Today’s Wall Street Journal cites a forthcoming (February 2, 2010) book entitled “The Quants”, written by Scott Patterson, who also writes for the Journal. An excerpt from this book appears on WSJ.com today under the title “The Minds Behind the Meltdown”, with the (provocative and candidly, rather hyperbolic) subtitle: “How a swashbuckling breed of mathematicians and computer scientists nearly destroyed Wall Street”. Also, here’s a video interview concerning the book excerpt which appeared in today’s Journal:
I would like to offer, as an antidote to Patterson’s article and video, Steven Shreve’s October 2008 Forbes article entitled “Don’t Blame the Quants”. Steven Shreve is the Orion Hoch Professor of Mathematical Sciences at Carnegie Mellon University, where he has built one of the world’s leading quantitative finance educational programs. Professor Shreve notes that:
“It is easy … to point an accusing finger at the “quants” on Wall Street, that cadre of mathematics and physics Ph.D.s who crunch numbers in esoteric models. Without the quants, the complicated mortgage-backed securities that fueled the housing bubble and led to the freezing of credit might not have been created…To prevent a recurrence of financial crises, some call for a return to a simpler time, before derivative securities and the quants who analyze them–a time when investors bought stocks and bonds and little else. Such complaints miss the point (italics added for emphasis). When a bridge collapses, no one demands the abolition of civil engineering. One first determines if faulty engineering or shoddy construction caused the collapse. If engineering is to blame, the solution is better–not less–engineering. Furthermore, it would be preposterous to replace the bridge with a slower, less efficient ferry rather than to rebuild the bridge and overcome the obstacle.”
I completely agree with Professor Shreve’s perspective on the role played by “quants” during the financial crisis. The problem was not with the technology per se. Rather, the problem involves a combination of poor managerial judgment coupled with perverse managerial incentives. It is difficult to exercise sound judgment about financial engineering and risk management when top management views risk models as black boxes which exist for the purpose of printing money.