Edward I Altman’s Z-score gets rejuvenated for new businesses, even for India

He is a man who loves bankruptcies and he does not shy away from admitting it anywhere. It is not because he is a sadist but mainly because he enjoys collecting data from failed entities to recognize the potential defaulters and  save them. Here we are talking about Professor Edward I Altman, best known for development of the Z-score to predict bankruptcy. 

Altman, who is Professor of  Finance, Emeritus, at New York University’s Stern School of  Business, has dedicated more than 50 years studying bankruptcy. He was in Mumbai recently. Before  one of his speeches, he chats exclusively with Jescilia Karayamparambil and RN Bhaskar about his plan in India.

Edited excerpts:

Your model was based on assets and most of the companies are traditionally based on assets. But if you look at technology companies like Uber, they are not based on assets. How do see those companies?
Apart from Z-score, I have another model that I have developed—Z double prime. It was built in 1995. That is for non-manufacturers, even though it has assets among the variables it considers. We found Z double prime to be accurate for retailers. But I cannot really say it will be accurate for technology firms.

However, this model is appropriate for retail, energy, services sector etc. It focusses on four variable rather than five. We eliminated the fifth variable—‘sales to total assets’. We find the variable most sensitive in case of such industries, and found that we had some trouble with it. If you apply the Z model, to a typical retailer, you will have on an average much higher scores compared to the manufacturing model.

In case of assets-light companies like Uber, both models will be a problem. I don’t have an answer to that. I will have to build a separate model for technology companies like Air B&B and Uber so on. Additionally, we would need a significantly large number of such companies before we build a separate model.

We would need defaults in this space to build the new model. I love defaults and bankruptcies. Not because I am a sadist but because I get data. I have said that over years. My model will not be appropriate for firms with less assets or no assets. I cannot do anything about it.

I have applied the model to firms especially when they have value of equities—or market capitalization—which is one of the variables. For instance, let us take another firm that has very high equity and that is Tesla. The model is perfect for Tesla as it does have assets and also, even though it has huge market value relative to other automobile companies, it has a relatively low number or score.

This is mainly due to the high level of debt of the company and the lack of profits. We rate Tesla in terms of credit risk as B(-). Even though this company could have a good market value. I have run my model on 10 large recent Indian bankruptcies. Everyone was predicted to be bankrupt two years before the bankruptcies actually happened. Even though their bond ratings were investment grade. So coming to India was prefect for me as it allows me to test the model against the background of increased defaults.

If your model were applied, maybe it would have been a different scenario in India?
The model is totally unemotional, no collusion and no corruption. People who used the model have saved a lot of money by not investing in Bhushan Steel, IL&FS etc. A lot of industries are getting to know that. There was a realisation that this model predicated bankruptcy or default and so they did not invest.

Do you see people in India applying this model very rigorously?
I am impressed that there are people who have been applying the model rigorously in India. At times, there have been some mistakes. This is one of the purposes for my visit to India. I would like to educate people on the way to use the model on the basis of what we have learned over years.

One of the purposes of the speech is to make the application more rigorous and accurate. It turns out that it is not only the Indian population but the people around the world need to understand the model. The model is out there on the internet. It is in the public domain. But the criteria for developing the model and classifying an asset as distressed have changed over years.

How many speeches are you giving in India?
I am giving roughly seven speeches in India. This is to teach people how to use the model in the modern context.

In what capacity, can we see you in India?
We are developing new models that we will be selling in India and elsewhere, concentrating on small and medium size firms. In this case, it will go beyond general model; and have specific models for different sectors and different regions of the country. We have built models for the SME area in 19 European countries. Now, we have decided to enter potential markets like India.

I am also aware that we might just not find much of a difference in India but we know that we will face some difficulty in getting reliable data in the quantities that we require. I am here to explore the possibility to build a model for Indian companies but particularly in SME domain.

What has been the outcome of your visit to India?
The crowds have been enthusiastic. My general feeling is that the model should be used by rating agencies in addition to their traditional techniques. I have been approached by banks and other institutions which I cannot talk about.

How do you see Bankruptcy Code in India?
I am very impressed by the Indian Bankruptcy Code. But it is too young to say whether it will work or not. For the first time, the concept of restructuring and reorganisation has become relevant in India. Most firms that go bankrupt in many countries usually liquidate and do not restructure. You have been objective to try to rehabilitate funds.

So, your new code and my visit to India is fortunate for me. I hate to say it but I have seen a lot of interest in my work due to the bankruptcy that you’ll had. If there were  no bankruptcies or non performing loan  issues, there will be low interest where my work is concerned.

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