variables in first DEA model In Figure 8 we plot the comparisons for the other inputs and outputs for the second model. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 40

52 Figure 8: Profile Analysis of remaining variables for revised DEA model It can be seen that nonbankrupt companies have higher averages than the bankrupt ones for all variables, except in this case the number of employees. The bankrupt companies have a higher number of employees for all years before bankruptcy, which is something to note. This observation affirms the decision to put the number of employees as an input variable for the DEA model, i.e. that it should be minimized to increase efficiency. Second dataset The Averages were also compared for the second set of data. Summarized in Tables 8 and 9: Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 41

53 Table 8: Profile Analysis of bankrupt companies in group 2 Bankrupt Year 1 Year 2 Year 3 Year 4 Year Current Assets 92, ,122 97,307 83,433 84,33 Current Liabilities 133,827 77,99 64,410 1,610 0,09 Working Capital 41,004 33,163 32,897 31,822 33,826 Retained Earnings 1,149 11,260 8,186 3,092 8,104 Operating Income 3,607 4,124 3,728 4,686,031 Book Value of Equity 3,771 2,19 3,93 3,426,929 Total Assets 19, ,48 191,003 12, ,362 Total Liabilities 173, , ,0 100, ,429 Employees 1,18 1,40 1,842 2,12 1,760 Shareholders 846 1,276 1,213 1,468 1,62 Table 9: Profile Analysis of Nonbankrupt companies in group 2 Nonbankrupt Year 1 Year 2 Year 3 Year 4 Year Current Assets 431, , , , ,141 Current Liabilities 30, ,711 2, ,62 217,643 Working Capital 126,37 110,91 94,69 98, ,498 Retained Earnings 220, ,13 198,78 340, ,824 Operating Income 63,67 0,148 32,082 62,309 64,823 Book Value of Equity 440,22 437, ,4 419,02 08,77 Total Assets 1,094,197 1,070, , , ,132 Total Liabilities 64, ,864 44, , ,781 Employees 13,798 13,712 13,182 13,14 10,18 Shareholders 3,474 4,27 3,438 3,2 3,688 The trends were very similar to those of the first group, except that for this group the nonbankrupt companies also have higher averages of employees. A simple ttest was done, the results of which are shown in appendix G. For the first group, the ttest concluded that all variables showed a difference in the means of the bankrupt and nonbankrupt companies with a significance of 0.0. However for the second group, the ttest showed that the bankrupt and nonbankrupt companies were different to a significance of 0.01 except for the means of the Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 42

54 number of employees. The discrepancy in results shows a bit of variability in the accuracy of the number of employees as a predictor of corporate wellbeing. Altman Z results The data collected was then processed through the Altman model to test the accuracy of his Z model. The Altman Z scores for each company can be found in Appendix D. When each of the companies was processed through this model the classification results were as follows. Table 10: Results of Altman Z'' Model on group 1 Year Type I error 18.2% 27.3% 37.2% 3.7% 41.% Type II error 4.2% 48.4% 38.7% 41.4% 42.9% Bankrupt accuracy 77.3% 9.1% 44.2% 47.6% 1.2% Nonbankrupt accuracy 41.9% 3.% 48.4% 44.8% 46.4% Total accuracy 62.7% 49.3% 4.9% 46.% 49.3% Bankrupt accuracy including grey area 81.8% 72.7% 62.8% 64.3% 8.% Nonbankrupt accuracy including grey area 4.8% 1.6% 61.3% 8.6% 7.1% Total accuracy including grey area 70.7% 64.0% 62.2% 61.9% 7.9% Total bankruptcy 64.0% 4.7% 41.9% 4.1% 47.8% Total nonbankrupt 28.0% 30.7% 41.9% 39.4% 43.% Total within grey area 8.0% 14.7% 16.2% 1.% 8.7% Type I error is a bankrupt company classified as nonbankrupt. The type II error is a nonbankrupt company being classified as bankrupt. Figure 9 shows the trend of the errors over the years before bankruptcy. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 43

55 60% Percent Error 0% 40% 30% 20% Type I error Type II error 10% 0% Year before bankruptcy Figure 9: Percent error of Altman Z'' Score The first year before bankruptcy, the Altman Z score does a relatively good job of classifying bankrupt companies, it has a type I error of 18%. As expected, the percent error increases as the time before bankruptcy increases, where the error more than doubles to 38%. The opposite effect is true for the nonbankrupt companies. The type II error is as high as 4% in the year before bankruptcy and decreases as the time before bankruptcy increases to a percent error of 38% five years before bankruptcy. The Type II error stays relatively constant compared to that of the Type I error, due to the fact that the bankrupt companies have more defined indicators closer to the bankruptcy making it easier to predict bankruptcy and less bankrupt companies being classified falsely. However for nonbankrupt companies they are not approaching bankruptcy and thus their statements are similar regardless of year, therefore the nonbankrupt companies that are doing poorly, i.e. the walking wounded are constantly classified as bankrupt regardless of year. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 44

56 The accuracy is plotted in Figure % 80.00% 70.00% 60.00% 0.00% 40.00% 30.00% 20.00% 10.00% 0.00% Percent classification accuracy Year before bankruptcy Nonbankrupt Overall Accuracy Bankrupt Figure 10: Classification accuracy of Altman Z'' Model Here in Figure 10, the nonbankrupt companies have no clear trend and the classification accuracy fluctuates between 3% and 48% over the range of the years before bankruptcy. The bankrupt companies however have a clear trend, the classification accuracy decreasing the further from the year of bankruptcy, with a high classification accuracy of 77% in the year before bankruptcy but declining drastically to 43% in just the third year before bankruptcy. The overall accuracy also shows a downward trend of 62% in the year before bankruptcy and 4% five years prior to bankruptcy. These results do not currently include the grey area of the model. This grey or undetermined area is where Altman classified companies as unsure whether they were in danger of bankruptcy or not. Figure 11 shows the accuracy, including the grey companies in the classification. I.e. Bankrupt companies classified in the grey area are deemed bankrupt and nonbankrupt companies classified in the grey area are deemed nonbankrupt. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 4

57 90.00% 80.00% 70.00% 60.00% 0.00% 40.00% 30.00% 20.00% 10.00% 0.00% Percent accuracy including grey zone Year before bankruptcy Bankrupt Nonbankrupt Overall Figure 11: Classification accuracy of Altman Z'' model including grey area If the grey area is included, the accuracy increases, with a bankruptcy accuracy of 81%, a nonbankrupt accuracy of 4%, and overall accuracy of 70% in the first year before bankruptcy. However, the bankrupt and nonbankrupt accuracy converge to approx. 61% at year before bankruptcy, with the overall and bankruptcy accuracies decreasing and the nonbankrupt increasing as seen before. The total number of companies classified is shown in Figure 12, regardless of the accuracy of the classification. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 46

58 70.00% Total classification amounts 60.00% 0.00% 40.00% 30.00% 20.00% Bankrupt Nonbankrupt Grey area 10.00% 0.00% Year before bankruptcy Figure 12: Total classification within each zone of Altman Z'' model Similar trends are seen for bankrupt and nonbankrupt, though it could be noted the large difference in the percentage for bankrupt and nonbankrupt in the year before bankruptcy, with a percent bankrupt classification of 64% and a nonbankrupt of 28%. For the grey area, the classification actually seems to decrease the closer the date is to the bankruptcy date with the exception of year five, with fewer classifications as unsure as the bankruptcy approaches. It is not clear why this trend occurs, though it could be suggested that as the date of bankruptcy approaches the indicators between the bankrupt and nonbankrupt companies are more distinct and thus more companies are classified as either bankrupt or nonbankrupt with less falling into the ambiguous area. Second group As mentioned previously, there were two data sets analyzed. The first was to be used to create the DEA model cutoff and the second to test those established cutoff points. The data above was for the first data set. Table 11 summarises the results for the second data set. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 47

59 Table 11: Results of Altman Z'' model on group 2 Year Type I error 13.3% 18.2% 34.1% 41.% 40.% Type II error 42.% 30.0% 27.% 27.% 2.0% Bankrupt accuracy 77.8% 9.1% 0.0% 41.% 3.1% Nonbankrupt accuracy 47.% 2.%.0% 2.% 63.9% Total accuracy 63.%.9% 2.4% 46.9% 49.3% Bankrupt accuracy including grey area 88.9% 86.4% 70.% 70.7% 83.8% Nonbankrupt accuracy including grey area 60.0% 72.% 7.0% 7.0% 88.9% Total accuracy including grey area 72.9% 69.1% 9.% 60.% 67.1% Total bankruptcy 61.2% 4.2% 39.3% 34.6% 30.1% Total nonbankrupt 29.4% 34.% 44.1% 46.9% 2.1% Total within grey area 11.8% 23.8% 20.2% 2.9% 36.9% DEA Model Each year was then analyzed with Data Envelopment Analysis, SlackBasedMeasure model. The model was input oriented, designed to minimize inputs. Through this analysis a score was given to each of the companies. This is listed in Appendix E. Once each company was given a score, a measure of bankruptcy had to be determined. For each year every possible cutoff point was tested at intervals of 0.0 from 0 to 1 to determine the classification accuracy of both bankrupt and nonbankrupt firms at each of those potential cutoff points. Figure 13 shows the percentages vs. the cutoffs. For example for a cutoff point of zero, no bankrupt companies are classified as bankrupt and all nonbankrupt companies would be classified as nonbankrupt. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 48

60 Percentage 120 Year NonBankrupt 1 Bankrupt Cutoff Point Figure 13: Cutoff point determination on year 1 results Figure 13 shows the cutoff for year 1. We want to have the highest percentage of both bankrupt and nonbankrupt companies being classified accurately. The dilemma is that as the cutoff point changes the accuracy of one increases while the other decreases, as seen in Figure 13. If only one cutoff point was chosen, then the best place would be where the graphs intersected, giving the highest possible accuracy for both simultaneously. Here that point would be approx. 0.. Because we are choosing not one but two cut off points, an upper, above which would be the nonbankrupt zone, and a lower, below which would be the bankrupt zone, we need to focus on the points just above and below the 0.. We zoom in around that point in Figure 14. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 49

61 Bankrupt Nonbankrupt Overall Accuracy Figure 14: Closer look at cutoff point for year 1 Here we also plot the overall accruracy of the Cutoff points. As we can see we get a high point in the overall accuracy at a cutoff point of 0., with 64% overall accuracy. Here the bankrupt companies have a classification accuracy of 61.36% and the nonbankrupt companies have a classification accuracy of 67.74%. For one year before bankruptcy these would be the chosen bottom cutoff. There is another maximum point at a cutoff of 0.6. Here the overall accuracy is 6.33%. The classification accuracy for bankrupt companies at this point is 72.73%, whereas for nonbankrupt companies the classification accuracy is 4.8% This would be the top cutoff point. However, this is only for one year before bankruptcy. If we look at the following years the graph changes quite a bit. Figure 1 shows the second year before bankruptcy. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 0

62 Percentage 120 Year NonBankrupt 2 Bankrupt Cutoff Point Figure 1: Cutoff point on year 2 It can be seen that the crossover point moves up to 0.6 in the second year which is quite different from the first. Because this model attempts to predict bankruptcy for up to years before the bankruptcy date, cutoff points for all years up to years before bankruptcy are examined in Figure 16. Here Bankrupt 1 is the plot of bankruptcy classification for 1 year before bankruptcy, and Nonbankrupt 1 is the nonbankrupt classification for 1 year before bankruptcy. Bankrupt 2 is the bankrupt classification for 2 years before bankruptcy, etc. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 1

63 Percentage 120 Cut off points for up to years before bankruptcy NonBankrupt Bankrupt NonBankrupt 1 Bankrupt 1 NonBankrupt 2 Bankrupt 2 NonBankrupt 3 Bankrupt 3 NonBankrupt 4 Bankrupt Cutoff Point Figure 16: Cut off point for up to years before bankruptcy Figure 16 clearly shows that for one year before bankruptcy the numbers are significantly lower with the numbers trending up as would be expected. In Figure 17 we compare the averages of the DEA scores for the years. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 2

64 DEA score 0.8 Average DEA scores Bankrupt Nonbankrupt Year before bankruptcy Figure 17: Average DEA scores for group 1 first DEA model The DEA scores for the nonbankrupt companies are higher than that of the bankrupt companies for all years, but with a much more significant difference in the 1 st year before bankruptcy and with that difference decreasing as the time before bankruptcy increases. At one year before bankruptcy the average bankrupt score is 0.446, using that and the fact that we have a maximum efficiency in bankruptcy classification at 0., the bottom cutoff is chosen to be 0.. We can see that for one year before bankruptcy the average for the nonbankrupt companies is we can choose the top cutoff point at Because we are more concerned about the accuraccy of classification for bankrupt companies than nonbankrupt we will shift these points up. By comparing the values over the years as well as the averages, the finalized cutoff points are as follows: Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 3

65 Table 12: Cutoff points for first DEA model θ>0.7 0.<θ<0.7 θ<0. Nonbankrupt Grey area Bankrupt This corresponds to the following clasiffication accuraccies: Table 13: Classification accuracies of group 1 used to determine cutoff Year Type I error 18.2% 29.6% 37.2% 40.% 36.6% Type II error 38.7% 32.3% 29.0% 24.1% 32.1% Bankrupt accuracy 63.6% 4.% 39.% 3.7% 34.2% Nonbankrupt accuracy 4.2% 4.2% 4.2% 1.7% 0.0% Total accuracy 6.0% 4.3% 41.9% 42.3% 40.6% Bankrupt accuracy including grey area 81.8% 70.% 62.8% 9.% 63.4% Nonbankrupt accuracy including grey area 61.3% 67.7% 70.9% 7.9% 67.9% Total accuracy including grey area 73.3% 69.3% 66.2% 66.2% 6.2% Total bankruptcy 3.3% 40.0% 3.1% 30.9% 33.3% Total nonbankrupt 29.3% 36.0% 40.% 4.1% 42.0% Total within grey area 17.3% 24.0% 24.3% 23.9% 24.6% These cutoff points are then tested on a second set of data that had not been influenced by the creation of the cutoff. This second set of data had the same inputs and outputs from the same genre of companies as the first group, with the same cutoff points determined by the first set, <0. is bankrupt, >0.7 is nonbankrupt and in between is a grey area, the see Table 14. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 4

66 Table 14: Classification accuracies of cutoff point for model 1 tested on group 2 Year Type I error 13.0% 28.9% 28.3% 30.2% 39.% Type II error 21.9% 14.6% 19.% 20.0% 29.7% Bankrupt accuracy 71.7% 1.1% 4.7% 41.9% 36.8% Nonbankrupt accuracy 6.9% 68.3% 6.9% 60.0% 62.2% Total accuracy 68.9% 9.3%.2% 0.6% 49.3% Bankrupt accuracy including grey area 86.9% 71.1% 71.7% 69.8% 60.% Nonbankrupt accuracy including grey 78.1% 8.4% 80.% 80.0% 70.3% area Total accuracy including grey area 82.8% 77.9% 7.9% 74.7% 6.3% Total bankruptcy 48.3% 33.7% 33.3% 31.3% 33.3% Total nonbankrupt 37.9% 47.7% 4.9% 44.6% 0.7% Total within grey area 13.8% 18.6% 20.7% 24.1% 16.0% Revised DEA Model The original DEA model was a comparison with the Atman Z score. It took the ratios that Altman used, split them into their numerators and denominators and made them outputs and inputs respectively in the DEA model. A second model was created and analyzed from the same data set. This used a similar set of inputs and outputs but was modified to not include total assets/liabilities and also to incorporate human capital by incorporating number of shareholders and number of employees. The DEA scores for this model can be found in Appendix F A similar process was done to determine the cutoff point for this model. The cutoff point determination shown summarized in Figure 18. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms

67 Percentage 120 Cut off points for up to years before bankruptcy second model Cutoff Point NonBankrupt Bankrupt NonBankrupt 1 Bankrupt 1 NonBankrupt 2 Bankrupt 2 NonBankrupt 3 Bankrupt 3 NonBankrupt 4 Bankrupt 4 Figure 18: Cutoff points for up to years before bankruptcy for revised model The intersection points for each year were considered. These are shown in table 1. Table 1: Intersection points for each year of model 2 Year before Bankruptcy Intersection Point The average scores for the years are shown in Figure 19. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 6

68 DEA Score Average DEA Scores revised model Year before bankruptcy Bankrupt Nonbankrupt Figure 19: Average DEA scores for group 1 in model 2 The cutoff points determined were the following: Table 16: Cutoff points for revised DEA model θ>0.8 Nonbankrupt 0.6<θ<0.8 Grey area θ<0.6 Bankrupt These cutoff points corresponded to the classification accuracies shown in Table 17. Table 17: Classification accuracies used to determine cutoff points for revised model Year Type I error 20.0% 29.4% 41.9% 31.3% 42.3% Type II error 41.4% 3.7% 38.% 2.0% 26.1% Bankrupt accuracy 7.0%.9% 4.2% 43.8% 46.2% Nonbankrupt accuracy 48.3% 46.4% 3.9% 4.2% 73.9% Total accuracy 63.8% 1.6% 49.1% 48.2% 9.2% Bankrupt accuracy including grey area 80.0% 70.6% 8.1% 68.8% 7.7% Nonbankrupt accuracy including grey area 8.6% 64.3% 61.% 7.0% 73.9% Total accuracy including grey area 71.0% 67.7% 9.7% 71.4% 6.3% Total bankruptcy 60.9% 46.8% 42.1% 3.7% 36.7% Total nonbankrupt 31.9% 37.1% 47.4% 41.1% 7.1% Total within grey area 7.3% 16.1% 10.% 23.2% 6.1% Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 7

69 As with the first model, these cutoff points were then tested on the second data set. Using the cutoff points above on this data, the results are shown in Table 18. Table 18: Classification accuracies of cutoff points for revised model on group 2 Year Type I error 14.3% 31.6% 30.8% 21.9% 42.3% Type II error 22.9% 11.8% 11.8% 6.7% 18.% Bankrupt accuracy 78.6% 7.9% 46.2% 3.1% 38.% Nonbankrupt accuracy 62.9% 61.8% 73.% 66.7% 70.4% Total accuracy 71.4% 9.7% 8.9% 9.7% 4.7% Bankrupt accuracy including grey area 8.7% 68.4% 69.2% 78.1% 7.7% Nonbankrupt accuracy including grey area 77.1% 88.2% 88.2% 93.3% 81.% Total accuracy including grey area 81.8% 77.8% 78.1% 8.% 69.8% Total bankruptcy 3.3% 36.1% 30.1% 30.7% 28.3% Total nonbankrupt 36.4% 4.8% 0.7% 43.6% 6.6% Total within grey area 10.4% 18.1% 19.2% 2.8% 1.1% Comparison of models Here the results of the Altman Z with the two DEA models are compared. The results are all for the companies in group 2. Figure 20 shows the classification accuracies of each company. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 8

70 Percentage 80.00% 70.00% 60.00% 0.00% 40.00% 30.00% 20.00% 10.00% 0.00% Overall Accuracy Year before bankruptcy Altman First DEA Model Second DEA Model Figure 20: Comparison of overall accuracies Not surprisingly, it can be seen that there is a general trend with all of the models such that the accuracy decreases as the time before bankruptcy increases. The second DEA model actually has a slightly higher overall classification accuracy than the first DEA model, which is slightly better than the Altman Z score. This accuracy can be split into bankrupt and nonbankrupt accuracy, as shown in Figure 21. Figure 21: Comparison of bankrupt and nonbankrupt classification accuracies Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 9

71 For bankruptcy, the trend is similar to the overall accuracy, though with bankrupt classification the Altman Z model fares better against the other models. With nonbankrupt accuracy there is no clear trend over time, however it can be seen that DEA has higher classification accuracy than the Altman Z score in this regard. Figure 22 shows the type I and type II errors. Figure 22: Comparison of type I and type II error Type I error has an increasing trend, with the exception of an outlier on the second DEA model in the fourth year before bankruptcy. Other than that outlier, the second model appears to have a higher type I error than the first DEA model. This mirrors the trend in bankruptcy classification accuracy, though the Altman Z model has lower error in the first 2 years before bankruptcy and after which the DEA model shows less error. Similarly the type II error mirrors the trend in the nonbankrupt classification accuracy, with no defined trend over time, however with the DEA model having much lower error than the Altman model. Something to also consider is the total percentage of companies classified as bankrupt, nonbankrupt and in the grey area, shown in in Figure 23. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 60

72 Figure 23: Comparison of percent classifications Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 61

73 Altman classifies more companies as bankrupt than the DEA models. However for nonbankrupt classification the DEA scores classify more as nonbankrupt in the first 3 years and then in the fourth and fifth years before bankruptcy the three models have similar nonbankrupt classifications. The Altman model seems to have more companies classified in the grey area as the time before bankruptcy increases. The DEA models have an increasing trend until the th year before bankruptcy where it dips again. Comments on DEA scores Upon a further analysis of the DEA scores, which can be found in Appendices E and F, it could be noted that a particular number of firms were classified as being efficient, i.e. having a score of 1. These companies were both bankrupt and nonbankrupt companies. Some of the nonbankrupt companies were the following: o Home Depot o American Consumers o Eat at Joes o Family Room Entertainment o Children s Place o Arden Group o ELXSI o Rocky Brands This is understandable for the nonbankrupt companies. In this study, companies that do not end up bankrupt are efficient. However, there were a number of bankrupt companies constantly classified as efficient. Those were as follows: o Image Innovations Holdings o CD Warehouse o etoys o Letchers Inc. o Quokka Sports o WHSU Inc. (aka Micro Warehouse Inc.') Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 62

74 It is difficult to say why these were classified as efficient even though they went bankrupt. The values that they have for the input and output variables are comparable to those of nonbankrupt companies. Upon further investigation, companies like etoys and CD Warehouse were actually repurchased by other companies upon bankruptcy. In these instances the company could still have positive financial statements, but simply dissolved the company as a business strategy. Some other companies, such as Image Innovations Holdings, filed for bankruptcy but still exist today and could have similarly had alternate intentions for filing bankruptcy, such as a Chapter 11 reorganization. In future studies these companies should be accounted for in the model or excluded from the data set. Other companies however, such as Quokka Sports, were reported to be doing poorly but still have a classification of efficient. This shows that there is still error in the DEA model that must be further investigated. Also a few nonbankrupt companies were constantly classified as bankrupt. Some of which are: o AMC Entertainment o Carrols Corp o ACG Holdings o Jennifer Convertibles o Amazon.com o All American SportPark Inc. o TIX Corp o United Artists Theatre Circuit These would be what Simak referred to as the walking wounded. Though the second DEA model did classify more companies as nonbankrupt, it still did not sufficiently take into account those that performed poorly but still survived. More tests need to be done to look into this. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 63

75 Chapter 7: Conclusion and Recommendations This section presents the conclusions and suggests potential areas for future research. Conclusions In conclusion, the DEA models more accurately predicted bankruptcy when compared to the Altman Z model. The overall accuracy of the original DEA model was higher than that of the Altman Z and the revised DEA model, slightly more than that of the original. The higher accuracy of the revised model showed that the total assets or liabilities of a company are not necessary in determining bankruptcy. This is important for companies like nonmanufacturing/retail companies which may not have a large investment in hard assets. The Altman Z model did a fair job in classifying bankrupt companies and the results of the DEA models were essentially the same in terms of bankrupt accuracy. However, DEA did a better job in classifying the nonbankrupt companies correctly, which factored into the overall higher accuracy. The classification errors varied. For type I error, i.e. bankrupt companies classified as nonbankrupt, Altman had lower errors in the first 2 years, then DEA had lower errors in the third and fourth year and in the fifth year the overall errors for all three models were high. For type II error, i.e. nonbankrupt companies classified as bankrupt, the Altman model definitely had a higher error than DEA. The Altman Z model would classify more companies as bankrupt than the DEA models in all years and the DEA models classified more companies as nonbankrupt in the first 3 years. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 64

76 Comparison of the average values of the inputs and outputs was also done. These input and output variables were: Current Assets Current Liabilities Working Capital Retained Earnings Operating Income Book Value of Equity Total Assets Total Liabilities Number of Employees Number of Shareholders Comparison of means showed that for all variables, nonbankrupt companies had higher means than bankrupt ones, except in the case of the number of employees, where the bankrupt companies had a higher average. Overall, this research showed that a SBM DEA model can be used for predicting bankruptcy or financial distress, that total asset size is not necessary for bankruptcy prediction in services oriented firms and also showed that the method for dealing with negative values, by splitting them into positive and negative values could be a viable option when needed in DEA analyses. This research has many useful conclusions but it also has some areas that should be examined further. Recommendations for Future Work There are many areas that could be improved on in this model. The following is a list of possible recommendations for future work. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 6

77 This work looked at an inputoriented SlacksBasedModel. Alternate DEA models could be studied to attempt to gain further accuracy. In particular, the possibility of using the Assurance Region model to restrict the weights and if possible, reconfiguring DEA to allow weight restrictions between inputs and outputs to account for the positive and negative values that had to be split in this model. Creating a different model with new inputs and outputs perhaps from sources other than financial statements could be investigated. The use of employees and shareholders, two nonmonetary variables, were an attempt to do this and their impact could be explored in further detail. Financial statements only look into one aspect of a company s health and other inputs could possibly make the model more accurate. A different method of determining the cutoff point could be explored. The trial and error approach is a simple and intuitive way, however a more statistically sound method could be developed. Decision trees were a discussed method and could be looked into for future research. Other analytical methods could be explored, rather than or in conjunction with DEA. Altman used MDA, however there could be more studies done with other models, such as logistic regression. Looking at a more homogeneous subset of companies could be helpful. DEA looks at classifying DMUs which are similar in culture, the more similar they are, the more accurately the culture can be defined. This study looked broadly at nonmanufacturing/retail companies. This could be more specific; however the problem could be finding a large enough sample size of data. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 66

78 The walking wounded was not sufficiently addressed as well as those companies that are doing relatively well but file for bankruptcy. In this case, more than simply the fact that a company has filed bankruptcy should be looked at when classifying a company as failed. The curiosity of the bankrupt companies having a larger number of employees than nonbankrupt firms should be explored in the context of the human capital being a significant factor in these firms. Future models could do more to look into the efficiency of a company based on the number of employees and the efficiency of the employees. Incorporating cash flows into a future model should be considered, as a few models have used this in the past and has shown that it could possibly be a good indicator of bankruptcy prediction [AZIZ88]. Testing the model on companies in a different corporate environment, different countries, such as the UK and Europe could be considered. Applying this lack of assets approach to a different analysis type, such as neural networks [BACK94] could also be used to test the legitimacy of this approach. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 67

79 References [ALEX49] [ALTM02] [ALTM06] [ALTM68] [ALTM68] [ALTM77] [ALTM93] [AZIZ88] [BACK94] [BACK9] [BALL82] [BANK78] [BEAV67] Sidney Alexander, "The Effect of Size of Manufacturing Corporation on the Distribution of the Rate of Return," Review of Economics and Statistics, August, 1949, pp Altman, Edward. Bankruptcy, Credit Risk and High Yield Junk Bonds. Blackwell Publishers Inc. Malden, Massachusetts Altman, Edward. Hotchkiss, Edith. Corporate Financial Distress and Bankruptcy. Third Edition. John Wiley and Sons Inc Hoboken, New Jersey Altman, Edward. Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy, Journal of Finance (September 1968) pp AItman, E., "Financial Ratios, Discriminant Analysis and the prediction of Corporate Bankruptcy". Journal of Finance. Sept pp Altman, E.. Haldeman, R., Narayan, P.. ZETA Analysis A New Mode1 to Identify Bankruptcy Risk of Corporations". Journal of Banking and Finance. June 1977, pp Altman. Edward. "Corporate Financial Distress and Bankruptcy", Second Edition, John Wiley & Sons Aziz, A., Emanuel, D.C., Lawson, G.H., Bankruptcy Prediction An Investigation of Cash Flow Based Models, Journal of Management Studies 2:, Sept 1988, pp Back, B., Oosterom, G., Sere, K., and van Wezel, M. (1994), A Comparative Study of Neural Networks in Bankruptcy Prediction, Proceedings of the 10th Conference on Artificial Intelligence Research in Finland, Turku, Finland, p Back, Barbro., Sere, Kaisa., van Wezel, Michiel C. Choosing the Best Set of Bankruptcy Predictors, proceedings of the 1NWGA, Vaasa, Finland, Jan 199, pp Ball R., Foster G. Corporate Financial Reporting: A Methodical View of Empirical Research Journal of Accounting Research, 20 (supp.), 1982, pp Banker, R.D., Charnes, A. and Cooper, W.W. Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis Management Science vol. 30. pp (1978) Beaver, William H. Financial Ratios as Predictor of Failure, Empirical Research in Accounting: Selected Studies, 1967, supplement to Vol., Journal of Accounting Research pp Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 68

80 [CASE80] [CASE84] [CASE8] [CHAR78] [CHAR94] [CHAV04] [CHUV03] [CIEL04] [COOP07] [CYBI03] [DEAK72] [GRIC01A] [GRIC01B] [HILL04] [HSIE93] Casey, C.M. Jr., The Usefulness of Accounting Ratios for Subjects renditions of Corporate Failure: Replication and Extension, Journal of Accounting Research, Autumn pp Casey, C. Bartczak, N., Cash Flow Its Not the Bottom Line. Harvard Business Review, 62, July/Aug. 1984, pp Casey, C. Bartczak, N., Using Operating Cash Flow Data to Predict Financial Distress: Some Extensions, Journal of Accounting Research, Spring 198, pp Charnes, A., W. Cooper, and E. Rhodes (1978): Measuring the Efficiency of Decision Making Units," European Journal of Operational Research, 2(6), Charnes,A., Cooper, W. W.. and Seiford.L.M., "Basic DEA Model", in Data Envelopment Analysis: Theory, Methodology and Application, Charnes et al. Editors, Kluwer Academic Publishers, 1994 Chava, S., Jarrow, R., "Bankruptcy Prediction with Industry Effects". Review of Finance 8: 37 69, Kluwer Academic Publishers. Chuvakhin, Nikolai., Gertmenian, L. Wayne., Bankruptcy Prediction in the WorldCom Age. Graziadio Business Review. Vol. 6 Issue Cielen, A., Peeters, L., Vanhoof K., "Bankruptcy prediction using a data envelopment analysis." European Journal of Operational Research 14 (2004) Cooper, W. W., Seiford, L.M. and Tone, K. Data Envelopment Analysis: A comprehensive Text with Models, Applications, References and DEASolver Software, 2nd Edition. Springer Science+Business Media, LLC Cybinski, P.J. Doomed Firms: An Econometric Analysis of the Path to Failure. Ashgate Publishing Limited. Burlington, VT ISBN Deakin, E.B., A Discriminant Analysis of predictors of Business Failure, Journal of Accounting Research, Spring 1972, pp Grice, J., Dugan, M "The Limitations of Bankruptcy Prediction Models: Some Cautions for the Researcher." Review of Quantitative Finance and Accounting, 17: , Kluwer Academic Publishers Grice, J., Ingram R., "Tests of the generalizability of Altman s bankruptcy prediction model." Journal of Business Research 4 (2001) 3 61 Hillegeist, Stephen A., Keating, Elizabeth., Cram, Donald P., Lunstedt, Kyle G Assessing the probability of bankruptcy. Review of Accounting Studies 9, 34 Hsieh, SuJane "A Note on the Optimal Cutoff Point in Bankruptcy Prediction Models." Journal of Business, Finance and Accounting 20(3), X. Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 69

81 [KERS09] [LIBB7] Kristiaan Kerstens*, Ignace Van de Woestyne** Negative Data in DEA: A Simple Proportional Distance Function Approach. *IÉSEG School of Management, CNRS LEM (UMR 8179) **Hogeschool Universiteit Brussel, Belgium April 2009 Libby, R. Accounting Ratios and the Prediction of Failure, Journal of Accounting Research, 197. [MERG08] Mergent Online Date Visited: September 2 th, 2011.Copyright 2008 by Mergent, Inc. [MERW42] [NEWG11] [OHLS80] [PATE02] [SHUM01] [SIGA09] [SIMA97] [TAFF82] [TRAN07] [WEAT72] [WORL09] Charles L. Merwin, "Financing Small Corporations in Five Manufacturing Industries ", National Bureau of Economic Research, New Generation Research Experts in Bankruptcy Research Date Visited: September 2 th, New Generation Research. inc. Suite 801, 22 Friend Street. Boston MA Ohlson, James, "Financial ratios and the probabilistic prediction of bankruptcy". Journal of Accounting Research 18, Pate, Carter (2002) Business Chapter 11 Bankruptcies: Recent Levels and Implicationsfor 2002, PricewaterhouseCoopers, March 2002 Shumway, Tyler, "Forecasting bankruptcy more accurately: a simple hazard model." Journal of Business 74, Sigaroudi, Sanaz, Paradi, J. Incorporating Ratios in DEA: Applications to Real Data, 2009, Masters Thesis. Centre for Management of Technology and Entrepreneurship, University of Toronto, Toronto, ON Simak, Paul, Paradi, J. DEA Analysis of Corporate Failure 1997, Masters Thesis. Centre for Management of Technology and Entrepreneurship, University of Toronto. Toronto, ON. Taffler, R.J., Forecasting Corporate failure In the UK using Discriminant Analysis and Financial ratio Data, Journal of R. Statist. Soc. A 14, 1982, pp Tran, Angela Paradi, J Two Stage Financial Risk Tolerance Assessment using Data Envelopment Analysis ProQuest Dissertations and Theses; 2007; ProQuest. Masters Thesis, Centre for Management of Technology and Entrepreneurship, University of Toronto, Toronto, ON. Weatherly, Leslie A. Human Capital The Elusive Asset: Measuring and Managing Human Capital: A Strategic Imperative for HR SHRM Research, Society for Human Resource Management, 2003 Growth of the Service Sector. " Date Visited: December 2th, World Bank 2009 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 70

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83 Appendix A: List of companies *Note that for the Year of bankruptcy descriptor, for nonbanktupt companies, the year refers to the year of bankruptcy of their bankrupt counterpart. Group 1 DMU Company Bankrupt/Non Bankrupt Flowers.com Inc 2 A.C. Moore Arts & Crafts Inc Year of bankruptcy Nonbankrupt 2001 Retail Specialty Nonbankrupt 2001 Retail Specialty 3 AccuHealth Bankrupt 2001 Diagnostic & Health Related Services Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms Sector Listed Country Exchange SIC SIC NAICS NAICS Incorpo Description Description rated US NMS 992 Florists Florists 1976 US NMS 94 Hobby, toy, and game shops US NBB 8082 Home Health Care Services 4 ACG Holdings Nonbankrupt 200 Printing US 274 Commercial printing, gravure AHT Corp Bankrupt 2000 Diagnostic & Health Related Services 6 All Star Gas Corp 7 AMC Entertainment 8 American Banknote 9 American Consumers Bankrupt 2003 Retail Specialty US NBB 8099 Health and allied Services US NL 984 Liquefied Petroleum Gas Dealers Nonbankrupt 2001 Entertainment US ASE 7832 Motion Picture Theaters ex drive in Bankrupt 200 Printing US NBB 274 Commercial printing, Nonbankrupt 2000 Retail Food & Beverage, Drug & Tobacco gravure US NBB 411 Grocery Stores Hobby, Toy, and Game Stores Home Health Care Services Commercial Gravure printing All Other Miscellaneous Ambulatory Health Care Services Liquefied Petroleum Gas (Bottled Gas) Dealers Motion Picture Theaters (except Drive Ins) Commercial Gravure Printing Supermarkets and Other Grocery (except

84 10 American Eco Corp 11 Ames Department Stores Bankrupt 2000 Miscellaneous Consumer Services Bankrupt 2002 Retail General Merchandise/ Department Stores 12 Arden Group Nonbankrupt 2001 Retail Food & Beverage, Drug & Tobacco 13 Ascena Retail Group Nonbankrupt 2004 Retail Apparel and Accessories 14 Avado Brands Bankrupt 2004 Hotels, Restaurants & Travel 1 Big Buck Brewery & Steakhouse 16 Big V Supermarkets Bankrupt 2004 Hotels, Restaurants & Travel Bankrupt 2000 Retail Food & Beverage, Drug & Tobacco 17 BioScrip Inc Nonbankrupt 2001 Diagnostic & Health Related Services 18 BonTon Stores Nonbankrupt 2002 Retail General Merchandise/ Department Stores Canada NBB 7699 Repair Services US NBB 331 Variety Stores US NMS 411 Grocery Stores US NMS 621 Women's clothing stores US NBB 812 Eating Places US NBB 812 Eating Places US 411 Grocery Stores US NMS 912 Drug Stores and Proprietary Stores US NMS 311 Department Stores Convenience) Stores Other Measuring and Controlling Devices All Other General Merchandise Stores Supermarkets and Other Grocery (except Convenience) Stores Women's Clothing Stores Full Service Restaurants Full Service Restaurants Supermarkets and Other Grocery (except Convenience) Stores Pharmacies and Drug Stores Department Stores (except Discount Department Stores) 19 Borders Nonbankrupt 2001 Retail US NBB 942 Book stores Book Stores 1994 Group Inc Specialty 20 Briazz Inc Bankrupt 2004 Hotels, US NBB 812 Eating Limited Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 73

85 21 Caliber Learning Network 22 Carmike Cinemas Inc Restaurants & Travel Bankrupt 2001 Educational Services Places US OTC 8299 Schools & Educational Services Nonbankrupt 2001 Entertainment US NMS 7833 Motion Picture Theaters ex drive in 23 Carrols Corp Nonbankrupt 2004 Hotels, Restaurants & Travel 24 Casual Male Corp Bankrupt 2001 Retail Apparel and Accessories 2 CD Warehouse Bankrupt 2002 Retail Specialty 26 Children's Place Retail Stores Inc 27 Cinemaster Luxury Theaters Inc 28 Commodore Applied Technologies 29 Computer Learning Centers Nonbankrupt 2000 Retail Apparel and Accessories US NBB 812 Eating Places US NBB 611 Men's & Boys Clothing US NBB 73 Record & Prerecorded tape stores US NMS 61 Family Clothing Stroes Bankrupt 2001 Entertainment US NAS 7834 Motion Picture Theaters ex drive in Nonbankrupt 2002 Sanitation Services Bankrupt 2001 Educational Services 30 Converse Bankrupt 2002 Apparel, Footwear & Accessories 31 Cooker Restaurant Corp Bankrupt 2001 Hotels, Restaurants & Travel US NBB 499 Sanitary services US OTC 8299 Schools & Educational Services US OTC 3021 Rubber and plastics Footwear US OTC 812 Eating Places Service Restaurants Professional and Management Development Training Motion Picture Theaters (except Drive Ins) Full Service Restaurants Men's Clothing Stores Prerecorded Tape, Compact Disc, and Record Stores Family Clothing Stores Motion Picture Theaters (except Drive Ins) All Other Miscellaneous Waste Professional and Management Development Training rubber and plastics footwear Full Service Restaurants 32 Crown Books Bankrupt 2001(2000) Retail US OTC 942 Book stores Book stores Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 74

86 Corp Specialty 33 Dairy Mart Bankrupt 2001 Retail Food & Beverage, Drug & Tobacco 34 Drug Emporium Inc 3 Eagle Food Centers 36 Eat At Joes Ltd Bankrupt 2001 Retail Food & Beverage, Drug & Tobacco Bankrupt 2000 Retail Food & Beverage, Drug & Tobacco Nonbankrupt 2004 Hotels, Restaurants & Travel 37 ELXSI Corp Nonbankrupt 2001 Hotels, Restaurants & Travel 38 etoys Inc Bankrupt 2001 Retail Specialty 39 Express Scripts Inc 40 Family Room Entertainment Corp Nonbankrupt 2001 Diagnostic & Health Related Services Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms US NBB 499 Miscellaneo us Food Stores US OTC 912 Drug stores and propietary stores US OTC 411 Grocery Stores US NBB 812 Eating Places US NBB 812 Eating Places US OTC 94 Hobby, toy and game shops US NMS 912 Drug Stores and Proprietary Stores Nonbankrupt 2001 Entertainment US NBB 7812 Motion picture & video production 41 Ferrellgas L.P. Nonbankrupt 2003 Retail Specialty 42 Florsheim Group Inc 43 Fresh Choice Inc Bankrupt 2002 Apparel, Footwear & Accessories Bankrupt 2004 Hotels, Restaurants & Travel US 984 Liquified Petroleum Gas Dealers US NBB 3143 Men's footwear, except athletic US NBB 812 Eating Places All Other Specialty Food Stores Pharmacies and Drug Stores Supermarkets and Other Grocery (except Convenience) Stores Full Service Restaurants Full Service Restaurants Hobby, Toy and Game Stores Pharmacies and Drug Stores Motion Picture and Video Production Liquified Petroleum Gas (Bottled Gas) Dealers Men's Footwear (except Athletic) Full Service Restaurants

87 44 Furr's Restaurant Group Inc Bankrupt 2003 Hotels, Restaurants & Travel 4 Gadzooks Inc Bankrupt 2004 Retail Apparel and Accessories 46 Gerald Stevens Inc 47 Grand Union Company Inc 48 Hartcourt Companies Inc 49 Hastings Entertainment Inc Bankrupt 2001 Retail Specialty Bankrupt 2000 Retail Food & Beverage, Drug & Tobacco Nonbankrupt 2001 Educational Services Nonbankrupt 2002 Retail Specialty US NYS 812 Eating Places US NBB 621 Womens Clothing Stores Cafeterias Women's Clothing Stores 1983 US NBB 992 Florists Florists 1970 US NBB 411 Grocery Stores Us NBB 8299 Schools & educational services US NAS 73 Record & prerecorded tape stores 0 HCI Direct Inc Bankrupt 2002 Textiles US NBB 221 Women's hosiery, 1 Healthcare Integrated Services 2 HeiligMeyers Company Bankrupt 2002 Diagnostic & Health Related Services Bankrupt 2000 Retail Furniture & Home Furnishings 3 Home Depot Nonbankrupt 2001 Retail Hardware & Home Improvement 4 Homeland Holding Corp Bankrupt 2001 Retail Food & Beverage, Drug & Tobacco except socks US NBB 8071 Medical Laboratories US NBB 712 Furniture Stores US NYS 211 Lumber and other building materials US NBB 411 Grocery Stores Supermarkets and Other Grocery (except Convenience) Stores All Other Miscellaneous Schools and Instruction Prerecorded Tape, Compact Disc, and Record Stores Sheer Hosiery Medical Laboratories Furniture Stores Home Centers Supermarkets and Other Grocery (except Convenience) Stores Horizon Bankrupt 2001 Retail Food & US NBB 912 Drug stores Pharmacies Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 76

88 Pharmacies Inc 6 House2Home inc 7 Image Innovations Holding Inc Beverage, Drug & Tobacco Bankrupt 2001 Retail Hardware & Home Improvement Bankrupt 2006 Leisure Equipment 8 Integra Inc Bankrupt 2002 Hospitals & Health Care Facilities 9 Integrated Health Services Inc 60 Jacobson Stores Inc 61 Jennifer Convertibles Inc 62 Jos. A Bank Clothiers Inc 63 Kasper ASL Ltd 64 KushnerLocke International Inc 6 LaCrosse Footwear 66 Lamonts Apparel Inc Bankrupt 2000 Hospitals & Health Care Facilities Bankrupt 2002 Retail General Merchandise/ Department Stores Nonbankrupt 2000 Retail Furniture and Home Furnishings Nonbankrupt 2001 Retail Apparel and Accessories Bankrupt 2002 Apparel, Footwear & Accessories and propietary stores US NBB 211 Lumber and other building materials US NBB 279 Commercial printing US NBB 8093 Specialty outpatient clinics, nec US NBB 8399 Social Services US NBB 311 Department Stores US NBB 712 Furniture Stores US NMS 611 Men's & boy's clothing stores US NBB 2337 Women's and misses' suits and coats Bankrupt 2001 Entertainment US NBB 7812 Motion picture and video production Nonbankrupt 2002 Apparel, Footwear & Accessories Bankrupt 2000 Retail Apparel and Accessories US NMS 3021 Rubber and Plastics Footwear US NL 61 Family Clothing and Drug Stores Home Centers Other Commercial Printing All Other Outpatient Care Centers Nursing Care Facilities Department Stores (except Discount Department Stores) Furniture Stores Men's Clothing Stores Women's and Girls' Cut and Sew Suit, Coat, Tailored Jacket and Skirt Motion Picture and Video Production Rubber and Plastics Footwear Family Clothing Stores Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 77

89 67 Lechters Inc Bankrupt 2001 Retail Furniture & Home Furnishings 68 Loews Cineplex Entertainment Corp Stores US NBB 719 Misc. homefurnish ings stores Bankrupt 2001 Entertainment US TSX 783 Motion Picture Theaters ex drive in 69 Med/Waste Inc Bankrupt 2002 Sanitation Services 70 Meritage Hospitality Group Inc 71 Mexican Restaurants Inc 72 New York Health Care Inc Nonbankrupt 2004 Hotels, Restaurants & Travel Nonbankrupt 2003 Hotels, Restaurants & Travel Nonbankrupt 2001 Diagnostic & Health Related Services 73 RadNet Inc Nonbankrupt 2002 Diagnostic & Health Related Services 74 Rocky Brands Inc 7 Sagemark Companies Ltd Nonbankrupt 2002 Apparel, Footwear & Accessories Nonbankrupt 2002 Hospitals & Health Care Facilities US NBB 499 Sanitary Services, nec US NBB 812 Eating Places US NBB 812 Eating Places US NBB 8082 Home health care services US NMS 8071 Medical Laboratories US NMS 3143 Men's Footwear, except athletic US NBB 8093 Specialty Outpatient Clinics All Other Home Furnishings Stores Motion Picture Theaters (except Drive Ins) All Other Miscellaneous Waste Management Services Limited Service Restaurants Full Service Restaurants Home Health Care Services Medical Laboratories Men's Footwear (except Athletic) All Other Outpatient Care Centers Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 78

90 Group 2 1 AFC Enterprises Inc Incorporated Non Bankrupt 2 AllAmerican SportPark Inc Non Bankrupt 3 Amazon.com Non Bankrupt 4 Amsurg Corp Non Bankrupt Blockbuster Inc Non Bankrupt 6 Cache Inc Non Bankrupt 7 Cala Corp Non Bankrupt 8 CareGuide Inc Non Bankrupt 9 Charming Shoppes Inc 10 Circuit City Stores Inc Non Bankrupt Non Bankrupt 11 DGSE Companies Non Bankrupt 12 Finlay Enterprises Non Inc Bankrupt 13 FragranceNet.com Non Inc Bankrupt Year of bankruptcy Sector Listed Country Exchange SIC SIC Description NAICS NAICS Description 2004 Hotels, Restaurants & Travel 2004 Sporting & Recreational 2002 Retail Specialty 2001 Hospitals & Health Care Facilities 2001 Retail Specialty 2004 Retail Apparel and Accessories 200 Property, Real Estate & Development 2004 Diagnostic & Health Related Services 2000 Retail Apparel and Accessories 200 Retail Appliances and Electronics 2003 Retail Specialty 2003 Retail Specialty 2000 Retail Specialty US NMS 812 Eating Places Limited Service Restaurants US NBB 7992 Public golf courses US NMS 961 Catalog and mail order houses US NMS 8011 Offices & clinics of medical doctors US NBB 7841 Video tape rental US NMS 621 Women's clothing stores US NBB 7011 Hotels and Motels US NBB 8099 Health and allied services nec US NMS 621 Women's Golf Courses and Country Clubs Electronic Shopping DMU Company Bankrupt/ Non Bankrupt Freestanding Ambulatory Surgical and Emergency Centers Video Tape and 1989 Disc Rental Women's 197 Clothing Stores Casino Hotels Internet Service Providers Women's clothing stores Clothing Stores US NBB 731 Radio, TV & Radio, Electronic Stores Television, and Other US ASE 944 Jewelry Stores Jewelry Stores US NBB 944 Jewelry Stores Jewelry Stores 1988 US NBB 999 Miscellaneous retail stores Drug and Druggidtd Sundries Merchant 1987 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 79

91 14 Great Atlantic & Pacific Tea Company Inc 1 Image Entertainment Inc Non Bankrupt Non Bankrupt 16 Internet Infinity Inc Non Bankrupt 17 Kirkland's Inc Non Bankrupt 18 Lowe's Companies Inc 19 Michaels Stores Inc 20 Million Dollar Saloon Inc Non Bankrupt Non Bankrupt Non Bankrupt 21 Movie Gallery Inc Non Bankrupt 22 National Record Mart 23 Natural Wonders Inc 2001 Retail Food & Beverage, Drug & Tobacco Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms Wholesalers US NBB 411 Grocery Stores Supermarkets and Other Grocery (except Convenience) Stores 2000 Entertainment US NBB 7822 Motion Picture and tape distribution 2000 Internet & Software 2003 Retail Furniture and Home Furnishings 2001 Retail Hardware & Home Improvement 2001 Retail Specialty 2001 Hotels, Restaurants & Travel 2000 Retail Specialty Bankrupt 2001 Retail Appliances and Electronics Bankrupt 2000 Retail Specialty US OTC 099 Durable Goods, nec US NMS 947 Gift, novelty and souvenir stores US NYS 211 Lumber and other building materials Motion Picture and Video Distribution Other Miscellaneous Durable Goods Merchant Wholesalers Gift, Novelty and Souvenir Stores Home Centers 192 Us NYS 94 Hobby, toy, and game shops Hobby, Toy, and Game Stores US NBB 812 Eating Places Limited Service Restaurants US NBB 7841 Video tape rental US NBB 73 Record & pre recorded tape stories US 999 Miscellaneous retail stores, nec 24 Netflix Non Bankrupt 2004 Entertainment US NMS 7841 Video tape rental 2 Netter Digital Bankrupt 2000 Entertainment US NBB 7812 Motion Picture & Video Production Video Tape and Disc Rental Prerecorded Tape, Compact Disc, and Record Stores All Other Miscellaneous Store Retailers (except Tobacco Stores) Video Tape and Disc Rental Motion Picture and Video Production

92 26 New Horizons Worldwide Inc 27 New York Bagel Enterprises Inc 28 Noble Roman's Inc 29 Omnicare Inc Non Bankrupt Non Bankrupt 2006 Business Services Bankrupt 2000 Hotels, Restaurants & Travel Non 2002 Hotels, Bankrupt Restaurants & Travel 2001 Diagnostic & Health Related Services 30 One Price Clothing Inc Bankrupt 2004 Retail Apparel and Accessories 31 Orbit Brands Bankrupt 2004 Sporting and Corporation Recreational 32 Overstock.com Non 2003 Retail Bankrupt Specialty 33 Paper Warehouse Inc 34 Park Pharmacy Corp 3 Paul Harris Stores Inc 36 Payless Cashways Inc 37 Pdg Environmental Bankrupt 2003 Retail Specialty Bankrupt 2002 Retail Food & Beverage, Drug & Tobacco Bankrupt 2000 Retail Apparel and Accessories Bankrupt 2001 Retail Hardware & Home Improvement Non Bankrupt 2000 Sanitation Services 38 PharMor Inc. Bankrupt 2001 Retail Food & Beverage, Drug & Tobacco 39 PHC Inc Non Bankrupt 40 Piccadilly Cafeterias Inc 2002 Hospitals & Health Care Facilities Bankrupt 2003 Hotels, Restaurants & Travel US NBB 8243 Data processing schools Computer Training US NBB 812 Eating Places Full Service Restaurants US OTC 812 Eating Places Full Service Restaurants US NYS 912 Drug Stores and Proprietary Stores US NBB 621 Women's clothing stores US NBB 7992 Public golf courses US NMS 961 Catalog and mail order houses US NBB 947 Gift, Novelty and Souvenir shops US NBB 912 Drug Stores and proprietary stores US OTC 621 Women's Clothing Stores US NL 211 Lumber and other building materials Pharmacies and Drug Stores Women's Clothing Stores Golf Courses and Country Clubs Mail Order houses Gift, Novelty and Souvenir Shops Pharmacies and Drug Stores Women's 192 Clothing Stores Home Centers 1988 US NBB 493 Refuse Systems Hazardous Waste Treatment and Disposal US NBB 912 Drug Stores and proprietary stores US ASE 8082 Home health care services Pharmacies and Drug Stores Home Health Care Services US NBB 812 Eating places Cafeterias Pier 1 Imports Inc Non 2000 Retail US NYS 719 Misc. home All Other Home 1986 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 81

93 42 Planet Entertainment Corp Bankrupt Furniture and Home Furnishings furnishings stores Bankrupt 2001 Advertising US NBB 7313 Radio, TV, publisher representatives 43 Play Co. Toys and Entertainment Corp Bankrupt 2001 Retail Specialty 44 Prandium Inc Bankrupt 2002 Hotels, Restaurants & Travel 4 Premier Concepts Inc Bankrupt 2003 Retail Specialty 46 Prosoft Learning Bankrupt 2006 Educational Corporation Services 47 Provell Inc Bankrupt 2002 Retail Specialty 48 Questar Assessment Inc Non Bankrupt 2000 Educational Services 49 Quokka Sports Inc Bankrupt 2001 Sporting & Recreational 0 Redline Bankrupt 2004 Retail Performance Automotive Products Inc Furnishings Stores Media Representatives US OTC 94 Hobby, toy and game shops Hobby, Toy and Game Stores US NBB 812 Eating Places Full Service Restaurants US NBB 944 Jewelry Stores Jewelry Stores 1988 US NBB 8243 Data processing schools US NBB 961 Catalog and mail order houses US NBB 8299 Schools & educational services US NBB 7999 Amusement and recreation nec US NBB 61 Recreational Vehicle Dealers 1 Regal Cinemas Inc Bankrupt 2001 Entertainment US NMS 7832 Motion picture theatres ex drive in 2 Rx for Africa Inc Non Bankrupt 2001 Leisure Equipment 3 Salex Holding Corp Bankrupt 2000 Miscellaneous Consumer Services 4 Samuels Jewelers Bankrupt 2003 Retail Inc Specialty Schlotzskys Inc Bankrupt 2004 Hotels, Restaurants & Travel 6 Showscan Entertainment Inc US NBB 7822 Motion Picture and tape distribution US NBB 749 Automotive Services Computer Training Electronic Shopping All Other Miscellaneous Schools and Instruction Other Spectator Sports Recreational Vehicle Dealers Motion Pictures Theatres Motion Picture and Video Distribution General Automotive Repair US NBB 944 Jewelry Stores Jewelry Stores 1982 US NBB 812 Eating places Limited Service Restaurants Bankrupt 2000 Entertainment US NBB 7832 Motion picture theaters ex drive Other Motion Picture and Video Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 82

94 7 Sight Resource Corp 8 Southern Investors Service Company Inc Bankrupt 2004 Diagnostic & Health Related Services Bankrupt 200 Hotels, Restaurants & Travel 9 Spiegel Inc Bankrupt 2003 Retail Specialty 60 Standard Management Corp Non Bankrupt 61 Star Buffet Inc Non Bankrupt 62 Steakhouse Partners Inc Non Bankrupt 2002 Retail Food & Beverage, Drug & Tobacco 2000 Hotels, Restaurants & Travel 2001 Hotels, Restaurants & Travel 63 Strouds Inc Bankrupt 2000 Retail Furniture & Home Furnishings 64 Stylesite Marketing Inc 6 Tender Loving Care Health Care Services Bankrupt 2000 Apparel, Footwear & Accessories Bankrupt 2002 Diagnostic & Health Related Services 66 Tesseract Group Inc Bankrupt 2000 Educational Services 67 Tilden Associates Non Bankrupt 68 TIX Corp Non Bankrupt 69 Tops Appliance City 2000 Miscellaneous Consumer Services Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms US NBB 8099 Health and allied services US NBB 7011 Hotels and motels US OTC 961 Catalog and mail order houses US NBB 912 Drug Stores and Proprietary in Industries Ophthalmic Goods Merchant Wholesalers All Other Traveler Accommodation Electronic Shopping Pharmacies and Drug Stores Stores US NBB 812 Eating Places Full Service Restaurants US NBB 812 Eating Places Full Service Restaurants US OTC 719 Misc. home furnishings stores US NBB 2399 Fabricated textile products nec US OTC 8082 Home health care services US NBB 8299 Schools & Educational Services US NBB 749 Automotive Services 2001 Entertainment US NBB 7999 Amusement and recreation nec Bankrupt 2000 Retail Appliances and US NBB 722 Household appliance stores Window Treatment Stores Linen Supply Home Health Care Services Professional and Management Development Training All Other Automotive Repair and Maintenance All Other Amusement and Recreation Industries Household Appliance Stores

95 70 Toys R Us Inc Non Bankrupt 71 U.S. Physical Non Therapy Bankrupt 72 U.S.A Floral Products Inc 73 Ultimate electronics Inc 74 Unapix Entertainment 7 United Artists Theatre Circuit Inc 76 United Petroleum Corporation Electronics 2001 Retail Specialty 2001 Hospitals & Health Care Facilities Bankrupt 2001 Retail Specialty Bankrupt 200 Retail Appliances and Electronics Bankrupt 2000 Entertainment US NBB 7822 Motion Picture and tape distribution Non Bankrupt 2001 Entertainment US 7832 Motion Picture Theaters ex drive in Bankrupt 2001 Retail Food & Beverage, Drug & Tobacco 77 USA Biomass Corp Bankrupt 2000 Sanitation Services Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms US NYS 94 Hobby, toy, and game shops Hobby, Toy, and Game Stores US NMS 8093 Specialty All Other Outpatient Outpatient Care Clinics Centers US OTC 193 Flowers & Flower, Nursery Florists supplies Stock and Florists Supplies Merchant Wholesalers US NBB 731 Radio, TV & Radio, Electronic Stores Television and Other Electronics Stores Motion Picture and Video Distribution Motion Picture Theaters (except Drive Ins) US OTC 411 Grocery Stores Convenience Stores US NBB 493 Refuse Systems Hazardous Waste Treatment and Disposal 78 Valley Media Inc Bankrupt 2001 Entertainment US NBB 7822 Motion picture and tape distribution 79 Video City Inc Bankrupt 2004 Entertainment US NBB 7841 Video Tape Rental 80 Video Update Inc Bankrupt 2000 Entertainment US NMS 7841 Video Tape Rental 81 Vision America Inc Bankrupt 2001 Hospitals & US NBB 8011 Offices & Health Care clinics of Facilities medical doctors 82 Wall Street Deli Bankrupt 2001 Hotels, Restaurants & Travel 83 West Coast Entertainment Inc Motion Picture and Video Distribution Video Tape and Disc Rental Video Tape and Disc Rental Offices of Physicians (except mental health specialists) US NBB 812 Eating Places Limited Service Restaurants Bankrupt 2001 Entertainment US OTC 7841 Video Tape and Disc Rental Video Tape and Disc Rental

96 84 Western Sizzlin Corp 8 Whole Foods Market Inc 86 WHSU Inc (fka Micro Warehouse Inc) Non Bankrupt Non Bankrupt 2003 Hotels, Restaurants & Travel 2000 Retail Food & Beverage, Drug & Tobacco Bankrupt 2003 Retail Specialty 87 Zany Brainy Inc Bankrupt 2001 Retail Specialty US NBB 812 Eating Places Full Service Restaurants US NMS 411 Grocery Stores Supermarkets and Other Grocery (except Convenience) Stores US NMS 961 Catalog and mail order houses US NBB 94 Hobby, toy and game shops Electronic Shopping Hobby, Toy and Game Stores Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 8

97 Appendix B: Financial Data ** Dollar values are recorded in thousands of dollars Group 1 1 year before bankruptcy DM U Current Assets 1 Current Liabiliti es 1 Workin g Capital 1 Retaine d Earning s 1 Operati ng Income 1 Book Value of Equity 1 Total Assets 1 Total Liabiliti es 1 Employe es 1 Sharehold ers Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms

98 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 87

99 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 88

100 2 years before bankruptcy DM U Current Assets 2 Current Liabiliti es 2 Workin g Capital 2 Retaine d Earning s 2 Operati ng Income 2 Book Value of Equity 2 Total Liabiliti es 2 Total Assets 2 Employe es 2 Sharehold ers , , , , , , , , , , , Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 89

101 , , , , , , , , , , , , , , , , ,293, , Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 90

102 38, , , , , , , , , , , , Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 91

103 3 years before bankruptcy Workin g Capital Retaine d Earning Operati ng Income Book Value of Equity DM U Current Assets 3 Current Liabiliti es 3 3 s Total Assets 3 Total Liabiliti es 3 Employe es 3 Sharehold ers Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 92

104 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 93

105 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 94

106 4 years before bankruptcy Workin g Capital Retaine d Earning Operati ng Income Book Value of Equity DM U Current Assets 4 Current Liabiliti es 4 4 s Total Assets 4 Total Liabiliti es 4 Employe es 4 Sharehold ers Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 9

107 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 96

108 , Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 97

109 years before bankruptcy Workin g Capital Retaine d Earning s Operati ng Income Book Value of Equity DM U Current Assets Current Liabiliti es Total Assets Total Liabiliti es Employe es Sharehold ers Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 98

110 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 99

111 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 100

112 Group 2 1 year before bankruptcy Book Value of Equity DMU Curren t Assets 1 Curren t Liabiliti es 1 Workin g Capital 1 Retain ed Earning s 1 Operati ng Income 1 1 Total Assets 1 Total Liabiliti es 1 Employe es 1 Sharehold ers Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms

113 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms

114 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms

115 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 104

116 2 years before bankruptcy Book Value of Equity DMU Curren t Assets 2 Curren t Liabiliti es 2 Workin g Capital 2 Retaine d Earning s 2 Operati ng Income 2 2 Total Assets 2 Total Liabiliti es 2 Employe es 2 Sharehold ers Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms

117 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 106

118 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms

119 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 108

120 3 years before bankruptcy Book Value of Equity DMU Curren t Assets 3 Curren t Liabiliti es 3 Workin g Capital 3 Retaine d Earning s 3 Operati ng Income 3 3 Total Assets 3 Total Liabiliti es 3 Employe es 3 Sharehold ers Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms

121 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 110

122 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms

123 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 112

124 4 years before bankruptcy Book Value of Equity DMU Curren t Assets 4 Curren t Liabiliti es 4 Workin g Capital 4 Retaine d Earning s 4 Operati ng Income 4 4 Total Assets 4 Total Liabiliti es 4 Employe es 4 Sharehold ers Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms

125 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms

126 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 11

127 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 116

128 years before bankruptcy Book Value of Equity DMU Curren t Assets Curren t Liabiliti es Workin g Capital Retaine d Earning s Operati ng Income Total Assets Total Liabiliti es Employe es Sharehold ers Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms

129 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 118

130 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 119

131 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 120

132 Appendix C: List of paired companies Bankrupt Company Healthy Company AccuHealth New York Health Care Inc AHT Corp Pacific Health Care Organization Inc All Star Gas Corp Ferrellgas L.P. American Banknote ACG Holdings Avado Brands Carrols Corp Big Buck Brewery & Steakhouse Eat At Joes Ltd Big V Supermarkets American Consumers Briazz Inc Meritage Hospitality Group Inc Casual Male Corp Jos. A Bank Clothiers Inc CD Warehouse Hastings Entertainment Inc Cinemaster Luxury Theaters Inc Carmike Cinemas Inc Computer Learning Centers Hartcourt Companies Inc Converse LaCrosse Footwear Cooker Restaurant Corp ELXSI Corp Crown Books Corp Borders Group Inc Drug Emporium Inc BioScrip Inc Eagle Food Centers Homeland Holding Corp etoys Inc A.C. Moore Arts & Crafts Inc Florsheim Group Inc Rocky Brands Inc Furr's Restaurant Group Inc Mexican Restaurants Inc Gadzooks Inc Ascena Retail Group Gerald Stevens Inc 1800 Flowers.com Inc Healthcare Integrated Services RadNet Inc HeiligMeyers Company Jennifer Convertibles Inc Homeland Holding Corp Arden Group Horizon Pharmacies Inc Express Scripts Inc House2Home inc Home Depot Image Innovations Holding Inc Kolorfusion International Inc Integra Inc Sagemark Companies Ltd Jacobson Stores Inc BonTon Stores KushnerLocke International Inc Family Room Entertainment Corp Lamonts Apparel Inc Children's Place Retail Stores Inc Loews Cineplex Entertainment Corp AMC Entertainment Med/Waste Inc Commodore Applied Technologies Natural Wonders Inc FragranceNet.com Inc New York Bagel Enterprises Inc Star Buffet Inc One Price Clothing Inc Cache Inc Orbit Brands Corporation AllAmerican SportPark Inc Paper Warehouse Inc Kirkland's Inc Park Pharmacy Corp Standard Management Corp Pathmark Stores Inc Whole Foods Market Inc Paul Harris Stores Inc Charming Shoppes Inc Payless Cashways Inc Lowe's Companies Inc Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 121

133 PharMor Inc. Piccadilly Cafeterias Inc Planet Hollywood International Inc Platinum Entertainment Inc Play Co. Toys and Entertainement Corp Prandium Inc Premier Concepts Inc Prosoft Learning Corporation Provell Inc Quokka Sports Inc Regal Cinemas Inc Salex Holding Corp Samuels Jewelers Inc Schlotzskys Inc Sight Resource Corp Southern Investors Service Company Inc Spiegel Inc Strouds Inc Tender Loving Care Health Care Services Tesseract Group Inc UCI Medical Affiliates Inc Ultimate electronics Inc Unapix Entertainment United Petroleum Corporation USA Biomass Corp Valley Media Inc Video City Inc Video Update Inc VisionAmerica Inc Wall Street Deli West Coast Entertainment Inc WHSU Inc (fka Micro Warehouse Inc) Zany Brainy Inc Omnicare Inc Western Sizzlin Corp Steakhouse Partners Inc Internet Infinity Inc Toys R Us Inc Noble Roman's Inc Finlay Enterprises Inc New Horizons Worldwide Inc Amazon.com TIX Corp United Artists Theatre Circuit Inc Tilden Associates DGSE Companies AFC Enterprises Inc CareGuide Inc Cala Corp International Commercial Television Inc Pier 1 Imports Inc PHC Inc Questar Assessment Inc U.S. Physical Therapy Circuit City Stores Inc Image Entertainment Inc Great Atlantic & Pacific Tea Company Inc Pdg Environmental Rx for Africa Inc Netflix Movie Gallery Inc Amsurg Corp Million Dollar Saloon Inc Blockbuster Inc Overstock.com Michaels Stores Inc Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 122

134 Appendix D: List of Altman Z Scores Group 1 DMU Altman yr1 Altman yr2 Altman yr3 Altman yr4 Altman yr Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 123

135 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 124

136 Group 2 DMU Altman yr1 Altman yr2 Altman yr3 Altman yr4 Altman yr Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 12

137 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 126

138 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 127

139 Appendix E: List of DEA Scores for Original Model Group 1 DMU yr1 yr2 yr3 yr4 yr Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 128

140 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 129

141 Group 2 DMU Year 1 Year 2 Year 3 Year 4 Year E E02.81E E E E E Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 130

142 E E03 6.8E04 2.3E E E E E E E E E E E E E04 7.9E E E E E E E E Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 131

143 E E Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 132

144 Appendix F: List of DEA Scores for Revised Model Group 1 DMU yr1 yr2 yr3 yr4 yr Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 133

145 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 134

146 Group 2 DMU yr 1 yr 2 yr 3 yr 4 yr Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 13

147 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 136

148 Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 137

149 Appendix G: Tstatistics for comparison of means Group 1 Ttest Year 1 Year 2 Year 3 Year 4 Year Current Assets Current Liabilities Working Capital Retained Earnings Operating Income Book Value of Equity Total Assets Total Liabilities Employees Shareholders Group 2 Ttest Year 1 Year 2 Year 3 Year 4 Year Current Assets Current Liabilities Working Capital Retained Earnings Operating Income Book Value of Equity Total Assets Total Liabilities Employees Shareholders Data Envelopment Analysis of Corporate Failure for NonManufacturing Firms 138