The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It
My questionable contribution to repairing the damage done by the financial crisis is to read almost all I can on the subject, but only when I find the book in the library. Not great for authors and publishers but it works for me.
Today’s tome tells the tale of Wall Street‘s most recent breed of ubermenschen–math nerds turned super traders by dint of their own brain wiring and the availability of cheap, fast computers. These aren’t your ordinary Wall Street denizens. There isn’t a Richard Fuld, Stan O’Neal or Jimmy Cayne in the lot. There are quite a few mathematicians and physicists, though, and they put all that brain power to work seeking out, and profiting from, inefficiencies and discontinuities in the markets.
Just how they do that is a proprietary secret. Except it really isn’t. Why do I say that? Well, math is math even when it’s on an almost abstracted plane. What these guys do is start with a widely held presumption–that at any given moment the markets are efficient and have priced in all available information–and then work around the edges. They make money by identifying pricing discrepancies and executing trades based on the idea that prices will realign as they should.
I am, of course, oversimplifying. If you could do what these guys do on a few semesters of graduate school math there would be a lot more folks doing it. In reality, these guys build teams that spend most of their time sifting market data looking for the markers of an asset or asset category that’s priced wrong. Then they bet.
And just to keep it interesting and make some more money, they leverage the bet. That’s bizspeak for borrowing to make a bigger bet. It’s the MBA-set equivalent of stopping by the loan shark’s on the way to the bookie’s. And when things don’t work out it’s as disastrous on Wall Street as it is on Cannery Row.
Which is still too simple. This is, literally, the stuff of rocket science. They use the same math you’d use to model what goes on inside the cylinders of your car’s engine and apply it to markets. So, for example, we see the Renaissance Technology crew applying the smarts they gained in developing voice recognition technologies. It makes sense because pattern recognition is pattern recognition.
So if these guys are so smart how did they help almost blow up the global economy? The answer, my friends, lies not in the math.
God may have written the universe in the language of mathematics. For the rest of us, though, there was the word. And the key word is model. Here’s what Webster’s says, deep down at the 12th definition:
mod·el noun \ˈmä-dəl\
12. a system of postulates, data, and inferences presented as a mathematical description of an entity or state of affairs; also : a computer simulation based on such a system <climate models>
Notice what’s missing–reality. I have heard it said that computing power makes it possible to mimic nature. Markets are a human construct, however, and modeling human behavior makes modeling natural phenomena look easy. The error of the quants is that for all the explanatory, predictive and money-making power of their models, if something hasn’t yet happened it isn’t in the data.
Again, I oversimplify. There are techniques for modeling variability and uncertainty. But all of these start with a series of rational assumptions–those would be the postulates mentioned above–about the data in hand. Build an adaptive algorithm, build a Monte Carlo model, it still will be bound by the underlying data set. When all correlations go to zero or one, and prices seem headed permanently south, the math isn’t going to work as intended.
Lord Keynes, who was no slouch in the math department but as member in good standing of the Bloomsbury group allowed for human variabilty, allegedly said that the markets can remain irrational longer than you can remain solvent. I suppose that somewhere a quant is working to model that assumption away.
And so the cycle goes round.