Prediction Of Corporate Bankruptcy Through Financial Ratios Finance Essay
This paper aims to define the importance of ratio analysis in evaluation of firms financial position and performance, second, To identify that which ratios has a significant role in the prediction of corporate failure, and third, It is possible to predict the corporate failure through the use of financial ratios 2 years prior to failed or bankrupted. Ten financial ratios covering four important financial attributes namely liquidity, activity and turnover, profitability, and leverage ratios has examined for a two-year prior bankruptcy. Multiple Discriminant Analysis (MDA) has used as statistical technique with the help of SPSS 17.0 Version on a sample of twenty six bankrupt and twenty six non-bankrupt firms two year prior bankrupted with the Asset range of 5 million to 750 million from the period of 1996-2010. The discriminant analysis model produced that Profit Margin, Debt to Equity ratio, and Return on Assets has a significant contribution in prediction of corporate bankruptcy. Our estimates provide the evidence that the firms having Z value below zero fall into “BANKRUPT” whereas the firms having Z value above the zero fall into “NON-BANKRUPT” category. Our model achieved 82% prediction accuracy from original selected cases and 100% prediction accuracy from original not selected cases when it is applied to forecast bankruptcies on the underlying sample.
Financial ratios have played an important part in evaluating the performance and financial condition / position of any firm. It helps to know the strength or weaknesses of the firm and to make forecast about the future prospects of the firm and thereby enabling the decision makers to take different decisions regarding the operations of the firm. Ratio analysis isn't just comparing different numbers from the balance sheet, income statement, and cash flow statement. It's comparing the number against previous years, other companies, the industry, or even the economy in general. Ratios define the relationships between individual values and relate them to how a company has performed in the past, how is performing in present and might perform in the future. There are numbers of financial ratios used in analysis to evaluate the performance and financial position of a firm. But most popular are, Solvency, Stability, Profitability, Operational efficiency, Credit standing, Structural analysis, Effective utilization of resources, and Leverage or external financing.
A fair number of different researchers has been worked in this field of research in previous years; the more notable published contributions are Beaver (1966; 1968), Altman (1968; 1973), Altman and Lorris (1976), Deakin (1972), Libby (1975), Blum (1974), Edmister (1972), Dambolena and Khoury (1980), Ohlson (1980), and Horrigan (1965). Two unpublished papers by White and Turnbull (1975a; 1975b) and a paper by Santomero and Vinso (1977) are of particular interest as they appear to be the first studies which logically and systematically develop probabilistic estimates of failure. The present study is similar to the latter studies.
As a tool of financial management, ratios are more important. It presents facts on a comparative basis & enables us to draw a true picture of a firm. Ratio analysis allows to assessing the performance of a firm in respect of the following aspects:
1] Liquidity position,
2] Long-term solvency,
3] Operating efficiency,
4] Overall profitability,
5] Inter firm comparison
6] Trend analysis.
1.1- Advantages of Ratio Analysis
Financial ratios are essentially concerned with the identification of significant accounting data relationships, which give the insight financial information and performance of a company to decision-maker. The advantages of ratio analysis can be summarized as follows:
Ratios help in conducting trend analysis, which is important for decision making and forecasting.
Ratio analysis helps in the assessment of the liquidity, operating efficiency, profitability and leverage of a firm that defines the true performance picture of a firm.
Ratio analysis provides a basis for both intra-firm as well as inter-firm comparisons.
The comparison of current actual ratios with base year ratios or standard ratios helps the management to analyze the financial performance of the firm.
Financial ratios are the only variables that are used in determining the bond ratings.
1.2- Limitations of Ratio Analysis
Ratio analysis has its limitations. These limitations are described below:
1) Information problems
Ratios require quantitative information for analysis of firm but it is not defines about analytical output of firm.
The figures used in ratios are extracted from accounting data and accounts are likely to be at least several months out of date, and so might not give a proper indication of the company’s current financial position.
Historical cost convention is used of fixed assets in balance sheet, asset valuations in the balance sheet, so balance sheet information can be wrong and could be misleading. Based on this wrong information, decision-making will not be very useful.
2) Comparison of performance over time
Prices are changing rapidly. Few years back, prices was changed on monthly or yearly basis but now prices are changing on daily basis. When comparing performance over time, there is need to consider the changes in price effect.
Technology is also changing rapidly, when comparing performance over time, there is need to consider the changes in technology. The movement in performance should be in line with the changes in technology.
Changes in accounting policy may affect the comparison of results between different accounting years. It could be misleading. So there is also need to consider the changes in accounting policies.
3) Inter-firm comparison
Companies may have different capital structures, one may be use equity financed and another may be a geared company. In that situation, comparison of performance may not be a good analysis.
Government gives an incentive to various Selective companies. In that situation, comparing the performance of two enterprises may be misleading.
Inter-firm comparison is only useful if the firms compared are of the same size and age, and employ similar production methods and accounting practices. Otherwise it may not be a good analysis.
Ratios only provide quantitative information of firms and not qualitative information of firms.
Ratios are calculated on the basis of past financial statements. They do not indicate future trends and prospective and they also do not consider economic conditions.
Over the years, empirical studies have repeatedly demonstrated the usefulness of financial ratios. For example, financially-distressed firms can be separated from the non-failed firms in the year before the declaration of bankruptcy at an accuracy rate of better than 94% by examining financial ratios (Altman 1968).
Business failure is a natural phenomena in our economic system in which some firms enter and exit as function of overall business activity and expectations. The failure of a business firm is an event which can produce substantial losses to creditors, financers, investors and stockholders. Therefore, a model which predicts potential business failures as early as possible can be reducing such losses by providing early warning to these interested parties and stockholders. That model can also help the management to take corrective actions before the failure occurred.
Bankruptcy is defined as the inability of a company to continue its current operations due to having high debt obligations (Pongsatat et al., 2004). Typically Failure is defined as the inability of a firm to pay its financial obligations as they mature. Operationally, a firm is said to have failed when any of the following events have occurred: bankruptcy, bond default, an overdrawn bank account, or nonpayment of a preferred stock dividend.
The definition of bankruptcy varies from country to country. For example, in the United States, there are two proper legal chapters (Chapter 7 and Chapter 11) that define in which conditions or situations a firm is considered as bankrupt (Altman, 1968). Similarly, in Japan, there are three basic laws (the Civil Rehabilitation Law, the Corporate Reorganization Law and the Liquidation Law) that defines the bankruptcy (Xu and Zhang, 2008). Due to the lacking of generalized definition, several studies such as Beaver, 1966 and Tavlin et al, 1989 have defined bankruptcy according to the rationale and scope of their study. Thus, the concept of bankruptcy in this study is similar to the bankruptcy concept described in various studies. A bankruptcy is defined in this study is, if any of the following actions have occurred is considered a firm bankrupt in Pakistan.
1. Company delisted by Karachi Stock Exchange (KSE) due to liquidation / winding up under court order i.e. violation of listing regulation no. 32 (1) (d).
(Listing Regulation no.32 (1) (d) = If company has gone into liquidation either voluntarily or under court order)
2. Winding up of company by Securities and Exchange Commission of Pakistan (SECP).
Pakistan is a developing country with emerging different industries. A large number of bankruptcy incidences have been occurred since the last two decades in Pakistan. Hence, this study recognized a need to develop a bankruptcy prediction model in order to protect additional failure of the companies in Pakistan. Bankruptcy prediction models would provide help to regulator authorities in Pakistan to keep timely monitoring and enhancing the financial position of the companies. And investors and bank loan officers should review the financial position of companies before taking any decision of investment or decision of loan giving. It will helpful for investors to take decision prior 3 years that either should invest or not in this firm or securities, financial position of that firm is strong or it will bankrupted in future. It will also helpful for bank loan officers to measure the financial position by using financial ratios and predict corporate failure before taking decision of loan giving to firms. This research will enable management to take preventative measures; operating policy change, reorganization of financial structure, and liquidity position that will help to take timely decision and thereby improve both private and social resource allocation.
2- LITERATURE REVIEW
Since 1960s, many researchers from different countries have been worked in this field of research in different time of periods to examine the bankruptcy prediction. One is a limited study by Altman and McGough (1974) in which failed firms were drawn from the period 1970-73 and one type of classification error (misclassification of failed firms) was analyzed. Moyer (1977) considered the period 1965-75, but the sample of bankrupt firms was unusually small (twenty-seven firms). The third study, by Altman, Haldeman, and Narayanan (1977), which "up- dates" the original Altman (1968) study, basically considers data from the period 1969 to 1975. Their sample was based on fifty-three failed firms and about the same number of non-failed firms. In contrast, this study is similar to the later studies but it relies on observations from 26 bankrupt and 26 non-bankrupt production firms. The data set used in this study is from the 1996-2010. And although the methodology and objective of research is also differ from previous studies.
Bankruptcy is a worldwide problem that can happen both in developed and developing economies. However, it occurs overly in developing economic environments. Some of the major causes behind corporate failures are different and varies across countries that are difference in capital structures, accounting standards and social, political, economic environment (Newton, 1985, Argenti, 1976, Her and Choe, 1999). Financial structure and the nature of financial risk in Pakistani companies differ substantially from U.S. firms and other countries firms. The major findings of this study can be summarized briefly. First, To define the importance of ratio analysis in evaluation of firms financial position and performance, second, To identify that which ratios has a significant role in the prediction of corporate failure, and third, It is possible to predict the corporate failure through the use of financial ratios 2 years prior to failed or bankrupted.
The analytical study on this issue that the quality of ratio analysis is an analytical technique in the prediction of corporate bankruptcy was begun by Altman (1968). He says that traditional ratio analysis is no longer an important analytical technique in the academic environment due to the relatively unsophisticated manner and a set of financial ratios combined in a discriminant analysis approach is a best technique in examine the problem of corporate bankruptcy prediction. Altman found that the age of the firm has a significant impact on its chance of failure. Another study had done by Altman (1973) to discuss and analyze the Railroad Bankruptcies in America. Through the use of Linear Discriminant Analysis (LDA) on twenty-one railroads that went bankrupt and same as non-bankrupt railroad between the years 1939-1970, he found that the major reasons for the railroad industry's dismal performances are: (1) inflexible pricing and cost structure of a firm (2) large net income losses during periods of economic stress due to a heavily leveraged fixed asset and liability structure (3) excess capacity (4) the acute labor and manpower rigidities; and (5) a shortage of innovative management. Many of these problems are the by-products of government regulation and industry rigidities.
Beaver (1966) was the one who use a paired sample analysis with size and industry type used as bases for pairing financial ratios to predict corporate failure. Beaver found overwhelming evidence that financial ratios are used to detect the firm’s financial illness and we can detect the firm’s financial illness before the failure occurred and proper treatments can apply to stop the failure. To test the predictive power of ratios, Beaver used a dichotomous classification technique, and found the cash flow to total debt ratio to be the best predictor of failure five years preceding failure. Another similar study had done by James A. Ohlson (1980). He studies the probabilistic estimates of corporate failure as evidenced by the event of bankruptcy through some empirical results. The data set used by James A. Ohlson is from the seventies (1970-76). Through the use of Conditional Logit Analysis (CLA) he draws a conclusion that, the predictive power of any model is depends upon available financial information. And if sample size will be large than the prediction will be more accurate and The size of the company, a measure(s) of the financial structure, a measure(s) of performance, a measure(s) of current liquidity can be predict the probability of failure (within one year). Through the use of statistical techniques, particularly Disciminant analysis by Edward B. Deakin (1972), he found that we can predict business failure from accounting data as far as three years in advance with a fairly high accuracy. With the help of sample of Thirty-two failed firms and thirty-two non failed firms from a population which experienced between 1964 and 1970. He succeeded in correctly classified 90% of all firms that failed or did not fail in the next one to three years. Similarly, Marc Blum (1974) identify the failing companies’ priori 2 years with the help of financial ratios and market data through the use of Discriminant Analysis on sample of 115 companies which failed during 1954-1968. he conclude that failing company model can predicts accurately 93-95 percent at the first year before failure and 80 percent at second year before failure.
Financial early warning system helps the investors to evaluate the financial position of firm before making any decision of investment in firms. Altman and Loris (1976) found and developed the FEWS (Financial Early Warning System). Data was selected on 40 firms as Failed Firm group, and 113 firms chosen randomly from the list of NASD (National Association of Securities Dealers) as Active or healthy group. After applying the Quadratic Discriminant Analysis (QDA) technique on sample they drew a conclusion that Net Income After Taxes/Total Assets, (Total Liabilities + Subordinated Loans)/Owner's Equity, Total Assets/Adjusted Net Capital, Ending Capital -Capital Additions/Beginning Capital, Scaled Age and Composite plays an important role In prediction of corporate failure and through these ratios prediction of corporate failure can be easily identified. Through the use of market variables and accounting variables to study the predictors of corporate failure that which are the reliable predictors in prediction of corporate bankruptcy had done by Beaver (1968). After applying cross-section analysis and time-series analysis he found that changes in prices of stock and ratios are depend on investor understanding. The lack of perfect association between the forecasts indicates that investors either respond to non-ratio sources of information, or respond to ratio source of information or both. He suggests that a multi-ratio model, consisting of the most recent value of the cash flow ratio and the first differences of the previous values, possesses greater predictive power than any single ratio.
Another model that predicts corporate failure through the use of financial ratios presented by Dambolena and Sarkis (1980). Data was collected on 68 firms, 34 of them failed and 34 of them non- failed firms from Moody's Industrial Manual for the 8 years prior to failed firms and a corresponding 8-year period for each non-failed firm. After applying the Linear Discriminant Analysis (LDA) as statistical technique on sample, conclusion was drawing that 1). The standard deviation of ratios over time is to be the strongest measure of ratio stability, and 2). the ratios of net profits to sales, net profits to total assets, fixed assets to net worth, funded debt to net working capital, total debt to total assets, the standard deviations of inventory to net working capital, and of fixed assets to net worth, are the most important predictors in predicting corporate failure, And 3). The profitability ratios offer a reasonable measure of management effectiveness; the leverage ratios and the stability of the fixed asset to net worth ratio represent historical reasons for corporate failure that are directly related to the excessive or unwise use of leverage. Similarly, Gombola, Haskins, Edward Ketz and Williams (1987) develop a model that identifies the Importance of cash flow ratios in prediction of corporate bankruptcy. Factor Analysis and Linear Discriminant Analysis (LDA) was use as statistical technique on 244 manufacturing or retailing firm that had complete data for at least one of the four years prior to bankrupt. After applied the technique they conclude that CFFO / ASSETS is a most important predictor in bankruptcy prediction. Through the use of new types of model on related topic of Forecasting Bankruptcy More Accurately by Tyler Shumway (2001). He used a new model A Simple Hazard Model and argues that this model is more appropriate than single period models for forecasting bankruptcy. He proposed that a model uses both accounting ratios and market-driven variables to produce out-of-sample forecasts that are more accurate than those of alternative models. After applying the model on variables he conclude that The hazard model is theoretically preferable to the static models used previously because it corrects for period at risk and allows for time-varying covariates. Simple Hazard Model can use all available information to produce bankruptcy probability estimates for all firms at each point in time. It avoids the selection biases inherent in static models. The hazard model is simple to estimate and interpret.
Risk and return can also identify the corporate bankruptcy. Using capital market data another study related to bankruptcy prediction through the use of risk and return structure has done Joseph Aharony, Charles P. Jones, Itzhak Swary (1980). The major purpose of this study was to compare the characteristics of bankrupt and non-bankrupt firms, prior to actual bankruptcy, with respect to various risks and return measures suggested by the capital asset pricing model (CAPM). The sample consists of a group of 45 industrial companies that went bankrupt during 1970-78 and a group of 65 control firms (non-bankrupt firms). Bankrupt companies were required to have at least six years of daily rates of return prior to bankruptcy. He concludes that risk measure based on market data exhibits significantly different behavior between two samples. Both the total variances and standard deviations behave quite different as four years before bankruptcy. Similarly, Dale Morse and Wayne Shaw (1988) analyze the data of those companies who have entered bankruptcy between 1973 and 1982 to examine the risk and return characteristics of the stocks of bankrupt firms before and after the implementation of the 1978 Act. And study how the Bankruptcy Reform Act of 1978 could have changed the investment environment of the bankrupt firm's securities.
Accounting ratios plays an important role in determining the firms’ financial position. It identifies firms’ debt, profitability, liquidity, leverage, activity position of firm with respect to short term and long term prospective. Chen Kung H. and Shimerda (1981) studied the Empirical Analysis of Useful Financial Ratios to identify which ratios should be deleted, and which should be included among the hundreds that have been used by different researchers and can be computed easily from the available financial data, should be analyzed to obtain the information for bankruptcy prediction. After study the variables of Beaver, Altman, Deakin, Edmister, Blum, Elam and Libby, he conclude that N.I/ Sales, N.I/ Common Equity, C.A/ T.A, Fund flow/ Net Worth, Fund flow/ C.L, L.T.Debt/ T.A, T.D/ T.A, Cash/ Sales, Quick Flow and Receivable/ Inventory Ratios have a good ability in the prediction of corporate bankruptcy. Horrigan (1965) analyze the sample of thirty two steel companies and twenty four petroleum companies during the period of 1948-1957 to study the importance of financial ratios, behavior of financial ratios, relationship between ratios, and identification of ratios that predict corporate failure in the year before the declaration of bankruptcy or failure. After applying factor matrix as statistical technique on sample he found that 1) long-term solvency ratios are highly inter-correlated as a group in selected steel and petroleum firms. 2) The collinearity pattern of profit margin has varied between industries. 3) Short term liquidity ratios are highly correlated with each other in selected steel and petroleum firms. 4) Current ratio, N.W to T.D, sales to inventory, sales to F.A and N.I to sales are best predictors in prediction of corporate failure. Another study related to identification of corporate bankruptcy through financial ratios had done by Libby (1975). He studied that whether accounting ratios provide useful information to loan officers in the prediction of business failure or not and empirically derived set of accounting ratios allowed bankers to make highly accurate and reliable predictions of business failure. After applying mathematical modeling, descriptive statistics and factor matrix on 60 firms sample consisted of 30 failed and 30 non-failed firms, he concluded that accounting ratios have an important contribution in prediction of corporate bankruptcy prior 2 years and empirically derived set of accounting ratios allowed bankers to make highly accurate and reliable predictions of business failure. Casey (1980) improved Libbys’ study by selecting of sample span from 1975 to 1975 with the help of descriptive statistics and factor matrix and concludes that loan officers' ability to predict corporate failure accurately based on accounting ratios alone may not be generalizable beyond certain situations. There are several other variables which allows for the simultaneous consideration in the prediction of failure. There are many causes in business failure. To identification of the determinants of failure in the agricultural sector and examine which broad classes of possible explanatory variables are most relevant in answering the question, "Why do farmers fail?" from the period of (1910-1978) has done by Shepard and Collins (1982). After applied of Ordinary least squares Regression Model on sample, they conclude that before World War II leverage and farm size were controlling influences on failure rates. As farms increased in size, producers were better able to survive in the market but increased in debt financing coincided increases the frequency of failure. After the war, higher levels of debt financing were not associated with increased incidence of farm failure. While this may have reflected institutional reforms or, perhaps, higher land values.
Financial structure and the nature of financial risk in Japanese companies differ substantially from those of U.S. firms. Through this assumption, Sadahiko Suzuki and Richard W. Wright (1985) has done research on financial structure and bankruptcy risk in Japan and explored some of the unique interrelationships among companies, banks and government that characterize the financial environment of Japan. Three conditions were identified as central to a foreigner's understanding of financial risk in Japanese companies: the mixed nature of debt and equity claims; the special relationships among companies of the same industrial group; and the quasi-equity nature of main-bank lending. Given these conditions, bankruptcy risk in large Japanese companies is probably much less than traditional Western accounting measures suggest. Empirical testing was performed to determine which types of measures most accurately indicate bankruptcy risk in Japan. Independent variables representing traditional financial accounting ratios, the firm's social importance, and the strength of its main-bank relationship were regressed against two sets of financially troubled Japanese firms. They conclude that the financial accounting variables were not significant in distinguishing between those financially troubled companies which went bankrupt and those that did not. While traditional indicators such as cash flow, falling profits, or increasing debt may help to identify those Japanese companies moving toward financial difficulty, but ratios are not predicting which of them will actually go bankrupt. On the other hand firm’s social importance and the strength of its main bank relationship variables are determining which of the financially troubled companies will actually go bankrupt in Japan.
3- RESEARCH METHODOLOGY
3.1- Sample and Variables
This research is caring out to identify that from liquidity, activity and turnover, profitability and leverage ratios which has a significant contribution in corporate failure and It is possible to predict the corporate failure through the use of liquidity, activity and turnover, profitability and leverage ratios 2 years prior to failed or bankrupted. Data is collected on 26 failed production companies and 26 non-failed production companies 2 years prior to failure from the period of 1996- 2010. All companies in the analysis are selected with the Mean Assets Size of firms 196 million with the range of between 5 million to 750 million from the period of 1996- 2010. All companies are registered in Karachi Stock Exchange. Income Statements and Balance Sheets of all companies are getting from Karachi Stock Exchange. "Simple Random Sample" technique is using in this research. Sample is selecting in such a way that all companies has an equal probability of being selected in the sample. Secondary data is using in carrying out this research. However, the analysis result and conclusion of the research is totally based on calculated financial ratios that are calculated through financial statements.
1) Liquidity Ratios
X1= Current Ratio
X2= Quick Ratio
2) Activity and Turnover Ratios
X3= Receivable Turnover
X4= Total Assets Turnover
3) Profitability Ratios
X5= Net Profit Margin
X6= Return on Equity
X8= Return on Assets
4) Leverage Ratios
X9= Times Interest Earning
X10= Debt to Equity
(Bankruptcy = (1) Company delisted by Karachi Stock Exchange (KSE) due to Liquidation / winding up of a company under court order i.e. violation of KSE listing regulation no. 32 (1) (d). Or (2) Winding up of a company by Securities and Exchange Commission of Pakistan (SECP)).
H1: Liquidity ratios have a contribution in corporate bankruptcy.
H2: Activity and Turnover ratios have a contribution in corporate bankruptcy.
H3: Profitability ratios have a contribution in corporate bankruptcy.
H4: Leverage ratios have a contribution in corporate bankruptcy.
H5: Liquidity ratios, Activity and Turnover ratios, Profitability ratios, Leverage ratios can identifies bankrupted and non-bankrupted corporations priori 2 years.
3.3- Multiple Discriminant Analysis (MDA) Approach
Multiple Discriminant Analysis (MDA) has been used in this research report as a statistical technique to test the hypothesis and to meet the objective of this research report. Multiple Discriminant analysis is the appropriate statistical technique used when dependent variable is a categorical and independent variables are metric variables. Multiple Discriminant analysis is use to distinguish between innovators from non-innovators according to their demographic and psychographics profiles. Other applications include distinguishing heavy product users from light users, male from female, national brand buyers from private label buyers, good credit risks from poor credit risks, and bankrupted firms from non-bankrupted firms. Discriminant function is derived from an equation. It takes the following form,
Z = ß₁ X₁ + ß₂ X₂ + ß₃ X₃ ……………. ßn Xn
Where Z is the overall index, ß₁, ß₂, ß₃… ßn are discriminant coefficients, X₁, X₂, X₃…. Xn are independent variables. The discriminant Score (Z) is taken to estimate the bankruptcy character of the company. Lower the value of Z, greater is the firm‘s bankruptcy probability. Discriminant analysis has been applied to various decision making processes which involve classification of individuals into two or more groups. The underlying concept of multiple discriminant analysis is that individual cases may be separated into two or more groups based on an analysis of their characteristics. Although MDA approach has been frequently used due to its high predictive ability, it has certain limitations.
The dependent variable (Z) is the discriminant score that forecast the bankruptcy probability of the company. This variable takes the value “0” or “1” for any firm observation. In this study, value “0” denotes to non-bankrupt firms and value “1” denotes to bankrupt firms while estimating the model.
4- DATA ANALYSIS AND FINDINGS
Through the use of SPSS soft ware version 17, All the Ten financial variables grouped under the leverage, liquidity, profitability and turnover ratios were examined for bankrupt and non-bankrupt companies by employing stepwise discriminant analysis to derive the discriminant variables with their coefficients and tested the accuracy and significance of developed model.
After Appling the model,
4.1- Case processing summary
Insert Table # 4.1.1 Here
Table # 4.1.1 (Analysis Case Processing Summary table) shows that from the total of 52 observations, valid observations are 50, and 2 observations are unselected. In percentage, 96.2% is valid observations and 3.8% is unselected.
4.2- Mean and Standard Deviation of Bankrupt and Non-Bankrupt Companies
Insert Table # 4.2.1 Here
Table # 4.2.1 (Group statistics table) shows that the Mean and Standard deviation of debt to equity ratio of bankrupt firms is higher that the non-bankrupt firms, Mean Times interest earned of bankrupt is negative as compare to non-bankrupt firms and standard deviation is also high of bankrupt firms, Mean return on asset, return on equity and profit margin of bankrupt firms is also negative and high standard deviation as compare to non-bankrupt firms, Mean total asset turnover and receivable turnover of bankrupt firms is also less than the non bankrupt firms, and Mean current and quick ratio of bankrupt firms is also lower than the non-bankrupt firms. This table shows the evident that bankrupt firms have higher indebtness, lower liquidity, and poor profitability and turnover as compare to non-bankrupt firms.
4.3- Test of Equality of Group Means
Insert Table # 4.3.1 Here
Table # 4.3.1 (Test of equality of group means table) is use to determine whether 10 financial ratios of two groups (bankrupt and non-bankrupt) are likely to have the same mean and F-statistics are used to check for any significant differences between the two groups mean. According to the result in this table shows that profit margin and debt to equity ratio have a less wilks lambda value and highest F value and less sig value among the other variables that show these are the significant variables in analysis.
4.4- Statistical Results of Multiple Discriminant Analysis (MDA)
TABLE # 4.4.1
Debt to Equity
Return on Asset
At each step, the variable that minimizes the overall Wilks' Lambda is entered.
a. Maximum number of steps is 18.
b. Minimum partial F to enter is 3.84.
c. Maximum partial F to remove is 2.71.
d. F level, tolerance, or VIN insufficient for further computation.
The Discriminant analysis procedure concludes the significant variables and excludes the insignificant variables for further analysis. Table # 4.4.1 (Variable entered/removed table) shows that from 10 financial variables, only three variables profit margin, debt to equity and return on asset are highly significant at 5% significance level in the analysis. Among these three variables, debt to equity ratio discriminated the most with sig value 0.000. That result shows that our H1 Hypothesis is rejected and H2, H3 and H4 are accepted.
TABLE # 4.4.2
Standardized Canonical Discriminant Function Coefficients
Return on Asset
Debt to Equity
Table # 4.4.2 (Standardized canonical discriminant function coefficients table) has determined and ranked the discriminant coefficients accordingly shown in standardized canonical discriminant function coefficients table. Profit margin discriminated the most with the highest discriminant magnitude 0.545 followed by return on asset ratio with 0.518 and debt to equity ratio with -0.780 that discriminating the least.
TABLE # 4.4.3
Functions at Group Centroids
Status of Firm
Table # 4.4.3 (Group centroids function table) determines optimum Z value based on which a firm is classified as bankrupt and non-bankrupt. Result in function at group centriods Table reveals that if a firm having Z score equals to -0.729 is classified as Bankrupt, whereas firm having Z score equal to 0.673 is classified as Non-bankrupt.
4.5- Z Score / MDA MODEL
The final Z score / MDA Model is derived from the function at group centriods table, Standardized canonical discriminant function coefficients as fellow,
Z = 0.545X₁ + 0.518X₂ - 0.780X₃
Z = Discriminant Score
X₁ = Profit Margin
X₂ = Return on Asset
X₃ = Debt to Equity
The midpoint or cut off point of bankrupt and non-bankrupt firms is zero that suggests that when the firms Z-value approaches to above the zero than it will towards non- bankrupt and whereas the firms Z-value approaches to below the zero than it will towards bankruptcy. At last, the firm having a Z-value = -0.729 classified as “Bankrupt” and the firm having a Z value = 0.673 classified as “Non-Bankrupt”.
4.6.1- Classification Results
TABLE # 4.6.1
Status of Firm
Predicted Group Membership
Cases Not Selected
a. 82.0% of selected original grouped cases correctly classified.
b. 100.0% of unselected original grouped cases correctly classified.
Table # 4.6.1 (Classification Results table) shows that from 26 Selected Cases, 24 we have correctly identified means we says that they are Non-Bankrupted firms and originally they are Non-Bankrupt firms, And 2 we have not correctly identified means we says that these firms are Bankrupted but originally these are Non-Bankrupted firms.
From 24 selected cases, 17 we have correctly identified means we says that they are Bankrupted firms and originally they are Bankrupt firms, And 7 we have not correctly identified means we says that these firms are Non-Bankrupted but originally these are Bankrupted firms.
Cases not Selected
Classification Results table shows that from 2 not Selected Cases, all we have correctly identified means we says that they are Bankrupted firms and originally they are Bankrupt firms.
Overall the classification result is,
82% we have correctly classified from original selected cases, and 100% we have correctly classified from original not selected cases.
4.7- Wilks’ Lambda of the Estimated MDA Model
TABLE # 4.7.1
Test of Function(s)
Table # 4.7.1 (Wilks Lambda Table) evaluates the overall discriminant function fitness. The value of 0.662 of Wilks Lambda at 99% of significant confidence level provides the evidence that our discriminant model has the potential to be applied practically.
5- CONCLUSION AND RECOMMANDATION
The main objective of this research is to define the importance of ratio analysis in evaluation of firms financial position and performance, second, To identify that which ratios has a significant role in the prediction of corporate failure, and third, It is possible to predict the corporate failure through the use of financial ratios 2 years prior to failed or bankrupted. Theoretically, literature and introduction proved that the importance of ratio analysis in evaluation of firms financial position performance in this research.
In this research paper we identify the financial ratios that are most significant in bankruptcy prediction for the non-financial sector of Pakistan using a sample of companies which became bankrupt over the 1996-2010. In doing so, Ten financial ratios covering four important financial attributes namely liquidity, activity and turnover, profitability, and leverage ratios has examined for a two-year prior bankruptcy. Multiple Discriminant Analysis (MDA) has used as statistical technique with the help of SPSS 17.0 Version on a sample of twenty six bankrupt and twenty six non-bankrupt firms two year prior bankrupted with the Asset range of 5 million to 750 million from the period of 1996-2010. The application of statistical techniques, particularly Multiple Disciminant Analysis (MDA), can be used to predict business failure from accounting data as far as two years in advance with a fairly high accuracy. The discriminant analysis model produced that Profit Margin, Debt to Equity ratio, and Return on Assets has a significant contribution in prediction of corporate bankruptcy. Our estimates provide the evidence that the firms having Z value below zero fall into “BANKRUPT” whereas the firms having Z value above the zero fall into “NON-BANKRUPT” category. Our model achieved 82% prediction accuracy from original selected cases and 100% prediction accuracy from original not selected cases when it is applied to forecast bankruptcies on the underlying sample.
In addition to estimating bankruptcy prediction model for Pakistani companies, the study shows that most of the companies that went bankrupt during the period from 1996 to 2010 have shown signs of financial distress i.e., poor financial performance, higher indebtness, lower liquidity, and poor profitability and turnover. Further, our study has explored three significant financial variables namely Profit Margin, Debt to Equity, and Return on Asset that can be used to explore the bankruptcy risk in Pakistan and corporate bankruptcy in Pakistan is dependent on these three significant financial variables. In aggregate, we suggest that the regulatory authorities in Pakistan should keep these three significant financial variables in monitoring/assessing the financial health of the firm, and investors and bank loan officers should review these variables before taking any decision of investment or decision of loan giving.
Altman. Edward I (1968); “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy” ; The Journal of Finance, Vol. 23, No. 4, pp. 589-609.
Altman. Edward I (1973); “Predicting Railroad Bankruptcies in America” ; The Bell Journal of Economics and Management Science, Vol. 4, No. 1, pp. 184-211.
Altman. Edward I and Lorris Bettina (1976); “Financial Early Warning System for Over-the-Counter Broker-Dealers” ; The Journal of Finance, Vol. 31, No. 4, pp. 1201-1217.
Amy Hing-Ling Lau (1987); “A Five-State Financial Distress Prediction Model” ; Journal of Accounting Research, Vol. 25, No. 1, pp. 127-138.
Beaver. William H (1968); “Market Prices, Financial Ratios, and the Prediction of Failure” ; Journal of Accounting Research, Vol. 6, No. 2, pp. 179-192.
Beaver. William H (1966); “Financial Ratios As Predictors of Failure” ; Journal of Accounting Research, Vol. 4, Empirical Research in Accounting: Selected Studies 1966, pp. 71-111.
Casey. Cornelius J, Jr (1980); “The Usefulness of Accounting Ratios for Subjects' Predictions of Corporate Failure: Replication and Extensions” ; Journal of Accounting Research, Vol. 18, No. 2, pp. 603-613.
Chen Kung H. and Shimerda. Thomas A (1981); “An Empirical Analysis of Useful Financial Ratios” ; Financial Management, Vol. 10, No. 1 (Spring, 1981), pp. 51-60
Dambolena. Ismael G and Khoury. Sarkis J (1980); “Ratio Stability and Corporate Failure” ; The Journal of Finance, Vol. 35, No. 4, pp. 1017-1026.
Dale Morse and Wayne Shaw (1988); “Investing in Bankrupt Firms” ; The Journal of Finance, Vol. 43, No. 5, pp. 1193-1206.
Deakin. Edward B (1972); “A Discriminant Analysis of Predictors of Business Failure” ; Journal of Accounting Research, Vol. 10, No. 1, pp. 167-179.
Eisenbeis. Robert A (1977); “Pitfalls in the Application of Discriminant Analysis in Business, Finance, and Economics” ; The Journal of Finance, Vol. 32, No. 3, pp. 875-900.
Gombola. Michael J, Haskins. Mark E, Edward Ketz and Williams. David D (1987); “Cash Flow in Bankruptcy Prediction” ; Financial Management, Vol. 16, No. 4, pp. 55-65.
Horrigan. James O (1965); “Some Empirical Bases of Financial Ratio Analysis” ; The Accounting Review, Vol. 40, No. 3 (Jul., 1965), pp. 558-568.
Joseph Aharony, Jones. Charles P, Itzhak Swary (1980); “An Analysis of Risk and Return Characteristics of Corporate Bankruptcy Using Capital Market Data” ; The Journal of Finance, Vol. 35, No. 4, pp. 1001-1016.
Marc Blum (1974); “Failing Company Discriminant Analysis” ; Journal of Accounting Research, Vol. 12, No. 1, pp. 1-25.
Ohlson. James A (1980); “Financial Ratios and the Probabilistic Prediction of Bankruptcy” ; Journal of Accounting Research, Vol. 18, No. 1, pp. 109-131.
Robert Libby (1975); “Accounting Ratios and the Prediction of Failure: Some Behavioral Evidence” ; Journal of Accounting Research, Vol. 13, No. 1, pp. 150-161.
Sadahiko Suzuki and Wright. Richard W (1985); “Financial Structure and Bankruptcy Risk in Japanese Companies” ; Journal of International Business Studies, Vol. 16, No. 1, pp. 97-110.
Shepard, Lawrence E. and Collins, Robert A (1982); “Financial Structure and Bankruptcy Risk in Japanese Companies” ; Journal of International Business Studies, Vol. 16, No. 1 (Spring, 1985), pp. 97-110.
Tyler Shumway (2001); “Forecasting Bankruptcy More Accurately: A Simple Hazard Model” ; The Journal of Business, Vol. 74, No. 1, pp. 101-124.