Go to end of page
Statements posted here are those of our readers and do not represent the BaseballThinkFactory. Names are provided by the poster and are not verified. We ask that posters follow our submission policy. Please report any inappropriate comments.
The most profound example of overshoot, of course, happened in finance, where the rise of quantification could concentrate decisionmaking—and moneymaking—within a relatively small group of people at a bank’s headquarters. Soon they were trying to optimize their algorithms to maximize profit, minimize risk, and make millions of dollars for themselves. Global regulators didn’t help: In 2004, in sympathy with the over-leveraged, hyper-quantified banking system, the Basel Committee—the Switzerland-based body that oversees world finance—put out the Basel II accord, more than 250 pages of regulations that effectively placed individual banks in the driver’s seat. The accord essentially embraced all of the quantitative techniques used by the wizards who would end up blowing up Wall Street, and it allowed banks to operate with astonishingly high levels of debt. As everybody knows, all of that ended in catastrophe in 2008. (You can read more about the particular math of that cataclysm in my March 2009 cover story for WIRED, “A Formula for Disaster."
it seems obvious that what he calls "black swans" are almost universally ignored by finance quants in their risk calcuations.
unless you like to scream "Look at those idiots!", as Taleb enjoys doing.
One definition of black swans is "the stuff that quants ignored that turned out to be important." It's not a useful concept, unless you like to scream "Look at those idiots!", as Taleb enjoys doing.
Who doesn't like screaming "look at those idiots!"?
Quants can't really get anything "wrong", at least not if they're genuine quants. Everything is/should be couched in terms of uncertainty and a corresponding likelihood of something outside that range occurring. It requires being wrong several times to provide sufficient information that the assumptions underlying the model were wrong and many, many more to help guide you as to what the better assumptions will be.
Why Quants Don’t Know Everything
I've never read "black swans" but my understanding is that it's basically about outliers and mutants -- was it "The Mule" in Heinlein's Foundation? Easy to find after the fact and essentially impossible to include in the model in any useful way. But the key point is no other method or person can tell you the black swan is coming other than the completely useless "a black swan will pop up sooner or later." That leads to decision paralysis.
When I read Black Swan a few years ago I was struck by how much of the book was stuff I had already learned from participating in the skeptical stathead community. There's a lot of stuff in there, for example, about mistrusting narratives (the daily market recaps: "investors were inspired by the morning's employment report").
was it "The Mule" in Heinlein's Foundation?
Ambac and MBIA both failed because they forgot what they really underwrote which was fraud protection. Instead earnest people in their ivory towers (Armonk and the Southern Tip of Manhattan) did mathematical models of loss probability rather than combing though loan files and checking the individuals. The end game of mathematical modeling was CDO squared deals – where the individuals were lost two or even three deep in securitization structures and so there was no way that you were understanding just how corrupt the underlying foundations were.
You must be Registered and Logged In to post comments.
Login to Join (8 members)
Page rendered in 0.4888 seconds, 59 querie(s) executed