The Altman Z-Score after 50 Years: Use and Misuse
This is the second installment in my interview series with Edward Altman in which we discuss the most advisable and problematic applications of the Altman Z-score. For additional details of our conversation, check out the first and third installments.
Larry Cao, CFA: It’s been almost 50 years since the Z-score was first developed. Would you suggest doing anything differently today?
Edward Altman: Over the years, the so-called cutoff scores in the model has been retained by the people who applied the model. But in my opinion, that is not the best thing to do.
Over time, I began to observe that the average Z-score of American companies mainly, but even global companies, began to get lower and lower. [The bond market] became more available for both investment grade and non-investment grade companies and companies periodically took advantage of low interest rates to raise their leverage. As a result, the financial risk of companies began to increase. Also with global competition, companies’ profitability began to diminish. And so the average Z-score became lower and lower, which meant that more firms would have been classified as likely bankrupt using the Z model if we kept the original cutoff scores. In order to modernize the model, we needed bond-rating equivalence of the scores, which changes constantly and adds on an updated nature to the interpretations of the scores.
We now think the most important attribute of the Z model is the probability of default (PD), not the zone classification — safe, grey, or distress. We do it in a two-step process. We get the PD from the score of the company, whether it be from Z, or Z prime, or Z double prime. And then we look at the bond rating equivalent as of that point in time. For example, 2015 — the average B-rate company has a Z-score [of] about 1.6. That would be in a distress zone back in 1968. But today, B is a very common bond rating for many companies. In fact, globally it’s probably [the] most dominant junk rating category. If you rated all companies in the world, the average would probably be about B if they had a rating. And so we ascribe a probability of default based on a bond rating equivalent by looking at the historic incidence of default given a B rating at birth. Cumulatively, I can tell you, from one to 10 years, what the likelihood of default is given a bond rating equivalent. So no longer do we only look at the cutoff scores for the three zones of credit worthiness.
Okay, bond rating equivalent is in and cutoff scores are out. What mistakes do you see practitioners making in using the Z-score today?
To this date, I would say the vast majority of people are misusing the Z-score because they are applying it across the board regardless of the sector, the industry. And what we found over the years is that non-manufacturers, especially in certain industries like services or retail, have on average higher Z-scores than manufacturing companies.
My advice for users is if you are outside the United States and particularly if you are not a manufacturer, you should look at Z” and its bond rating equivalence approach for ascertaining a PD.
Would you say the value of the Z-score is more in its methodology or the score itself?
That’s a great question, Larry. Yes, I’ve always argued it’s better to use a local model rather than the original US model. And I’ve done it myself. I’ve personally built models in Brazil, Australia, France, Italy, and Canada. And you will find references to models almost anywhere in the world in the literature. It’s a pretty easy methodology for PhD students and practitioners to adapt to a different environment.
But then again, even if it isn’t the best model that could be built for service companies or energy companies in 2016, it’s still a good benchmark and has retained its accuracy. If I had the time, I would build the model for Malaysian companies or Indonesian companies or Hong Kong companies or Asia all together. I suppose that there are good researchers there who might just attempt that!
Will there be a data issue? For a lot of these countries, the history may not be there. They don’t have bond rating equivalence.
That’s exactly right. That’s a very good point. The bond rating equivalence in almost all cases has to be derived from data from the United States. We have lots of defaults, lots of bankruptcies in the US, so you can get probability distributions based on ratings that have a fairly large sample. You can’t do that in emerging markets or countries like Australia, where they haven’t had a recession since the early 1990s.
So yes, people said I should have continually updated the Z model but that means you have to keep publishing the updates. People have to find it. People have to use it and test it. It’s much easier to just periodically test the model, and to even build new models that incorporate the lot of data from the relevant countries and industries and combine this firm data with market value measures and possibly even macro-economic data.
What advice do you have for practitioners who want to build their own version of the Z-score model? For example, what’s your secret sauce for putting together the sample?
Although the methodology is pretty straightforward, there are subtleties to it. You need a sample of healthy companies and unhealthy companies. There are issues such as sample size. Should [there] be [an] equal number in the two groups or should there be more representatives of the population — 99% non-default, 1%, 2%, or 3% default, depending on the time period? Should they all be manufacturers? Should they be a cross section of industries?
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All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer.
Photo courtesy of the Hong Kong Society of Financial Analysts (HKSFA)