Predicting Financial Distress and Corporate Failure

The financial failure of a company can have a devastating effect on all seven users of financial statements e.g. present and potential investors, customers, creditors, employees, lenders, the 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. Users of financial statements can predict the financial position of an organization using the Altman Z score model, Argenti A score model, and by looking at the financial statements i.e. balance sheet, income statements, and cash flow statements. 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 exceed the total assets of a company, as total assets are considered a measure of the productivity of a company’s assets. This article looks at the pros and cons of models in predicting corporate failures in order to measure the financial position of the company.

Predicting Financial Distress and Corporate Failure

Reasons for business failure is identified as i.e. 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 are a good indicator to measure the performance but sometimes, it is hard to achieve the required result due to different accounting policies, resulting in difficulty to analyze 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.

Business failures can be predicted by approaches like ‘Z’ score and ‘A’ score models, using a number of financial variables. Z score is defined as the product of a quantitative model that uses a blend of traditional financial ratios and a statistical technique is known as MDA. Altman (1968) used multiple discriminant analysis (MDA) in the effort to find a bankruptcy prediction model. Altman combined five ratios to produce Z score. Companies with a ‘Z’ score of 2.7 or more indicated as non-failure or a going concern and firms with a Z score of 1.8 or less indicated as a failure. ‘Z’ score is between a grey area. Altman’s Z score is found to be about 90% accurate in forecasting bankruptcy one year in the future and about 80% accurate in forecasting in two years in the future. Resultantly, the 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 ‘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 increasing attention, due to the fact that if the business fails the investors, employees, lenders, creditors, etc. may all suffer the loss. The Z score analysis can be employed to rise above some of the limitations of traditional ratio analysis as it assesses corporate stability and more significantly predicts potential cases of corporate failures.

However, Altman ‘Z’ score model also has some disadvantages. The ‘Z’ score model is based on historical financial data, which is a big problem in making economic decisions making because some of the present circumstances can be different from the past. Also, some of the accounting policies used by companies make 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. Also, there is lack of conceptual base in Z score model and lack of sensitivity to time scale of failure i.e. time factors may not be fully taken into account. Another limitation of the Z score model is that it does not provide the theory to explain bankruptcy, it only checks the financial position of the company and not the fact that how to recover from this financial distress.

Argenti ‘A’ score model is also a well-known approach for predicting corporate failures use by various users of financial statements. The identification of potential failures can be done through a qualitative approach, usually examines the non-financial variables such as type of management, the number of active shareholders, the availability of effective accounting information systems, and also the levels of gearing in different economic situations.

Argenti developed a model to predict the likelihood of company failure. This model is based on calculating scores for a company based on three-stage events i.e. defects of the company, management mistakes, and the symptoms of failure. In calculating company ‘A’ score, different scores are allocated to each defect, mistake, and symptom according to their importance. The defect exists in the organization’s top management which rises due to accounting systems and wrong decisions. Management fault can lead to company failure which is highly geared, over trading, etc. Due to these defects and mistakes, symptoms of business failure will start to rise. Various symptoms include high staff turnover, delayed management decisions, etc. If a company achieves an overall score of over 25 or a defect score of over 10, or a mistake score of over 15, then the company is showing classic signs leading up to failure. However, a business is understood to be a going concern if the overall score of the company mistakes and defects below 18. ‘A’ score model is the best tool to analyze the management performance and non-financial procedures to predict corporate failures.

There are also some limitations of Argenti’s ‘A’ score model. The financial health of an organization cannot be explained by specific financial indicators e.g. liquidity, return on investment, profit, etc. The existence of management errors in different failure paths is also not totally clear, resulting in little differences between them. There is also no proper rule to calculate the points of defects, mistakes, and symptoms that give a rise to situations the ‘A’ score model is complex but the ‘Z’ score model provides an exact figure to predict the corporate failures.

In conclusion, this article looks at different approaches i.e. ‘Z’ score and ‘A’ score to predict companies failures and their pro and cons in relation to economic decision making. Users of financial statements rely on ‘true and fair view’ of these statements, so they can get an idea of the financial position of a company because of the fact that investors are interested in their returns plus dividend, employees are interested because of the job security and bonuses etc. The traditional ratio analysis is an excellent indicator but it cannot make all decisions single handily. ‘Z’ score model is based on ratios, which are based on accounting information. ‘Z’ score model reduces the risk for the investors, creditors, customers, lenders, etc. and enables the management of the company to increase profit levels, productivity, and shareholders wealth. Altman ‘Z’ score model is the best approach to predict corporate failure because it gives an exact benchmark for decision making. However, publishing a poor ‘Z’ score of a company can also have a devastating effect on the business itself as investors might withdraw the investment in the business which might result in its financial collapse of the company. Argenti ‘A’ score model is a good approach to measure the manager’s performance that shows the success or failure of a company. Corporate failures are common in a competitive business environment where only the fittest company has a guarantee to survive in the market discipline.

The financial distress on a company and its management can have an intense effect on how the firm behaves and how its investors, suppliers, and customers see it. When a company is in financial distress, suppliers are reluctant to extend credit and customers are concerned about future supplies, warranties, and after-sales services. If a company has the support of its shareholders, then the company has more chances to survive especially in this subprime mortgage crisis and credit crunch era. Both the qualitative and quantitative information is important in identifying financially distressed firms e.g. the financial information, share price, bank debts which also are the important distressed signals for potential failures. Predicting variables other than financial ratios may prove beneficial for the company e.g. management skills & experience and other behavioral aspects that have an impact on the day-to-day running of the firm which could be significant in a bankruptcy prediction model.

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