Wall Street’s Math Wizards Are Tweaking Their Formulas
In the aftermath of the great meltdown of 2008, Wall Street’s quants have been cast as the financial engineers of profit-driven innovation run amok. But the real failure, according to finance experts and economists, was in the quants’ mathematical models of risk that suggested the arcane stuff was safe.
The risk models proved myopic, they say, because they were too simple-minded. They focused mainly on figures like the expected returns, volatility and the default risk of financial instruments. What they didn’t sufficiently take into account was human behavior, specifically the potential for widespread panic. When lots of investors got too scared to buy or sell, markets seized up and the models failed.
That failure suggests new frontiers for financial engineering and risk management, including trying to model the mechanics of panic and the patterns of human behavior.
At the Massachusetts Institute of Technology, Andrew W. Lo, director of the Laboratory for Financial Engineering, is taking a different approach to incorporating human behavior into finance. His research focuses on applying insights from disciplines, including evolutionary biology and cognitive neuroscience, to create a new perspective on how financial markets work, which Lo calls “the adaptive-markets hypothesis.” It is a departure from the “efficient-market” theory, which asserts that financial markets always get asset prices right given the available information and that people always behave rationally.
Efficient-market theory, of course, has dominated finance and econometric modeling for decades, though it is being sharply questioned in the wake of the financial crisis. “It is not that efficient market theory is wrong, but it’s a very incomplete model,” Lo said.
Lo is confident his adaptive-markets approach can help model and quantify liquidity cascades in a way traditional models, with their narrow focus on expected returns and volatility, cannot. “We’re going to see three-dimensional financial modeling and eventually n-dimensional modeling,” he said.