Quants forgot about the human factor
Quantitative finance analysts, or “quants” in the financial jargon, played a prominent role in the subprime crisis. Using a blend of mathematics, statistics and computing, they were the Wall Street geniuses who assessed the risks on all those exotic mortgage-backed securities and credit-default swaps. Their models were telling them that the risks were widely spread and that it was safe for the institutions employing them to pour billions of dollars into such financial instruments. This was supposed to be the most up-to-date, scientifically based way to make such calculations. What went wrong? Well, they forgot to include one minor variable… the “human factor.”
An article published two days ago in the New York Times surveys the various defects that have been found in those models.
The models, according to finance experts and economists, did fail to keep pace with the explosive growth in complex securities, the resulting intricate web of risk and the dimensions of the danger.
But the larger failure, they say, was human – in how the risk models were applied, understood and managed.
Yes, one of the fundamental problems of mainstream neoclassical economics was at play here again: the erroneous belief that one can reduce human action to a set of numbers and formulae, and that these can be calculated and extrapolated just like physicists do when they try to predict the position of some space object a few millions years in the future.
The Wall Street models, said Paul S. Willen, an economist at the Federal Reserve in Boston, included a lot of wishful thinking about house prices. But, he added, it is also true that asset price trends are difficult to predict. “The price of an asset, like a house or a stock, reflects not only your beliefs about the future, but you’re also betting on other people’s beliefs,” he observed. “It’s these hierarchies of beliefs – these behavioral factors – that are so hard to model.”
Indeed, the behavioral uncertainty added to the escalating complexity of financial markets help explain the failure in risk management. The quantitative models typically have their origins in academia and often the physical sciences. In academia, the focus is on problems that can be solved, proved and published – not messy, intractable challenges. In science, the models derive from particle flows in a liquid or a gas, which conform to the neat, crisp laws of physics.
Not so in financial modeling. Emanuel Derman is a physicist who became a managing director at Goldman Sachs, a quant whose name is on a few financial models and author of “My Life as a Quant – Reflections on Physics and Finance” (Wiley, 2004). In a paper that will be published next year in a professional journal, Mr. Derman writes, “To confuse the model with the world is to embrace a future disaster driven by the belief that humans obey mathematical rules.”
Funny how we always tend to reinvent the wheel in economics – or at any rate that’s the impression one gets watching mainstream economists trying to make sense of the world from their confused perspective. I haven’t read Mr. Derman’s book and I don’t know who he is quoting, but that insight is not particularly new. Decades ago, in a book aptly titled Human Action, Mises denounced “those economists who want to substitute ‘quantitative economics’ for what they call ‘qualitative economics’ (…). The impracticality of measurement is not due to the lack of technical methods for the establishment of measure. It is due to the absence of constant relations.”
Human acts of choice, Mises explained, cannot be predicted with certainty because “different individuals value the same things in a different way, and valuations change with the same individuals with changing conditions.”
The quants were blinded by their too complex models. If they had only applied some common sense, they would have noticed that something wrong was going on in the wonderful world of these securities. Of course, the ultimate high-tech device to avoid being caught in this mess would have been a good book explaining the Austrian theory of the business cycle. But sadly, Wall Street is still behind the times when it comes to such sophisticated devices.