Global Journal of Management and Business Research, A: Administration and Management, Volume 22 Issue 8

As early as 1970 Edmister (1970) cited the following ratios as significant predictors of business failure: 1. Current ratio (current assets to current liabilities); 2. Net working capital to total assets; 3. Debt to total assets; 4. Total assets turnover (sales to total assets); 5. Net sales to net working capital; 6. Net operating margin (net working capital to total assets); 7. Earnings after tax to total assets; 8. Market value of equity to book value of total debt; 9. Cash flow to total debt; 10. Trend breaks of net quick assets to inventory; 11. Net quick assets to inventory; and 12. Rate of return to common shareholders. Of the twelve ratios, Edmister notes that five (iii, ix, x, xi, xii) are generally the best indicators of failure. Daya (1977) in his study also mentions three of these five. Three of the five ratios used by Altman (2000) in the Z-score are also included in the twelve variables mentioned by Edmister. Other popular ratios mentioned in the literature as failure predictors are: • Retained earnings to total assets; • Profit after tax (PAT) to total assets; • Shareholders’ funds to total assets; • Turnover to total assets; • Operating profit to operating assets; • Inventory to sales; • Quick assets to current liabilities; • Receivables to inventory; and • Equity/total capital. Vallely (2008) states that with regard to liquidity difficulties, the most important indicators include liquidity/solvency ratios, particularly the current and quick ratios. A consideration of how these ratios change over time and how they relate to the recommended averages may indicate whether or not a liquidity problem and potential corporate collapse are looming. Over the years, to overcome the shortcomings of financial ratio analysis, some authors (Altman, 1968 and Edmister, 1970) have suggested the grouping of similar ratios to develop meaningful bankruptcy prediction models. d) Bankruptcy prediction models Mosalakae (2007) defines a bankruptcy prediction model as a tool that can be used to assess whether or not a firm will be able to continue its operations. These models feature among the tools available for measuring financial performance. A number of researchers have tried to predict company failure based on the company’s financial ratios, and ratios have been used to develop bankruptcy prediction models for this purpose. Examples of bankruptcy prediction models are Altman’s Z-score and the ZETA credit models. The reason for singling these two out is that the Z-score is widely used and the ZETA credit risk model has a high prediction accuracy up to five years prior to failure. Daya (1977) reports that Altman discusses three generic terms which are often used to describe “corporate problems”, these being failure, insolvency, and bankruptcy. He describes failure as represented by the situation where the realised rate of return on invested capital, with allowances for risk considerations, is significantly and continually lower than prevailing rates on similar investments. The state of insolvency exists when a firm cannot meet its current obligations, signifying a lack of liquidity. Bankruptcy can be of two types: the state of insolvency, and the declaration of bankruptcy in court accompanied by a petition to either liquidate the entity’s assets or attempt a recovery programme. i. Altman’s Z-score model Professor Altman developed the Z-score more than 40 years ago, and it is still widely used today. He researched 66 companies in the United States that experienced corporate failure between 1946 and 1965 to determine whether or not their failure could have been predicted. The model is used by investors and analysts to assess the financial risk associated with potential investments. In developing his models, Altman chose multiple discriminant analysis (MDA). This technique has been utilised in a variety of disciplines since its first application in the 1930s. During those earlier years, MDA was used mainly in the biological and behavioural sciences. In recent years, this technique has become increasingly popular in the practical business world and in the academic environment. MDA is a statistical technique used to classify an observation into one of several a priori groupings on the basis of the individual characteristics of the observation. It is used primarily to classify and/or make predictions in problems where the dependent variable appears in qualitative form, for example, male or female, bankrupt or non-bankrupt. ii. The ZETA credit model (1977) In 1977, a second-generation model with several enhancements to the original Z-score approach was constructed. The purpose was to “construct, analyze and test a new bankruptcy classification model which considers explicitly recent developments with respect to business failures” (Altman, 2000). The new study also incorporated refinements in the utilisation of discriminant statistical techniques. The new ZETA model for bankruptcy classification appears to be quite accurate for up to five years prior to failure, with successful classification of well over 90% of the sample one year prior to failure and 70% accuracy up to five years prior to failure. It is also observed that the inclusion of retailing firms in the same model as manufacturers does not seem to affect the results negatively. This is probably due to the adjustments to the data based on recent and anticipated financial Financial Performance Measurement of Manufacturing Small and Medium Enterprises in Pretoria, South Africa: A Multiple Exploratory Case Study 35 Global Journal of Management and Business Research Volume XXII Issue VIII Version I Year 2022 ( ) A © 2022 Global Journals