Predicting Bankruptcy in Organizations

Bankruptcy is a state in which firms are not able to meet their obligations to internal as well as external stakeholders. It is for this reason that bankruptcy prediction is of utmost importance. Stakeholders like employees, suppliers, customers, etc. could gain a lot if they had a method for predicting the likelihood of a company going bankrupt.

Over the course of time, various models have been developed in order to identify and predict bankruptcy within organizations. However, these models have a lot of limitations. Some of these models are not applicable to all industries, whereas there are some others that do not provide consistent results. However, there are a couple of models that are widely used across various industries.

In this article, we will review some of the important models which are used to predict bankruptcy.

Altman Z - Model

The ratio analysis has always been considered to be a tool that has helped in the early identification of companies which face the threat of bankruptcy. The Altman Z score is a specific format of ratio analysis. This method was developed by Professor Altman. The Altman model is basically a combination of five ratios. Each of these ratios is given a weighted coefficient. The final score of this ratio is then derived.

Professor Altman has provided predefined thresholds. If the final score falls below the given threshold, then there is a high chance of bankruptcy. On the other hand, if the final score falls above the threshold, then the firm can be considered to be safe. This model was developed by Professor Altman after collecting empirical evidence. Data related to bankruptcies and defaults over-collected for many years. The ratios involved were analyzed under different circumstances until finally, the ratios were isolated.

The five ratios used by the Altman model are as follows:

• Current Assets – Current Liabilities/Assets
• Retained Profits/Assets
• EBIT/total assets
• Market valuation of equity/book value of total liabilities
• Revenue/Assets

Over a period of time, the Altman score has developed a strong reputation. About 90% of the bankruptcies predicted by Altman actually do happen. Only about 6% of the bankruptcies do not happen. The rest fall in the grey area. Altman scores have now become quite popular. These scores are widely used by auditors, bankers, etc. in order to gauge the creditworthiness of the underlying company.

It is important to note that the Altman Z-score model should not be applied to financial firms. Basically, the model should not be applied to any firm which uses opaque and complicated off-balance sheet financial structures. From the above list of ratios, it is clear that the Altman model pays a lot of attention to the balance sheet numbers. Financial companies tend to use opaque financing. Hence, their balance sheet numbers cannot be considered to be as reliable.

Merton’s Distance to Default Model

Merton’s distance to default model is another model which is commonly used by market participants. This model is quite mathematical in nature and hence is complex to understand.

The model considers the value of the firm to be an option in which the strike price is equal to the value of the debt of the firm. This might seem complicated, but the logic is quite simple. The strike price is the value at which the option becomes valuable. For instance, if the strike price of an option is $100 and the market value is $110, then the option is valuable.

Professor Merton is only trying to say what we intuitively know i.e., that the value of the equity should be more than the strike price i.e., the value of the debt. Only in this situation will the equity holders make an attempt to run the firm. Otherwise, they will simply wind up their business and leave.

Once Merton proves that the value of the firm can be thought of as the value of an option, he makes it possible to use the Black Scholes model to value the option. This is because the Black Scholes model is the most widely used model for option valuation. Merton then used the Black Scholes model in the reverse way i.e., uses the current valuation of the firm to find out the implied probability of default.

The biggest problem with this model is that it uses the stock market valuation as an input. Market values tend to be inflated. Hence, when the market is at the peak or at the bottom, the probability of default is also skewed to some extent.

This model is also widely used often in conjunction with the Altman Z score. The combination of these models often provides predictable and accurate results. The models are quite different from one another. The Altman Z score uses empirical data as input, whereas the distance to default model uses the present valuation as input. The combination of both models, therefore, gives empirical as well as theoretical validity to the final outcome.

It needs to be understood that bankruptcy prediction is still not a perfect science. However, we have come a long way with the help of these models. The firms do not have to be clueless about the probability of default. These models can be used to help them make wise investment decisions.

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