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Future of Financial Mathematics?
An anonymous reader writes "Nassim Nicholas Taleb, a famous 'Quant,' has long been a strong critic of the use of mathematics and statistics in the financial markets. He has been very vocal in his books The Black Swan and Fooled by Randomness. In his article on edge.org, he says 'My outrage is aimed at the scientist-charlatan putting society at risk using statistical methods. This is similar to iatrogenics, the study of the doctor putting the patient at risk.' After the recent financial crisis, wired.com ran an article titled 'Recipe for Disaster: The Formula That Killed Wall Street' in which the quant David Li and his Gaussian Copula were crucified — we discussed it at the time. Now, I've recently been admitted to a graduate program of good repute in Computational & Applied Mathematics. There is a wide range of subjects in which you can pursue your PhD, one of them being Financial Mathematics. I had a passing interest in it for quite some time. In the current scenario, how advisable it is to pursue a PhD in this topic? What would my options be five years down the line? Will the so-called 'quants' still be wanted by the banks and other financial institutions, or will they turn to more 'non-math' approaches? Would I be better off specializing in less volatile areas of Applied Mathematics? In short, what is the future of Financial Mathematics in light of the current financial crisis?"
But his thesis is that such events are fundamentally unpredictable. If he made a fortune, it means _he_ was able to predict it, well enough to profit for it.
No. His "system" is indeed based on the assumption that such events are unpredictable. That's why he for years bet
simultaneously on a sharp increase and a sharp decrease in stock values - and ran slight daily losses, in the
anticipation and expectation that once such an event inevitably happens, the profits will more than make up for those
losses.
It worked.
He basically had no idea - and didn't care too much - when this (and what this "this" would be) would happen though.
As a former Wall Street trader turned academic, I can agree that the demand will continue to grow for financial mathematicians. The "old school" trader is a former ivy-league athlete who is good at networking and teamwork, but can't do a lick of math. The D.E. Shaw's and hedge funds are crushing the "old school" traders as trading becomes more about speed (esp. algorithmic trading) and liquidity and less about connections. The large banks still have plenty that follow the old mindset, but they are slowly being replaced by the more successful "quant" traders. Granted, the current crisis was caused by over-reliance on models, but that happened because most traders and managers did not understand the models and their limitations. To rectify that, there will be an even greater need for those trained in financial mathematics.
His system is basically arbitrage. We have an algorithm for pricing options (Black-Scholes) that makes an invalid assumption (it uses Gaussian statistics where it shouldn't). This fault was recognized almost as soon as it was published, but people continue to use it anyway, which means they're mis-pricing their options. Black Swan makes money in the long haul because they know big price swings occur more often than the options are priced for, and they buy based on this knowledge. Exploiting market mis-pricings like this one is the essence of arbitrage.
Like classic arbitrage, this only works because a) there is a mismatch between price and real cost [risk] and b) there aren't a lot of players making the same purchase. Change either A or B and Black Swan's strategy will become a money-loser, or at least a break-even.
-JS
Vanity of vanities, all is vanity...