Importance of Ratio Analysis in Evaluation of Firms
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Published: Wed, 14 Mar 2018
This study aims to define the importance of ratio analysis in evaluation of firms’ financial position and performance, second, to identify that which ratios have 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 that’s covering four important attributes of financial prospective 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 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 estimation provide the evidence that the firm fall into “BANKRUPT” category if firms having Z value below zero whereas the firm fall into “NON-BANKRUPT” category if the firms having Z value above the zero. Our model achieved 82% prediction accuracy from original selected cases and 100% prediction accuracy from original not selected cases when it is applied on the underlying sample to forecast bankruptcies.
Financial ratios have played an important part in evaluating the performance and financial condition / position of any firm. It helps to define the true picture of the firm with respect to strength or weaknesses and survival position of the firm and helps in forecasting the future of the firm and thereby enabling the decision makers to take different operational decisions of the firm and take corrective actions for the betterment of the firm. Ratio analysis doesn’t mean just comparing different numbers or figures from financial statement like cash flow statement, income statement, and the balance sheet. It’s a comparison of current numbers or figures with previous years, with other companies, with the industry, or even against the economy in general. Ratios define the relationships between individual values and relate these values with previous values that how a company had 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 and analyze the financial performance and survival position of a firms or enterprises. 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). Due to a particular interest which is related to this field has shown in two unpublished research papers by White and Turnbull (1975a; 1975b) and a paper by Santomero and Vinso (1977) which logically and systematically develop probabilistic estimates of failure. The present study is similar to the latter studies/ research papers.
As a tool of financial management, ratios are more important. It defines facts on a comparative basis & enables us to draw a true picture of a firm. Ratio analysis allows to analyst to asses, analyze and evaluate the performance of a firm in respect to the following different aspects:
1] Overall profitability
2] Liquidity position
3] Operating efficiency
4] Long-term solvency
5] Trend analysis
6] Inter firm comparison
7] Growth Trend/ Growth Position
1.1- Advantages of Ratio Analysis
Financial ratios are based on accounting data which identify the significant accounting data relationships, which give the insight financial information and performance of a company to decision-maker. The advantages and usefulness of financial ratio analysis are as follows:
Ratios help in identification of trend analysis, which is useful in forecasting future and decision making process.
Analysis of financial ratios helps in assessing and evaluating of firms / enterprises profitability, liquidity, operations, and leverage that defines the true performance picture of an enterprises / firm.
Analysis of financial Ratios provides a basis for both types of comparisons and evaluations, intra-firm as well as inter-firm comparisons and evaluation.
Analysis and comparison of financial ratios of current year with previous year or standard ratios helps the management and decision makers to analyze and evaluate 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
Conducting ratio analysis can be a good and useful for a firm but it has some limitations that are described below:
1) Information problems
Ratios require quantitative information of firm for analysis but it is not defines about analytical output of firm.
The figures used in ratios are extracted from accounting data and accounts are mostly to be several months out dated, and so it might not present a proper indication and true picture of the company’s current financial position and performance.
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. So before conducting any comparison of performance over time, there is needed to focus on changes in price effect.
Technology is also changing rapidly, when comparing performance over time, there is needed to consider technological changes before conducting any comparison. Changes in technology should be in line with any movement in performance.
Any Change in accounting policies can also reflect and affect the results of performance comparison between different accounting years that could be misleading. So there is also need to consider the changes in accounting policies.
3) Inter-firm comparison
Different Companies 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, it will be a mislead or wrong comparison of performance of two enterprises.
Inter-firm comparison is only useful when the firms compared are the same age same size, application of similar production process / methods, and same accounting process methods and practices. Otherwise it may not be a good analysis.
Ratios only provide quantitative information of firms and not qualitative information of firms.
Ratios calculation is based on past financial statements. They do not indicate or identify any future trends and future 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 word is defined as the inability of firm/enterprises to pay its financial obligations on their maturity period. Operationally, failure of a firm is depend on different factors. When any of the following events have occurred a firm is said to have as a failed firm, like bankruptcy, default on bond, an overdrawn bank account, or inability of payment 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).
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.
CHAPTER 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 through the use of some empirical results as evidenced by the event of bankruptcy. 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 T.A (total assets), F.A (fixed assets) to N.W (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 F.A (fixed asset) to N.W (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 and reliable in forecasting bankruptcy than single period models for forecasting bankruptcy. He proposed that hazard model can forecast more accurate prediction through the use of both accounting ratios and market-driven variables on sample 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.
CHAPTER 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 have 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. Initially, data was collected on 40 failed production companies and 40 non-failed production companies 2 years prior to failure from the period of 1996-2010. But due to missing data of some companies, finally 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. Sample is selecting in such a way that all companies has an equal probability of being selected in the sample from above mentioned Assets range. 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
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 identify bankrupted and non-bankrupted corporations priori 2 years.
3.3- Multiple Discriminant Analysis (MDA) Approach
Multiple Discriminant Analysis (MDA) is used in this study as a statistical technique to test the hypothesis and to meet the objective of this thesis. Multiple Discriminant Analysis (MDA) is the appropriate statistical tool and technique used when dependent variable is a categorical and independent variables are metric variables. Multiple Discriminant analysis is used 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 = ßâ‚ Xâ‚ + ßâ‚‚ Xâ‚‚ + ßâ‚ƒ Xâ‚ƒ ……………. ßn Xn
Where Z is the overall index, ßâ‚, ßâ‚‚, ßâ‚ƒ… ßn are discriminant coefficients, Xâ‚, Xâ‚‚, Xâ‚ƒ…. 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 pre
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