Predicting Corporate Failures
There are many factors that affect the value of a stock. One of these is the risk that a company may default on its loan in the future.
When financial risks become evident, higher discounts demanded by the market may cause a share price to fall.
But how do we identify these risks?
One source of information that market analysts and investors usually rely on is the financial statement of the company.
By computing a set of key ratios, one can more or less gauge the existence of a probable financial problem.
In 1968, a professor from New York University by the name of Edward Altman developed a handy financial model that can predict the likelihood of a company’s bankruptcy within two years.
Known as the Altman Z-score, the model generates a number by taking the sum of five weighted financial ratios of a stock. The lower the resulting score, the larger the probability that the company will fail.
The formula in the computing for the score is expressed as Z = 1.2a + 1.4b + 3.3c + 0.6d + 0.99e.
The “a” in the formula represents the ratio of working capital divided by total assets; “b” is retained earnings divided by total assets; “c” is earnings before interest and tax divided by total assets; “d” is market capitalization divided by total liabilities; and “e” is revenues divided by total assets.
Note that the common denominator among the four of the five ratios in the model is the total assets.
A company with a relatively small working capital as a percentage of total assets means it has less receivables and inventories to tap for liquidity in case it needs to boost its cash flow.
A low retained earnings to total assets means the company may be accumulating losses resulting in falling retained earnings while a low earnings before tax and interest indicates low return on assets.
A low revenue to total assets ratio, on the other hand, means the company may not be generating enough sales to justify its investments.
If investors expect potential problems to unfold in the company, a falling share price may shrink its total market capitalization lower than its total liabilities.
All low financial ratios lead to low Z-scores in the model.
According to Altman, companies that have scores of less than 1.81 are considered financially problematic.
But companies that have Z-scores of 2.99 and above are regarded as financially healthy while those that fall in between 1.81 and 2.99 are the companies that warrant further research.
Over at the PSE, according to historical data from 2017, about 42 percent of all listed stocks suffer from low Z-scores while 37 percent enjoy above 2.99 Z-scores and 21 percent fall in the grey area.
It has also been observed that the higher the Z-score of a company, the greater the probability that it will be priced higher by the market.
For example, in 2017, stocks with high Z-scores had median price to earnings (P/E) ratio of 20 times while those with Z-scores of 1.81 and below had only a median P/E of 10 times.
In 2018, the same can also be observed where low Z-score stocks are priced at only 9 times P/E as against high Z-score stocks at 16 times P/E.
Moreover, it is also interesting to know that high Z-score stocks have relatively stronger correlation with P/E ratio at 13 percent as compared to low Z-score that has 0.4 percent.
This means quality stocks as measured by Z-score is more predictable to trade at high P/E than speculative stocks with tricky ratios.
While the Altman model seems to be useful in identifying value stocks, studies have shown in recent years that the model has been less effective in predicting corporate failures.
In the past, problematic companies do not easily go bankrupt because they either raise funds to improve their financial situation or get acquired by a stronger company.
Despite its limitations, the Altman Z-score offers a good alternative tool in evaluating the financial health of a company to manage risks.
Henry Ong is a Registered Financial Planner of RFP Philippines. He is one of best selling book co-author of Money Matters. He also writes regularly as columnist for the Philippine Daily Inquirer.
Source: https://business.inquirer.net/264366/predicting-corporate-failures
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