Moody lessons for Icra

Most of us feel stock markets in India are not to be trusted because they’re controlled by a handful of brokers, and given that a Harshad Mehta can still be followed by a Ketan Parekh despite ten years of regulatory reforms, you can hardly fault us.

Yet, despite all their imperfections, stock markets manage to capture information about changes in firms long before even sophisticated credit rating firms do!

Indeed it is precisely for this reason that ratings firm Moody’s Investor Services acquired a firm called KMV a couple of years ago — KMV was the pioneer in creating this model after taking into account 30 years of information on over 76,000 corporate defaults out of a total of one million privately-held and 70,000 publicly-held companies.

So, if you’re looking to see which company’s more likely to default, forget credit ratings firms like Icra and Crisil, just follow the stock market!

Sounds hard to believe, doesn’t it? So let’s look at the theory behind the model first made by nobel laureate Robert Merton way back in 1974 (of course, it was only in the mid-90s that the model was empirically tested by KMV).

Essentially, Merton’s point was that since a firm’s value is the sum of its equity and debt, any reduction in its equity value lowers its overall value, and therefore increases the riskiness of its debt. So, the stock market is the first signaller of a default event developing.

Since the proof of the pudding is only in the eating, let’s go to the Moody’s website to look for examples of how well the model works. The site has details of three celebrated defaults of recent times — United Airlines, Enron and WorldCom.

In each case, plotting has been done of KMV’s ‘expected default frequency’ (EDF) along with that of other agencies like Standard and Poor’s (S&P), and it is the KMV EDF that most closely mirrors events in the company, and its downward descent into default.

Readers would do well to go to www.moodyskmv.com/research/ Enron.pdf to see the actual graphic as it is really quite evocative. From February 28, 2001 to early November, when the company’s stock fell from $ 68 to around $ 12, the EDF rose from 0.35 to nearly 10 (an EDF of 20 is the maximum a company can go to), but the S&P rating stood rock-steady at BBB.

It was only then that it began to move, and reflect Enron’s increased riskiness. An EDF of 0.35 means a company has a 0.35 per cent chance of defaulting within the next year, and a company with an EDF of 3.5 is ten times more likely to default than the one with a 0.35 EDF.

In other words, by early November, the stock-price derived credit risk showed that Enron’s default risk had increased 28 times since February.

But what of India, has any similar model been developed here, considering how ‘rigged’ the markets here are perceived to be? Susan Thomas of the Indira Gandhi Institute of Development Research (IGIDR) in Mumbai along with Ajay Shah of the ministry of finance and Rajeeva Karandikar of the Indian Statistical Institute have worked on a similar study for India, and their results appear quite robust.

But since there is no long-term data available in India on firms actually defaulting (banks who, for instance, roll over debt which has defaulted tend to keep it secret, and the RBI doesn’t release data on when actual defaults took place), they’ve used a proxy, that of credit rating firms upgrading or downgrading corporates.

Imagine the irony, they’re trying to create a rival model to discredit the credit-raters, and they’re using their data! But what even this limited data shows confirms the original hypothesis — that the markets get it first.

And that stock prices of companies begin declining as early as 300 days before the actual event of their being downgraded! Intuitively, by the way, that makes perfect sense, as stock markets factor in events like suppliers not getting paid on time, salaries coming late and so on, all incipient signs of troubles in a firm.

Of course, there’s a problem here. Does just the fact of a fall in stock prices imply a firm is that much closer to default? Clearly it does, but what if there is an overall fall in the Sensex, for instance, and so doesn’t really indicate a problem with that particular firm? Thomas acknowledges this to be an area of weakness, but says there’s little the model can do unless there is actual defaults data available, so that the model can then be fine-tuned.

ICICI Bank, for instance, is using the model, and has probably fine-tuned it since they have decades of data on actual defaults by borrowers. But how they’ve tweaked the model, obviously, is not in the public domain.

The importance of early warning systems, needless to say, is critical since one of the biggest problems with loans that have turned NPAs is that banks simply aren’t monitoring them closely enough.

Unless there is serious corruption, few banks give rank bad loans to begin with, it’s only several years later that they become bad — so, if loan officers have better early warning systems, they will actually be able to begin restructuring loans long before they actually go bad. But for that, we need a lot more information on defaults of corporates.

Mr Governor of the RBI and Chairpersons of banks, let’s put some information out in the public domain. It’s, as Arundhati Roy puts it, for the greater common good.