Slashdot Mirror
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?"
Don't pick your research area based on profitability or popularity. There are always "hot" areas of research but these things are usually cyclic. Pick something interesting that excites you, and that you can spend the next 4 (or 5 or 6 or 7) years working on.
chillax137
Reading up on the Wikipedia article on this guy...
"Taleb appeared to be vindicated against statisticians in 2008, as he reportedly made a multi-million dollar fortune during the Financial crisis of 2007-2008, a crisis which he attributed to the failure of statistical methods in finance "
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. Which argues not that the events are unpredictable, but rather that his model is better.
As long as it is possible to get paid for the short term results of your crazy bets with other people's money, it barely matters whether the math actually works or not, you are fucked either way.
While I'm all for good mathematical modeling, the notion that our financial problems are caused by bad math is a distraction at best.
There will always be a quant element in finance. I'd guess there will be fewer quants in five years than there were in (say) 2007, but there was definitely an over-supply then. Having said that, most quant jobs don't require you to have had specific training in finance or financial mathematics. For the best firms, its much more about your mathematical and programming abilities. So you definitely don't need to specialize now in finance to become a quant. You could make a case that focusing on AI would be a bigger draw with quant firms. The other advantage of not doing finance now is that it gives you five years to think about whether you want to be in a career where when you get down to it, you rent out your brain to rich people so that they can get richer. I do work as a quant and find it interesting and competitive etc, but ultimately its a money thaang.
... your passion? Are you the kind of person that gets bored even about things you are passionat about? You can study something if you believe it will bring you more money in the future.
What people don't understand is you can LEARN to love doing something by looking at it from another perspective - i.e. take joy in solving problems and strategizing in general and then it shouldn't matter what 'speciality' you go into specifically.
I have worked with companies that implement and use "algorithmic" trading. The real problem is that algorithmic trading doesn't try to beat the market... it tries to beat other algorithmic traders. The idea is to get the trades in before anyone else and there is only so much analysis you can do in a given period of time. Honestly, there's no real analysis to it... it's snap judgments based off a few dozen indicators. It's the equivalent of saying you should guess all C's on standardized tests. On average it works... but you should be shooting for better than average.
While I'm all for good mathematical modeling, the notion that our financial problems are caused by bad math is a distraction at best.
I agree with this statement completely. I live in Canada, and had the misfortune to pick up a Toronto Star newspaper a couple of months back. The front headline was about the Canadian financial mathematician who had created the 'equation that caused the economic meltdown'. I don't recall the specifics but I believe it was used for insurance calculations.
Instead of blaming the idiots that failed to find fault in the formula, or used it without question, they blamed the guy who wrote it. How asinine is this? They gonna come at me with pitchforks and torches when they use my special 1=0 formula for spaceflight and something goes wrong?
Do not be concerned about "restricting" your future options. The applied mathematics in financial mathematics involves all areas of probability, random variables, and stochastic processes. These topics in applied mathematics have wide application in many diverse areas: digital image processing, gambling (e. g., card-counting techniques in the casinos of Las Vegas), computer simulations of warfare outcomes, etc. A degree in financial mathematics will enable you to work in many fields outside finance.
Mathematics, in general, does not restrict anyone's options -- if you are smart and hardworking. Just ask William Perry. He received graduate degrees in "only" mathematics and eventually became Secretary of Defense of the United States. His most recent accomplishment was authoring an essay published in "The Washington Post". In the essay, he advocates using military force to destroy North-Korean military facilities. Mr. Perry is a smart person with the right solution for dealing with North Korea.
In quantum mechanics, you use statistical models because that is the true nature of the underlying physics. In financial analysis, you do not need to use statistics. A borrower ability to pay monthly payments is not some unknown quantum state, but well known (at least to himself or his employer)
It is a fallacy to estimate risk in lending and then charge interest based on this risk. All borrowers that pay on time should get the best rate and those who don't just should be denied the loan.
The only reason not to do this, is lack of information or lack of computing power.
With fast computers and good data all population statistical analysis should be thrown out, and replaced with calculation for each individual and then integrated. This will replace the entire field of mathematics from insurance to lending and investing.
don't cut it off www.mgmbill.org
Studying math with some concrete career in mind is like marrying for money.
If you are going to study math, study it for the love of it, and your own soul.
Your degree will prove useful to you in what ever career you choose for yourself later.
As the island of our knowledge grows, so does the shore of our ignorance.
There was an article in Maclean's (our Newsweek) about a pure-science institute having trouble recruiting in the 90's and 00's because "an entire generation of physicists, chemists and biologists went into Finance instead".
The "quant" maths haven't been proven wrong, exactly; whether heavy mathematical analysis and modelling can make markets more efficient and lower-friction is a separate question from the morals of those managing them.
The trouble is, baroque complexity of financial instruments and transactions was the primary concealment tool that allowed all the lying in the first place - lying to other institutions, to regulators, and certainly to the public that handed over all their dough at low interest because the institutions were so guaranteed-safe; and I suspect, they managed to lie to themselves. Models - especially complex ones with many parameters - have a way of reflecting all the prejudices of (and pressures on) the developers. A big part of the scientific method is about systematically counteracting that. There is way less pressure to counteract if you are not working for open publication after a rigorous peer-review. If your models will be strictest trade secrets, however, your only reviewer is your boss - who may personally become hugely wealthy if the model says X, and not much, if it says Y. Science (as in, "the search for truth") suffers.
If nobody, for a generation or two, will trust an institution with opaquely complex business methods, the market for quants is going to stay "plummeted" for a long time. (It has already plummeted because of the contraction in the whole finance industry - I presume you are even asking about this career only because you think there will again be some job openings in 4 years when you complete a degree.)
I think even 4 years from now, there will still be surplus quants littering the weak market; resumes in the hundreds will flood in for openings.
So, stay away from THAT career, job-wise. There's a crying need for physicists, chemists, and biologists.
I don't completely agree with you. The BA or BS is the new high school diploma. To really optimize your earning potential, get an MA or MS. But yes, the PhD is actually good only if you love what you are working on more than you love the money it can earn you.
In financial analysis, you do not need to use statistics.
You aren't a financial analyst are you? Statistics is required precisely because financial decisions are almost always made with limited information. You can't model most financial activities including risk without statistics coming into play at some point.
A borrower ability to pay monthly payments is not some unknown quantum state, but well known (at least to himself or his employer)
Actually ability to pay in the future IS unknown even to the borrower. Furthermore ability to pay is not and never will be perfectly known to the lender. There is an inherent information asymmetry because the lender can never be sure the borrower isn't hiding something. Furthermore you are leaving out willingness to pay, as well as the fact that life sometimes isn't so kind and circumstances change. People lose their jobs, they invest with Bernard Madoff, their employer turns out to be Enron, etc. These things cannot always be predicted.
The only reason not to do this, is lack of information or lack of computing power.
So you are comfortable providing no insurance to people with a high likelihood of disease? How about losing most/all access to credit when you lose your job. Because that's what happens with perfect information. Be careful about the unintended consequences of perfect information. Even if a perfect model were possible (and it is not) there are many social reasons why we limit how much information is available and how it can be used.
With fast computers and good data all population statistical analysis should be thrown out, and replaced with calculation for each individual and then integrated.
Except that there NEVER is enough data and it is IMPOSSIBLE to perfectly model future events and actions. Even if I concede that you are right and ignore the unintended consequences, what you are proposing is quite literally impossible. The best you can do in many cases is to make a statistical model of likely behavior based on population models and then seek a portfolio to minimize risk for the desired return. Companies use population statistics because they are the best option available.