Study On The Prediction Of Corporate Bankruptcy

CHAPTER 1:

A number of researches have been carried on the prediction of bankruptcy; formal studies linked with failure of business were conducted in 1930’s. A study conducted by Simth and Winakor (1935) said that ratios of the failing firms were significantly changed from the continuing firms. In addition to that another study was related to the financial ratio of large size corporation that suffered in meeting fixed liability (Hickman 1958). Recent studies took potential ratios given in annual financial statements like profitability, solvency, and liquidity ratios considered as the most predictive indicator and these ratios were matched with failed and well worth firms for analysis. A group of financial and economic ratios were examined in the prediction of bankruptcy through multiple discriminant statistical technique, highest contributor ratios were profitability, operational profit/ total assets and very low contributor ratio was working capital/Assets (Altman, 1968).

According to Pastena and Ruland (1968), the bankruptcy was defined in the literature review in various ways. Among those one was in a condition of negative worth where the market value of assets was less than the total value of liabilities. And the other was that the firm was not in a condition to pay back its liabilities as it became due. This term could also be used in a legal condition under which the firms continued to operate under court protection.

1.2 Problem Statement

In the corporate finance, the prediction of corporate bankruptcy was considered to be one of the most important issues. The main objective behind the study of the prediction of corporate bankruptcy was that this was the most important issue for the present firms to either file for the bankruptcy or not.

The rationale of the study was to examine whether the financial ratios given in detail by Altman (1968) presented the detail regarding the factors of the firm which were helpful in the prediction of corporate bankruptcy in Pakistan. The capacity of study was to investigate the distinctive financial ratios which impacted the firms’ decisions to file for the bankruptcy or not and on the basis of firms’ financial ratios, the research study found the different significant ratios which were useful in determining the prediction of any of the organization.

1.3 Hypotheses

The main problem of the different firms was to identify those financial factors or the most important ratios which could lead to the filing of bankruptcy or those factors which were useful in determining the prediction of the corporate firms. A central query in front of firms which wanted to file for bankruptcy was why the firms filed for bankruptcy or what financial factors helped out in taking decision to file for bankruptcy. Various financial factors or ratios impacted the decision regarding the filing for bankruptcy. These financial characteristics or the most important ratios were current ratio, debt ratio, net profit margin, assets to long term debt ratio, and growth rate. Many authors as Altman (1968) discussed these characteristics in research. The hypothesized relationship of these listed financial factors with bankruptcy was provided below:

H1: There is a difference between the Current ratio of bankrupted companies and non bankrupted companies.

H2: There is a difference between the Debt ratio of bankrupted companies and non bankrupted companies.

H3: There is a difference between the Net Profit Margin ratio of bankrupted companies and non bankrupted companies.

H4: There is a difference between the Assets to long term debt ratio of bankrupted companies and non bankrupted companies.

H5: There is a difference between the Growth rate of bankrupted companies and non bankrupted companies.

1.4 Outline of the Study

The research structured as follows. Chapter one based on the introduction of the thesis, which consists of the some introduction of the prediction of bankruptcy by different authors, the statement of problem, scope and objectives, hypothesis etc. Chapter two consists of literature review given by different authors, theories on prediction of bankruptcy and financial factors affecting the choice of decision to file for bankruptcy or not. Chapter three described methodology which is composed of justification of the selection of the variables utilized in analysis sample, the data, technique and hypothesis, also estimate model utilized in analysis. In chapter four, analyses of the results were there which were taken after the data processing. Chapter five contained the final results, conclusions and recommendations. References are included in chapter number six.

CHAPTER 2:

LITERATURE REVIEW

A number of researches have been carried on the prediction of bankruptcy; formal studies linked with failure of business were conducted in 1930’s. A study conducted by Simth and winakor (1935) said that ratios of the failing firms were significantly change from the continuing firms. In addition to that an other study was related to the financial ratio of large size corporation that suffered in meeting fixed liability (Hickman 1958). Recent studies took potential ratios given in annual financial statements like profitability, solvency, and liquidity ratios considered as the most predictive indicator and these ratios were matched with failed and well worth firms for analysis. A group of financial and economic ratios were examined in the prediction of bankruptcy through multiple discriminant statistical technique, highest contributor ratios were profitability, operational profit/ total assets and very low contributor ratio was working capital/Assets (Altman, 1968).

A study conducted by Sandin and Porporato (2007) on corporate bankruptcy prediction model applied to emerging economies. The aim of this study was to find the predictability of bankruptcy by using the financial ratios given in the financial statements and these financial statements were taken from the Buenos Aires Stock Exchange. To test the hypothesis twenty two bankrupt and non bankrupt companies were examined by using the multiple discriminant analysis technique, resulted that financial ratios were very useful in predicting the bankruptcy. Actually this study was about the prediction model and classification of the distressed and failed companies in the Argentina.

William Beaver (1996) conducted a study that Financial Ratios As Predictor of Failure, wherein ratios were tested for a specific purpose. The purpose was to forecast the failure. Since ratios were mostly examined for the prediction of failure. The aim of the study was to analyze the status quo that was depended on the financial statements made under the reporting standard and this study was conducted as a bench mark for further studies in bankruptcy area. Sample of data was selected on the basis of industry, firm size and period, Walworth companies should have taken from the same industry where from failed companies taken along with same firm size based on firm value and equal time duration then reliable result can be obtained said by Beaver (1996). This study pointed out and directed to the asset size and relationship among ratios, assets size and failure, study implicated that larger firms were more solvent than smaller firms, even if ratios were same. To examine the hypothesis, a paired analysis was used.

According to Pastena and Ruland (1968), the bankruptcy was defined in the literature review in various ways. Among those one was in a condition of negative worth where the market value of assets was less than the total value of liabilities. And the other was that the firm was not in a condition to pay back its liabilities as it became due. This term could also be used in a legal condition under which the firms continued to operate under court protection.

Merger and Bankruptcy

Based on the literature review in the different research studies, it was found that the shareholders of the distressed firms were getting more benefit from mergers than from the bankruptcy. Thus, the investors kept the positive number of the firm’s stocks up as a consequence of the merger. Contrastingly, the stakeholders received nothing in case of the bankruptcy. Shrieves and Stevens (1979) managed to explain all of the possible reasons for preferring merger over bankruptcy and those principles included: (1) to avoid the bankruptcy legal and administrative costs, (2) possible loss of tax carry forwards of the loss firm incurred on liquidation, (3) the value of the going-concern in the merger was more than liquidation value if the firm bankruptcy progressed towards the liquidation, and (4) the bankruptcy created the bad effects on the revenues including sales and income due to the customer fears of inability contracts, give replacement parts, etc.

Bulow and Shoven (1978) noticed based on the research that the investors have always been avoiding the bankruptcy and this tendency always benefitted the creditors as a whole and that theoretically, the bankruptcy occurred because of the disagreement between the concerned parties. This was treated in a literature that the merger was the best possible alternate of the bankruptcy with the assumption in the mind that it was more easy for the distressed firms to find a merger partner at some price as long as the net asset value was positive and this was also under the assumption of a well-functioning market for information. When the situation was aggravated toward a condition of less or negative net asset value, the possibility of merger was reduced.

Hong (1983) made an empirical as well as theoretical model which distinguished among three different categories of financially upset firms and it was organized in three ways such as: firms which filed bankruptcy but reorganized successfully, firms which filed for bankruptcy but were liquidated ultimately, and also the firms which continued operations with out even filing for bankruptcy. Author further made a hypothesis that the intangible assets, the value of the firm as in a going concern and the value of the same firm in liquidation was different, were the main describing factor which affected the eventual outcome. The firms which had greater intangible assets were possibly having a sustainable economic growth and that growth allowed a firm to survive rather than be liquidated.

LoPucki (1983) made an explanatory study of about 41 firms which filed the bankruptcy court of the Western District of Missouri. In this study, the “successes? were defined as the firms which have verified its various reorganization strategies that kept it on to survive for about three years after the date of petitioning the bankruptcy. Failures according to the author were those firms which had stopped operating functions before February 1983. LoPucki (1983) further could not try to make a method with discriminatory power, but in fact simply scrutinized the associations between the results of reorganization process and numerous individual variables. These individual variables included size, age, and type of the businesses, the survival of creditors’ opposition to the reorganization strategy, and physical geographic location. The relationships which were found during the research were: significantly higher success rate was associated with the manufacturing firms; more successful firms were only the larger firms; success was not significantly associated with the age of the firms; the target opposition of the creditors was mainly at the more successful firms; and lastly, the physical geographical location was not a significant describing variable.

In short, only a finite amount of research was conducted on the topic of differentiating between failures and successes in bankruptcy, and outcomes have been open to doubts or inconclusive. The one published study conducted by the LoPucki (1983), scrutinized the first order correlations and could not struggle to build the model of classification. The other published research study conducted by Hong (1983), scrutinized the comparative importance of numerous individual variables and had not analyzed the classification authentication of the multivariate model. As it was already discussed in detail, this present study scrutinized the classification authentication of the multivariate model by using data from both analysis sample and a holdout sample 113 firms.

Bordman, Bartley, and Ratliff (1981) noticed that firms went bankrupt only when its capital resources were not enough to pay back the obligations of the business. Thus it became the more important challenge for the new comers in the industry to maintain and establish such valuable resources and capabilities which could ultimately leaded to the production of positive cash flows before starting asset resources were exhausted (Levinthal, 1991). According to D’Aveni (1989), and Hambrick and D’Aveni (1988), both researches have noticed that most of the attention has been paid to the early failures and dramatic research has also been conducted in the literature.

A macro view of the bankruptcy was given as a known strategy and an empirical examination of factors associated to successful reorganization (Moultan, and Thomas, 1993) and however, the structures of corporate governance were not incorporated in the analysis. An extensive data was available relating the intensity to which the officers and directors of the firm which was bankrupt were more possibly resigning or were being replaced (D'Aveni, 1990; Fizel & Louie, 1990; Gilson, 1989).

Several researchers used the multiple discriminant analysis MDA technique to develop a linear model to predict those firms which failed could be differentiated from the non-failed firms in UK (Taffler, 1977). This model resulted in an overall classification authentication for the year before the failure as comparative to three or two prior years of failure. The major contribution made by Taffler was the establishment of a Z-score model which was used for the prediction of company failures in the UK and furthermore, the author claimed of 100 percent predictive authentication in the model. In addition, in the consequent studies, Taffler (1982, 1983) discussed the pairing technique which was used in the prediction of corporate failure studies proved no more successful technique than any selection by the other tool or technique. Multiple Discriminant Analysis MDA models were dependable to certain intensity in the prediction of corporate failure.

CHAPTER 3:

RESEARCH METHODS

3.1 Method of Data Collection

Data was selected from Karachi Stock Exchange KSE 100 Index as given by State Bank of Pakistan in publication Balance Sheet Analysis of Joint Stock Companies Listed on the KSE (2004-2009). The period of study covers six years, 2004-09. The opted sample size of 44 firms was taken from KSE 100 Index and all of the firms listed on KSE 100 Index were selected for the samples which were going to bankruptcy in the past and some were also the present functioning firms which were currently working; so, only 44 firms included in the sample period of 2004-09. The objective behind the inclusion of these selected firms in the sample was that the inclusion of bankrupt and non-bankrupt firms in the analysis made it easier to distinguish the critical financial ratios of these both firms in order to predict for corporate bankruptcy.

The data availability was the major issue faced in this research study. The secondary data sources were adopted for the collection of the data during this research study. Both of the empirical and theoretical aspects regarding the prediction of corporate bankruptcy were analyzed in this research study. For the purpose of the collection of the secondary data, external data sources were used, such as the data was collected from State Bank of Pakistan, general business publications, newspapers and journal articles, annual reports, internet and books. The data required for this study was completely dependent on the published data sources, such as the published sources listed above.

3.2 Sample Size

A sample of 44 firms from KSE 100 Index was selected and in addition, out of these firms 22 firms were bankrupt and the remaining 22 were not bankrupt which was taken as the holdout sample for the prediction of the corporate bankruptcy. Only firms were used in the samples which were either became bankrupt due to the impact of the some of the financial factors or the ratios or the firms which were in operations during the research study was conducted and these firms were listed on the KSE 100 Index form 2004-2009. The impact of the different financial factors or ratios, which were listed in the previous chapters, on the prediction of corporate bankruptcy was analyzed on all of the firms selected as the sample.

3.3 Research Model Developed

There are various financial factors or the ratios of the firms which affected the prediction of the corporate bankruptcy of the firms. This research study analyzed the impact of different factors or ratios already listed in the previous chapters on the prediction of corporate bankruptcy. The model developed was a binary logistic model and its specifications are provided below:

Liquidity = a0 + a1Firm Size + a2DEBT + a3LTD + a4LSALES + a5OI/S + a6OI/TA+

a7IGP/TA+ a8Market to Book Ratio + є where:

Liquidity = the sum of cash and marketable securities divided by total assets

Firm Size = natural log of the book value of total assets

DEBT = the ratio of shorter period plus longer period debt to total assets

LTD = the ratio of longer period debt to total assets

LSALES = natural log of the annual sales

OI/S = the ratio of operating income to sales

OI/TA = the ratio of operating income to total sales

IGP/TA = the inventory plus gross fixed assets to total assets ratio

Є = the error term

3.4 Statistical Technique

Binary Logistic Regression Analysis technique was used for this research study to examine the impact of the distinctive financial characteristics or the financial ratios of the firms on the prediction of corporate bankruptcy of the selected firms; Statistical Package for the Social Sciences (SPSS) was used for the examination of the secondary data.

Binary Logistic Regression Analysis technique was used for the purpose of prediction of of corporate bankruptcy or the prediction of the firms’ decisions to file for bankruptcy. The selected technique was used to study the impact of the different independent variables (financial factors as listed in the previous chapters) on the dependent variable i.e., prediction of corporate bankruptcy. The binary logistic regression analysis was selected for this study. It showed the intensity of the impact on the prediction of corporate bankruptcy during year 2004-2009 on the basis of several independent variables.

CHAPTER 4:

RESULTS

The sample of 44 firms from the Karachi Stock Exchange KSE 100 Index was taken; Binary Logistic Regression Analysis technique was used for this research study. Research examined the distinctive financial characteristics or financial ratios of firms which filed for the bankruptcy. The selected technique was used to study the impact of the different independent variables (financial factors as listed in the previous chapters) on the dependent variable i.e., the prediction of corporate bankruptcy. Statistical Package for the Social Sciences (SPSS) was used for the analysis and examination of data.

4.1 Findings and Interpretation of the results

Initially, the binary logistic regression technique was applied on the data collected using SPSS. Now, it was a nice time to proceed with the analysis of the results because the data was collected and ready to be examined. The interpretation and analysis is presented in the next sections of this research study.

Case Processing Summary

Unweighted Casesa

N

Percent

Selected Cases

Included in Analysis

192

91.4

Missing Cases

18

8.6

Total

210

100.0

Unselected Cases

0

.0

Total

210

100.0

This table explains the total population in the data file that is the 210 observations or the cases for the analysis of the bankruptcy prediction. This table further elaborates that the there were also some of the cases missing in the data because of the issue of data availability and some of the cases were the figures of zero.

Dependent Variable Encoding

Original Value

Internal Value

Bankrupt

0

Non-Bankrupt

1

The above table shows that there are only two variables in the dependent variable of bankruptcy that are the being bankrupt or non-bankrupt.

Model Summary

Step

-2 Log likelihood

Cox & Snell R Square

Nagelkerke R Square

1

234.707a

.144

.192

This table elaborates the predictability of the complete model of the logistic regression which meant that to what extent the model predict the variation in the predicted group of bankruptcy. According to Cox & Snell, the total predictors jointly explained variation in the groups of bankruptcy was 14.4%. While according to Nagelkirki, the all independent variable explained the group prediction of about 19.2%.

Hosmer and Lemeshow Test

Step

Chi-square

df

Sig.

1

32.715

8

.000

This table checks the overall model fit which means that the model is at its best in predicting the group variation from non-bankrupt to bankrupt. The hypothesis of the above table is that the test model is fit. The hypothesis is rejected because the sig value is less than .05 which concluded that the test model was not fit in this case of predicting the group variation.

Classification Tablea

Observed

Predicted

Banckruptcy

Percentage Correct

Bankrupt

Non-Bankrupt

Step 1

Banckruptcy

Bankrupt

76

29

72.4

Non-Bankrupt

43

44

50.6

Overall Percentage

62.5

The classification table is the most important table in case of the logistic regression because this table explained the correct identification of the cases correctly identified. The percentage of correctly identified cases is 62.5% which is also commonly known as the hit ratio which means that to what extent the numbers of cases were correctly identified.

Variables in the Equation

B

S.E.

Wald

df

Sig.

Exp(B)

95% C.I.for EXP(B)

Lower

Upper

Step 1a

DA

-1.219

.510

5.725

1

.017

.295

.109

.802

AtoLTD

-.002

.001

1.583

1

.208

.998

.996

1.001

CR

.938

.348

7.242

1

.007

2.554

1.290

5.056

NPM

.037

.073

.262

1

.609

1.038

.899

1.198

SG

.161

.232

.482

1

.488

1.175

.745

1.852

Constant

.066

.579

.013

1

.910

1.068

This is the final most important table in the logistic regression because this is the only table which shows the role of different predictors in significantly explaining the role in the prediction of group variations. Those important significant variables were only two that were DA, and CR because the sig value of only these variables were less than .05.

4.2 Hypotheses Assessment Summary

The hypothesis of the study was distinctive financial ratios have significant impact on the non firms’ decision to file for bankruptcy. These financial characteristics were current ratio (CR), debt ratio (DA), net profit margin (NPM), assets to long term debt ratio, and growth rate. In this study each of the financial characteristics or financial ratios of firms was tested and concluded the results.

TABLE 4.4 : Hypotheses Assessment Summary

S.NO.

Hypotheses

β

 

 

SIG.

RESULT

H1

There is a difference between the Current ratio of bankrupted companies and non bankrupted companies.

0.938

0.007

Accepted

H2

There is a difference between the Debt Ratio of bankrupted companies and non bankrupted companies.

-1.219

0.017

Accepted

H3

There is a difference between the Net Profit Margin Ratio of bankrupted companies and non bankrupted companies.

0.037

0.609

Rejected

H4

There is a difference between the Assets to long term debt ratio of bankrupted companies and non bankrupted companies.

-0.002

0.208

Rejected

H5

There is a difference between the Growth rate of bankrupted companies and non bankrupted companies.

0.161

0.488

Rejected

CHAPTER 5:

DISCUSSIONS, CONCLUSION, IMPLICATIONS AND FUTURE RESEARCH

5.1 Conclusion

It was concluded based on the results of this research study that current ratio and debt ratio were only the independent variables which were showing significance in Pakistani market and these variables were highly significant in playing the vital role explaining the variation in the dependent variable of the prediction of corporate bankruptcy and the remaining independent variables could not explain the variation in the prediction of corporate bankruptcy. These results were not matching with the study conducted by Altman (1968). These results were varying because in various countries, there was difference in environments and circumstances and firms usually made decision accordingly.

5.2 Discussions

Current ratio played a significant role in defining the variation in the prediction of corporate bankruptcy and this was also the case with the research study conducted by Altman (1968) because in his study the firm size was also playing a significant role. The variation in the prediction of corporate bankruptcy was not explained by the net profit margin ratio while it was significant in the study done by Altman (1968). The assets to long term debt ratio, and growth rate were not significantly explaining the variation in the prediction of corporate bankruptcy and study analyzed by Altman (1968), concluded the different results with some addition.

5.3 Implications and Recommendations

This research was limited to the various firms listed on Karachi Stock Exchange of Pakistan only. The data taken from 44 firms which were took through various sectors of the KSE 100 Index for the year 2004-09 which were previously bankrupt and which were currently operating. It suggested that such type of study should be carried out in other countries of Asia as well, as to have comprehensive idea about the choices of the firms’ decision to file for bankruptcy. Moreover, it also suggested that other factors except ones examined in this study should be researched as to have perfect idea about the selection of the prediction of corporate bankruptcy. Besides that, this study can also be replicated in other developing countries.

5.4 Future Research

This study helped various investors, management and other research conductors in analyzing and observing the behavior of firms regarding their decisions to file for the bankruptcy. Research students who want to work further on the prediction of bankruptcy can be benefited by this research study. Further more, the firms will become advantageous from this study because the study clarifies the distinctive financial characteristics or the financial ratios of different firms which significantly explain the variations in the prediction of corporate bankruptcy.