Practical analysis for investment professionals
09 February 2016

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)

About the Author(s)
Larry Cao, CFA

Larry Cao, CFA, senior director of industry research, CFA Institute, conducts original research with a focus on the investment industry trends and investment expertise. His current research interests include multi-asset strategies and FinTech (including AI, big data, and blockchain). He has led the development of such popular publications as FinTech 2017: China, Asia and Beyond, FinTech 2018: The Asia Pacific Edition, Multi-Asset Strategies: The Future of Investment Management and AI Pioneers in Investment management. He is also a frequent speaker at industry conferences on these topics. During his time in Boston pursuing graduate studies at Harvard and as a visiting scholar at MIT, he also co-authored a research paper with Nobel laureate Franco Modigliani that was published in the Journal of Economic Literature by American Economic Association. Larry has more than 20 years of experience in the investment industry. Prior to joining CFA Institute, Larry worked at HSBC as senior manager for the Asia Pacific region. He started his career at the People’s Bank of China as a USD fixed-income portfolio manager. He also worked for US asset managers Munder Capital Management, managing US and international equity portfolios, and Morningstar/Ibbotson Associates, managing multi-asset investment programs for a global financial institution clientele. Larry has been interviewed by a wide range of business media, such as Bloomberg, CNN, the Financial Times, South China Morning Post and the Wall Street Journal.

7 thoughts on “The Altman Z-Score after 50 Years: Use and Misuse”

  1. Dominic says:

    Hi Larry,
    I ‘ve been following your discussion on Altman Z-score model for credit scoring and i have a couple of questions. 1. Is it possible to carry out a Z-score for banks given their different style of reporting and also the nature of there business? 2. If yes how would you go about it. If no what method would one use to analyse the financial health of a bank with respect to ability to meet obligations to debt holders?

  2. Larry Cao, CFA says:


    Great question. Z-Score was designed mainly for use with manufacturing companies so to apply it to banks naturally would require much tweaking. There’s some published research on the subject using a bankling sample (such as paper
    Alternatively, SRISK, a measure that Prof. Robert Engle developed to estimate how undercapitalized a financial institution can become in times of crisis, may also be useful. See my interviews with him for more details.

    Warm regards,

    1. Dominic says:

      Thanks Larry, i will go through the interviews.

      1. Dominic says:

        Hi Larry,
        Thank you for your insights and discussions on the Altman z-score topic.
        I was just doing a z-score for a beer manufacturer in a frontier market and the following were the findings
        1. The current z- score looks healthy at 3.87 in 2016, but the trend has been gradually declining from 6.29 in 2011.
        2. I tried to break down the contribution of each metric to the total score and I found out that on a 5-year average, 53% of the score is contributed by one metric; Market value equity/ total liabilities. The others Sales/Assets contributed 26% and Ebit/assets contributed 23%. The other two had a negative contribution.
        My question is what should I read from these two findings of the company? Should I be worried about the trend and the dependence of a single metric in getting the score?

  3. Preeti gupta says:

    Hii larry i have one question. Z score can we use as a normal health check either company always in profit.

    1. Larry Cao, CFA says:

      Hi Preeti,

      Thank you for visiting our blog. The short answer is yes. The more elaborate answer is as follows –

      Z-score was developed to predict bankruptcy. As long as the profitable companies borrow money, which they could do for any number of reasons including financing their growth, they would have insolvency risk. Granted though the analysis would be rather boring if the companies have significant retained earnings and very low liabilities.

      Warm regards,

  4. Nigma Bajracharya says:

    Hi Larry,
    If we have so many problems with z-score what are your suggestions to improve this model

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