The financial failure of a company can have a devastating effect on the all seven users of financial statements e.g. present and potential investors, customers, creditors, employees, lenders, general public etc. As a result, users of financial statements as indicated previously are interested in predicting not only whether a company will fail, but also when it will fail e.g. to avoid high profile corporate failures at Enron, Arthur Anderson, and WorldCom etc. Business failure is defined as the unfortunate circumstance of a firm’s inability to stay in the business. Business failure occurs when the total liabilities exceeds the total assets of a company, as total assets is consider a measure of productivity of a company assets. The main reasons for business failure are high interest rates, recession squeezed profits, heavy debt burdens, government regulations and the nature of operations can contribute to a firm’s financial distress. The traditional analysis of financial ratios has been widely used in disclosing of operative and financial difficulties of an organization. Traditional ratio analysis allows the users of financial statements to understand the firm’s performance when placed in environment e.g. the firm’s previous performance, existing economic climate etc. However, the ratio analyses is a good indicator to measure the performance but sometimes, it is hard to achieve the required result due to different accounting policies, resulting in difficult to analyse the company performance based on only an individual ratio. Liquidity or working capital ratios are the foundation for analysis of potential corporate failure, which is significant to investors as they wish to know whether additional funds could be loaned to the company with reasonable safety and whether the business is able to return back the interest and the principal itself.
It is therefore essential that new approach to assessing financial statement must be devised and changed to suit to new market conditions. One such technique was developed by Professor Altman who devised a new technique popularly known as the Z-Score. The Altman Z-Score is a statistical model that incorporates the use of five different ratios which serve to predict the health of a firm. The Altman Z-Score is used to predict bankruptcy of the business using traditional financial ratios and statistical method known as the Multiple Discriminant Analysis. The Z-score is considered to be 90 % accurate in forecasting business failure one year into the future and 80 percent accurate in forecasting it two years into the future.
By selecting various financial ratios and applying weight to each ratio it is possible to estimate the financial position of the company. In his study, Professor Altman analysed over 22 such financial ratios and selected 5 distinctive ratios that focused on the balance sheet and performance ratios. Weights were assigned by establishing appropriate coefficients to show how each of these ratios influenced the dependent Z-Score.
Altman Z-Score equation: Z = X1 (a) + X2 (b) + X3 (c) + X4 (d) + X5 (e)
- Z – Overall Z-Score.
- a, b, c, d, e are respective coefficient assigned to each ratios (X1,X2 ¦ ¦..X5).
- X1 – Working Capital/Total Assets : Working capital is a company’s current assets less its current liabilities and measures a company’s efficiency and its short-term financial health. Positive working capital means that the company is able to meet its short-term obligations. Negative working capital means that a company’s current assets cannot meet its short-term liabilities; it could have problems paying back creditors in the short term, ultimately forcing it into bankruptcy.
- X2 – Retained Earning/Total Assets : The retained earnings of a company are the percentage of net earnings not paid out as dividends; they are “retained” to be reinvested in the firm or used to pay down debt. The ratio of retained earnings to total assets helps measure the extent to which a company relies on debt, or leverage. The lower the ratio, the more a company is funding assets by borrowing instead of through retained earnings which, again, increases the risk of bankruptcy if the firm cannot meet its debt obligations.
- X3 – EBIT/Total Assets : This is a variation on return on assets, which is net income divided by total assets. This ratio assesses a firm’s ability to generate profits from its assets before deducting interest and taxes.
- X4 – Market Value Equity/Book Value of Total Debt : The ratio of market value of equity to total liabilities shows how much a company’s market value (as measured by market capitalization, or share price times shares outstanding) could decline before liabilities exceeded assets. Unlike the other ratio components used by the Z-Score, market value isn’t based purely on fundamentals – the market capitalization of a firm is an indication of the market’s confidence in a company’s financial position. Generally speaking, the higher the market capitalization of a company, the higher the likelihood that the firm can survive going forward.
- X5 – Sales/Total Assets : The ratio of sales to total assets, more commonly referred to as asset turnover, measures the amount of sales generated by a company for every dollar’s worth of its assets. In other words, asset turnover is an indication of how efficiently a company is as using its assets to generate sales. The higher the number the better, while low or falling asset turnover can signal a failure by the company to expand its market share.
Now, companies expand their reach in the global market by producing goods that belong to different markets. Firms engage in horizontal and vertical integration to expand their market and also to reduce risk by spreading their resources in different activities. It is therefore illogical to do a financial comparison of firms from different industry. Professor Altman overcame the problem of comparing companies that specialized in different industries by altering the above equation by eliminating certain ratios. For example, Ratio X5 for non-manufacturing companies. This is done because sales/total assets ratio greatly varies from industry to industry.
Z = X1 (a) + X2 (b) + X3 (c) + X4 (d)
He modified the equation for a privately held company by implementing book value of equity as a private company’s stocks are not publicly traded. He further devised Z-Score table to determine a financial healthy company from a sick company in various industries.When analyzing the Z-Score of a company, the lower the value, the higher the odds that the company is headed toward bankruptcy. Altman came up with the following rules for interpreting a firm’s Z-Score:
- Below 1.8 indicates a firm is headed for bankruptcy;
- Above 3.0 indicates a firm is unlikely to enter bankruptcy; and
- Between 1.8 and 3.0 is a statistical “gray area.”
The Altman Z-Score technique is gaining popularity in the financial world as an efficient and accurate method to predict financial health of a company and also it is less likely to be manipulated as Ratio Analysis are known to be. Altman Z-score model is useful for the management of the company to improve the potential ability and also helps the users of the financial statements to make essential economic decisions.
The users of financial statements use Altman Z-Score model in order to assess the financial position of the company e.g. shareholders of a firm may use Z-score to provide an early warning signal of failure i.e. to evaluate the degree of risk attached to the investment. Customers of the company may be interested in the future supplies of the product and services. If the Z-score is negative, it shows that the business is at risk and customers might opt for alternative products. In the last decade, the usefulness of financial ratios for decision making has been paid increasingly attention, due to the fact that if the business fails the investors, employees, lenders, creditors etc. may all suffer the loss. The Altman Z-Score analysis can be employed to rise above some of the limitations of traditional ratio analysis as it assess corporate stability and more significantly predicts potential case of corporate failures.
However, Altman Z-Score model also have some disadvantages. The Z-score model is based on the historical financial data, which is a big problem in making economic decision making because some of the present circumstances can be different from the past. Also, some of the accounting policies used by companies which makes it difficult to get the required result from the Altman Z-score model. In other words, we can say that corporate failure models relate to the past i.e. without taking into account the current state of the macroeconomic environment e.g. the level of inflation, interest rates etc. The publication of accounting data by companies is subject to a delay, failure might occur before the data becomes available. These failure models share the limitations of the accounting model including the accounting concepts and conventions on which they are based. Another limitation of the Altman Z-Score is the use of historical data. There is lack of conceptual base in Altman Z-score model and lack of sensitivity to time scale of failure i.e. time factors may not be fully taken into account. Other limitation of Altman Z-score model is that it does not provides the theory to explain bankruptcy, it only check the financial position of the company and not the fact that how to recover from this financial distress.