In light of the recent surge in coronavirus cases, there is a growing risk that a prolonged economic downturn could result in more business failures this year.
Last year, we have discussed in this column “Sizing up the coming surge of financial distress” that by using the Altman Z-score model, we could predict which companies were likely going to fail.
According to the Altman Z-score, companies that score less than 1.81 have high risk of financial distress. The lower the score the company gets, the higher probability that it may go out of business in the future.
When we applied this model to listed companies in the Philippine Stock Exchange (PSE), we found that the number of financially distressed companies, as determined by the Altman Z-score, had been increasing over the years.
The percentage of companies with low Z-scores has increased from 43.6 percent in 2017 to 47 percent in 2019, while the credit quality of the overall market decreased from average Z-score of 2.26 in 2017 to 1.95 by the end of 2019.
Last year, with the outbreak of the crisis, the percentage of companies with low Z-scores increased to 58 percent as average Z-score in the market fell to a record low of 1.33.
Given a recessionary economy, it is not surprising to find the overall Z-score of the market to be falling since three of the five key ratios used in the Altman Z-score model are based on earnings, sales and market capitalization.
In the early 1980s, a professor by the name of Dr. James Ohlson from New York University, the same university where Edward Altman came from, proposed an alternative model to the Altman Z-score in predicting financial distress.
The model, which is known as the Ohlson O-score, uses nine different measures of a company’s default risk, where only three of which are directly affected by earnings and sales, while the rest are related to asset size, leverage, working capital, liquidity and debt financing.
According to Ohlson, the O-score model has better accuracy because it was derived from a study of over 2,000 companies as compared to Z-score, which considered only 66 companies.
The O-score tells us that a company has high probability of default when it scores 0.5 or higher. Unlike the Z-score, the result of O-score can be converted into probability percentage using logarithm techniques.
Using the O-score of 0.5 as example, we can convert this to 62.2 percent of probability of bankruptcy. The higher the score, the higher the chances of failure.
Now, if we will apply this to all listed companies in the PSE and use 50 percent probability as a cut-off, we will find that the number of financially distressed companies has been increased from 7.5 percent in 2017 to 11.1 percent in 2020.
Although the number is not as big as those found by Z-score, as it is roughly only about one-tenth of the total, the increase in financial distress is significant.
This trend is also evident even among the big-cap stocks belonging to the PSE Index if we will use the O-score as indicator of financial distress.
The average probability of bankruptcy among PSE Index stocks was only 4.3 percent in 2017, but over the years, with the increase in borrowings, this has almost doubled to 8 percent by end of 2020.
If this trend in O-score will continue, given the slowdown in the economy, we can expect more companies to struggle with debt payments in the foreseeable future.
The huge buildup of debt in the past decade has caused financial risks to increase to unprecedented levels. For example, in 2010, the ratio of total debt of PSE listed companies to total market capitalization was only 20.1 percent.
But through the years, as interest rates fell to record lows, the ratio of total debt-to-market cap slowly increased to 34.7 percent in 2017. By the end of 2019, in just only two years, this ratio has accelerated to 50.2 percent.
Today, with the fall in the PSE index, the ratio of total debt-to-market cap stands at 57.4 percent.
Rising risk of business failures as suggested by Z-scores and O-scores only show that the stock market is headed for more challenges this year. INQ