Altman Z =
1.2 X Working capital/Total assets +
1.4 X Retained earnings/Total assets +
3.3 X EBIT/Total assets +
0.6 X Market value of equity/Book value of debt +
0.999 X Sales/Total assets
The original Altman Z score assigned fixed weights to each of the components. Different ranges for Altman Z score represented different levels of risk of bankruptcy. Subsequent versions of the formula varied the weights depending on whether the analyzed company is public or private and also varied the cutoff ranges for bankruptcy risk.
Altman Z was formulated for operating industrial companies
The main problem with this formulation of solvency risk is that the formula is not suited for many industries. As an example, when I first tried to apply Altman Z I found that many regulated utilities showed up as having high bankruptcy risk.
I found that Altman Z was not industry specific enough to my liking. For instance, low or negative working capital doesn’t score well on Altman Z but some industries can operate with zero or negative working capital. For example, a restaurant gets paid in cash, but their suppliers will generally give them net 30 on their payables and the inventory (food) turns over very quickly.
Another sector that the Altman Z doesn’t analyze is the financial sector. What does “sales” mean for a bank? Financials tend to be highly levered and their operating risks and exposures are not well disclosed.
A heuristic for solvency analysis of non-financials
In a recession, the combination of high operating risk and excessive leverage combine to produce insolvency for non-financial company. A better way of forecasting solvency risk is to look for companies that show:
- High operating risk: The top two deciles of standard deviation of EBIT or EBITDA margin over the last 5 or 10 years (pick your horizon)
- High financial leverage: The top two deciles of financial leverage, by total debt to market equity or interest coverage. Normalized decile scores by sector.
I have found that this heuristic, or simple rule of thumb, serves as a better forecaster of solvency risk as it neutralizes many of the industry specific effects that Altman Z failed to address.
Solvency analysis of Financials is difficult
The problems of creating a solvency test for financials that operate in real-time or relative real-time is not easy. It’s not hard to do after the fact, but at any one time, no one – not even the directors of the company really know what is embedded on the books at a financial. Société Générale, Barings, Northern Rock, Bear Stearns – the list of blowup surprises go on and on.
I have had some successes with a solvency risk test for lending institutions based on the following two characteristics:
- Excessive lending growth as a sign of lending portfolio quality: In good economic times, a bank can produce earnings growth by growing its assets, or loan book. In the long run, not all banks can grow their loan books significantly in excess of GDP. High asset growth comes at a cost of lower asset quality.
- Loan loss provisions as a measure of the current level of stress: Instead of the standard ratio of loan loss provisions to total assets, I like to use loan loss provisions to assets three years ago. It’s not the loans that you make today that go sour, it’s the ones that you made two or three years ago that tend to get into trouble.