Quants, hagiographized, get their comeuppance

In response to what amounts to a hagiography of quants in the investment industry in Wall Street Journal reporter Scott Patterson’s new book, “The Quants,” I wrote a review that appeared recently in the online journal Advisor Perspectives (may require registration to view).

A few excerpts:

“Before reading [the book] I even thought twice because I had first read an excerpt in a column in The Wall Street Journal. The column was drenched in the hyperbole that seems, for some reason, to be obligatory when talking about quants in the investment field: ‘brainy math whizzes’; ‘theoretical breakthroughs in the application of mathematics to financial markets’; ‘brain-twisting math’; ‘superpowered computers’; ‘advances that had earned their discoverers several shelves of Nobel Prizes’.”

“… of the quants … only a very, very few actually make money … by beating the other players. The rest of them, I believe, the overwhelming majority, make money because of the fees charged by their companies, and because of the arcane and sophisticated images they help their companies to project, the better to charge high fees.”

“There are larger errors. For example [the book] says, ‘theoretical breakthroughs in the application of mathematics to financial markets, advances that had earned their discoverers several shelves of Nobel Prizes, [were applied] to the highly practical, massively profitable practice of calculating predictable patterns in how the market moved and worked.’ This is essentially false. The Nobel Prize-winning theories pretty much all rest comfortably with a random walk model of the market, and can’t be applied to calculate predictable patterns in how the market moves. Yet this misconception seems often to prevail.”

“One problem … is that there are too many pseudo-mathematicians in the field. Most of the pseudo-mathematicians don’t really know the difference between a discrete process and a continuous process, and when it is reasonable to believe that a continuous process can be assumed for theoretical purposes because the (real-world) discrete process converges to it, and when it is not… And they don’t know basic probability theory well enough to apply simple forms of it that don’t rest on too many assumptions, and that would be more transparent to practical analysis. Instead, they revert rapidly to the more ‘sophisticated’-looking, but more opaque, techniques of regression analysis or Ito’s lemma, or the like.”

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