Counting, from Bruno Ombreux

March 30, 2007 |

 Sometime one doesn't need to count. Here is an example. Once I was considering buying a very illiquid small cap with a huge dividend. I called the CFO and said I was an investor interested in their stock. I asked him why such a dividend? He told me it was a one-off; they were getting rid of cash they didn't need. There was no chance of such a high dividend in the future.

Then I told him his stock was too illiquid anyway. He said they could do something about it. The next morning there were 100,000 shares for sale, instead of the usual 1,000 or 2,000. Needless to say, I never bought this stock. There was no need for counting.

There are plenty of examples like that, but to my knowledge they are always in opaque markets, with few players. I could give similar examples in physical oil or even swaps.

However, when it comes to huge markets like stocks indices, big caps, or WTI (even the oil majors or OPEC don't try to call the price of oil), how can anybody believe that he knows more than the market, that he has an information advantage? In those cases, counting is the only solution.

You could reply that information is not enough; you need to process it. And someone with experience and interest in the markets is able to process information better than the rest of the financial community. This may be true. Still, for the rest of us, with less experience and wits, isn't it safer to do what scientists do when confronted with time series, that is, count?

Besides, nobody can deny the incredible efficiency of the scientific method. Just look at its positive impact on everybody's lives from the Age of Enlightenment. To deprive oneself of such a tool doesn't make sense, even if one is a superior analyst. 

Adi Schnytzer replies:

I'd like to present my critique of counting. I assume that we wish to predict where the market is heading, be it in an hour's or a year's time. Counting — as exemplified by Vic and Laurel — generally involves regressing the returns or prices of stocks on one or, at most, two explanatory variables and testing for significance. Thus, using daily data, we may ask was has happened to the S&P500 over the past few years if, on Groundhog Day, the little beast saw its shadow.

captured British sailors
We may check what happened the next day or daily for the next month or whatever. The problem is that rarely are other explanatory variables added to the regression and this is OK if those missing variables are uncorrelated with shadow viewing. But, if this does tell us about a cold winter remaining, it affects energy prices and these should appear in the regression since the S&P 500 is clearly affected by energy stocks which are known to be related to the weather. But this is not the real problem.

The real problem is that since the variance of stock price returns is relatively large in all models that have ever been built, any exogenous shock can turn ups into downs and vice versa. And it is precisely exogenous shocks (e.g. what will the Iranians do tomorrow, what will Bush do, what will the big boys do?) that counting and its big brother econometrics cannot handle at all! But the world is full of these. How many of the news items in today's newspaper have you predicted?

To be sure, many turn out to be irrelevant, but not all. And once they have happened, it's too late for the model! Now, there are people who evidently know in advance things that are not in the public domain. The Iranians know what they'll do to the sailors tomorrow, but most of us don't.

Suppose they are each given $1 mil in gold and sent home first class tomorrow after seeing the Iran nuclear sites destroyed tonight. One suspects the market might react and counting will have proven utterly useless. Suppose, on the other hand, the Iranian Navy, having proven that it is superior to the Royal Navy, decides to blockade all oil exports from the Gulf. Hmmmm…





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