Contents
What’s it: Altman Z-score is a multivariable formula for measuring a company’s potential bankruptcy. It is a function of the five financial ratios: profitability, leverage, liquidity, solvency, and activity ratios.
The calculations are also easy. You only need to calculate each of the five ratios beforehand. Then, you plug in the equation to generate the Z-Score.
This score is useful for predicting whether a company has a high probability of bankruptcy. Then, from the results, you can also compare it with other companies.
Why the Altman Z-score is important
Altman Z-score is important because it facilitates analysis and decision making. You can quickly assess a company’s credit quality without having to develop your own model.
In the stock market, investors use the Z-Score for buying or selling decisions of a company. They usually consider buying a stock if the Altman Z-Score approaches 3 and selling the stock if it approaches 1.8.
Altman Z-score formula
Edward I. Altman, in 1967 measured the susceptibility of businesses to failure using multivariate statistics. It uses a weighting system of the five main financial ratios. He then released his results in 1968 as his first Altman Z-score model:
Z-Score = 1,2X1 + 1,4X2 + 3,3X3 + 0,6X4 + 1,0X5… Model 1
Where:
X1 = Working capital/Total assets. You calculate working capital by subtracting current assets from current liabilities. This ratio tells you how good the bearing is available for short-term liabilities. If the company has a relatively high working capital compared to total assets, it has relatively good liquidity.
X2 = Retained earnings/Total assets. Retained earnings are the accumulated net profit remaining after paying dividends to shareholders. It is a source of internal capital. In the financial statements, it’s in the shareholder’s equity section. Companies can use it for various purposes, including to pay off debts. The higher the ratio, the greater the internal capital and the smaller the company depending on the debt. If the company has little retained earnings, it must raise capital from other sources, through equity injection by shareholders or debt.
X3 = Earnings before interest and tax (EBIT)/Total assets. EBIT tells you the profit a company earns from its overall operations before paying for routine expenses, taxes, and interest. This ratio shows you the company’s profitability, which specifically measures the return rate that a company makes on its assets. A high ratio indicates the company can utilize its assets to generate profits.
X4 = Market value of equity/Total assets. This ratio measures the company’s solvency using market value instead of book value. The market value of equity is equal to the multiplication of the company’s stock price and the number of shares outstanding, including common stock and preferred stock. Meanwhile, total assets are equal to the market value of equity plus liabilities. This ratio tells you the amount of equity the company has relative to the assets the company owns. The higher the ratio, the less the company will rely on debt.
X5 = Total sales/Total assets. This is the asset turnover ratio, which is the company’s ability to generate revenue from its assets. Altman views that this ratio measures the company’s ability to face competitive conditions. A higher ratio indicates the better the company uses its assets to generate sales.
For Model 1, the chances of bankruptcy are higher if the score is below 1.8. Meanwhile, companies with a score above 3 have little chance of going bankrupt and are relatively safe. Meanwhile, a score between 1.81 and 3 represents a grey zone.
Altman’s first model above is not suitable for small, non-manufacturing, or private companies. In building the model, Altman uses statistical data from public manufacturing companies. Apart from that, he also excluded all companies with assets less than $1 million. Thus, outside these categories, the model is irrelevant due to an inadequate sample.
Then, Altman developed two further Z Score models. He uses a sample of private firms and non-manufacturing firms. Thus, the updated model is more relevant for all companies.
Because share price information is unavailable for private companies, Altman replaces the market value of equity in variable X4 with the book value of shareholder equity. The Altman Z-Score model for a private company is:
Z-Score = 0.717X1 + 0.847X2 + 3.107X3 + 0.420X4 + 0.998X5 … Model 2
For Model 2, the sound Z-Score for private companies is above 2.9, indicating a low bankruptcy risk. On the other hand, private companies have a high chance of going bankrupt if the Z-Score is below 1.23.
Meanwhile, for non-manufacturing companies, it omits the X5 variable. The equation for the model is as follows:
Z-Score = 6.56X1 + 3.26X2 + 6.72X3 + 1.05X4… Model 3
For Model 3, a value above 2.60 is doubtful to go bankrupt. A value below 1.10 means the company has a high chance of going bankrupt.
Criticisms of the Altman Z-Score
In an early paper, Altman found a Z-Score 72% accurate at predicting bankruptcy two years before the event. In subsequent tests, the Altman Z-Score’s accuracy was between 80% and 90%.
Z-scores provide a better indication than credit ratings by rating agencies. This is reflected in Altman’s examination of the company’s Z-Score before the financial crisis in the United States in 2008-2009. In 2007, he found that 50% of companies were at risk of bankruptcy with a median score of 1.81, equivalent to a B rating. However, the bond ratings of certain asset-related securities were much higher than they should have been.
Despite all the accuracy, some have criticized the model.
- The model depends on the sample taken. It may be inaccurate for companies in different countries. In Indonesia, for example, the accuracy of the Altman Z-Score model is only around 27.96% for listed manufacturing companies on the Indonesia Stock Exchange.
- The business and competitive environment are also continually changing. It exposes the company’s financial performance and the rate of bankruptcy. Increased global competition, for example, is putting further pressure on the profitability of many companies. Thus, using the previous score ranges to classify firms is inappropriate.
- The model does not predict when a company will actually be legally bankrupt.
