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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?"
... given that people want to see subjective numbers. See:
http://www.amazon.ca/How-Lie-Statistics-Darrell-Huff/dp/0393310728
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
I always found it troubling that one of the computational algorithms they relied on was also the name of where James Bond liked to gamble.
Dual Opteron < $600
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.
I think the future of financial mathematics is that we need more people with a fluent financial math skills. Not people who can just use the formulas and economical models in place, but rather reinvent more logical less speculative models based on harder data then prediction and statistical trends.
Specializing in this particular area and getting a PHD could potentially put you at the forefront of the inevitability that is the need for reform with current economical model.
The people that designed the models (futures market) that are some of the fundamental reasons for the recession, knew what they were doing, they were in fact so affluent with financial mathematics they were able to make it so confusing only somebody with there credentials could see how B.S there models were, you could be that person.
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.
... then do it.
Realistically, Financial Mathematics isn't going to go away. If anything, financial institutions will want to improve the formulas and/or realise their limitations in light of what's been going on, so they're going to need Quants more than ever. Look at Taleb, for example. I seriously doubt he would be railing against his own field if it meant an end to employment opportunities. He seems to be advocating a more realistic assessment of what financial mathematics can do, rather than having a blind faith in the numbers, and for people like that, there's always going to be opportunities.
'If Christ had tweeted the sermon on the mount, it might have lasted until nightfall.' - John Perry Barlow
... 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.
taleb is a self-promoting idiot. he is right that distributions are fat-tailed, and normal distributions are sometimes a poor approximation. (he also did not invent this; he only popularized it.) taleb's solution is stupid: he claims we should ask "wise old men" who somehow know more.
it's like concluding that cars are dangerous, so we better ask stone-age men about their views on transportation.
A new oxymoron...Goes similar to: "one for you, nineteen to me..."
Todos mis movimientos están friamente calculados
If you pick the "Hot Topic" remember so will many others, also it's the "Hot Topic" now, not what will be hot when your looking for a job 3 - 5 years down the line. In IT this frequently seen hot topic "X" earning big money so loads of people train in it and it becomes a cheaper skill and now "Y" is the new hot topic as "X" is now mainstream. Go with what interests you but keep an eye on the horizon for new things and check them out and chase if they interest you. This will give you the best chance of being in the right place with the right skills. My personal opinion is if your interested in then "Financial Mathematics" do it and it may be a good chance of being "Hot", if because of current problems few people will take this problem so 3-5 years down the line this may be a scarce skill
I would call that the future. But you will need a second foundation, to maintain the predicted events unknown for the people that affect those events, or things will not work.
Lots of questions.
There aren't really any viable 'non-math' approaches to finance these days. However if you want to work in finance, you need to have in mind ideas of risk, profit, and so on. 100 years ago when there was some idea of 'any profit is good' you could hire an expert in mining to advise you on which mine stocks to buy, and that would be all you'd need - these days, small percentage profits are actually bad for your business (your market value is better if you make £100 on a £1000 investment compared to £500 on a £10,000 investment, so it can be good to sell off bits of a business that make a profit). So, banks will continue to want quants.
On the other hand, something that isn't well-enough realised is that ultimately what banks are playing with is quite close to a fixed-total-reward game. If you make a huge profit then someone has to be making a huge loss to compensate. What confuses the issue is that various financial tricks - hedging and the like - mean that you're effectively playing not only against every other bank on the planet, but also against future time versions of yourself and your competitors. If you and everyone else are doing too well now, then what you are doing is beating up on the future-time versions of yourselves. Which is fine for a while - those future-time versions can in turn beat up on their futures: but eventually you end up claiming that you hold a certificate now for 100 years of future work, and people refuse to believe in its value. At this point you get an economic collapse.
Short version: you should not go for a PhD just because you think it will increase your salary later. If that's all you want, drop your graduate program and get a job, it will save you several years of misery trying to work on something you're not really interested in. If you are actually passionate about some subject, then that is what you should do a PhD on.
Artificial intelligence is highly applicable to many areas, including finance, and it's very math-intensive. Plus you won't be tying yourself down to one career path.
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.
Is usually disregarded as a joke by people whose opinions matter.
It's less profitable to do science than to teach science. It's less profitable to teach science than it is to fake science. That's why there will still be quants, that's why we're going to keep having these problems, why there is so much competition for faculty positions and why industrial scale research is dead in this country. I don't know the causes, but that seems to be the way it is.
"'My outrage is aimed at the scientist-charlatan putting society at risk using statistical methods. This is similar to global climate change"
Because relying heavily on statistical models is BAD for Wall Street, but GOOD for climate research, and clearly more useful and accurate when used that way.
Or maybe you should use some critical thinking skills and question some of your assumptions instead of frantically trying to conjure a refutation for my irrefutable observation.
The original poster displayed his brilliance in asking for career advice on Slashdot. I'm sure he will go far, as only good advice is given here.
The economics blogs have been talking about this issue for a while. All of the blogs that saw this coming for years (like CalculatedRisk) are very anti-quant.
What we are seeing is a push for the study of behavioral economics, as seen in the popular new book Animal Spirits. This book is being heavily quoted by Obama's Budget Director Peter Orszag.
If you're asking this kind of stuff on slashdot, get out now, while you can. You'll end up wasting 10 years of your life in grad school, forgoing lots of money in the process. You'll also hate yourself and everyone around you.
1) Taleb has a bit of the stopped clock quality about him. Anyone saying "bad things will happen" is bound to be right sooner or later. Plus, his writing is the most self-indulgent wankfest ever.
2) I don't know whether you will choose financial maths or not, but Banks will always need people that can do "fancy" maths. Although some maths is out of favour, high-frequency (algo) trading is currently still popular, and making money.
People used to tell me the same thing about IT after the .COM bust. I stayed in and it has paid off. I like what I do and that made the decision easier. True, there are rough times every now and again but you can say that about almost any profession that pays well. Also, I've been migrating my professional experience to be more financially based over the last 5 years having worked for a bank and now a brokerage firm. People are saying these things now and I can't help think we're going to be short on good financial minds after the dust settles as we were on IT minds in 2004-2008. That said, the future of quants in particular is pretty cemented. I don't see it going anywhere, if anything they are getting more and more sophisticated. Also, I see huge potential in financial trading related data mining. Imagine a database housing all kinds of information about stock performance, the overall economy, politics, etc, then being able to enter parameters and have it rank for you likely good investments given the scenario. For example, assuming the economy is in a deflationary spiral, a democrat is in the white house, the fed rate is close to zero, and the S&P 500 has rallied up 30 percent, etc... What are good investments historically? I doubt there has been an exact case like this in history, but a "google" style close intelligent match can be made. This sort of math/stats/whatever will only grow as well. In short, financial math may morph but will likely not be going away.
I used my skills and turned to the natural sciences. They have really, really big datasets and nobody really knows what is going on there. Salt water intrusion models, various solute transport models are crying out for application. Earthquake modeling and prediction gets big budgets. What is occurring under your feet is really complex.
I did an interview to go into the financial district about 4 years ago but I rejected it after finding out how much they would cripple me. They don't trust anybody. I had no interest in being in that sort of environment. Sure, you make some money.
.
Like astrology or tarot cards? Maybe financial institutions will start 'going with their gut'. I can't see financial institutions that abandon quantitative methods altogether staying competitive.
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?
In short, what is the future of Financial Mathematics in light of the current financial crisis?
Jeez man this is slashdot. Wipe it and install Ubuntu.
While I'm all for good mathematical modeling, the notion that our financial problems are caused by bad math is a distraction at best.
Nevertheless, after a major market crash people will take any scapegoat they can get, and there's always a market for pdeeling hate of "allegedly smart people in their ivory towers, out of touch with reality or the consequences of their actions". People alwa blame those who predicted the crash, those who profitied from the crash, and really anyone with any mechanical trading system.
However, 5 years later no one cares.
Our current financial problems are caused by:
(a) Financial institutions relying on "experts" (who happened to be quants) telling them what they wanted to hear.
(b) A culture that rewarded bets that "make 1% 99% of the time, or lose everything 1% of the time" above all else.
(c) Inidividuals with enough math to understand that you simply cannot afford a house that costs 6 (or 20) times your annual income, no matter what the salesman says!
The whole "black swan" thing is overblown, but in a time where every major bank was betting the company against 1% risks it became profoundly important.
Socialism: a lie told by totalitarians and believed by fools.
There is a glut of "Financial Math" PhDs from second tier programs. You will find a job with this degree, but it will entail making spreadsheets for traders, 15 hour work days, a salary in the 80-110k range and hating yourself. These guys all started out thinking they'd be the next Jim Simons.
If you're in a top tier program, MIT, Stanford, Princeton, you will have a great shot at landing a "sexy" position in hedgefund land. If you're going to a Purdue or Iowa State type school(no offense to anyone in these programs), take a different path. Data Mining IS the next big thing in finance.
Err, that should be "individuals without enough math ..."
Damn slashcode and it's last-century-forum-can't-edit-posts perl spaghetti!
Socialism: a lie told by totalitarians and believed by fools.
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.
If math can be used to tell us which stocks/companies will profit, can't math also tell you which outcome is best for you?
Mathematical modeling of markets assumes that people in markets behave in a manner that can be reflected in smooth continuous mathematical functions.
People don't behave like that. They behave in a boolean fashion.
An example of a boolean function would be something like
r= ( gaussian random number between 0 and 1)
if ( marketConfidence gt 1 ) { //bubble .95) { //topping .05 + ( .1 * r) //crash
return yesterday + (.1 * r)
} else if (marketConfidence lt 1 and marketConfidence gt
return yesterday -
}
else {
return yesterday - (.5 * r)
}
When the market is going up, it keeps going up regularly. When the market confidence is broken thend the market is in crash mode, and the markets go down regularly and pessimism rules the day. The value of "market confidence" is a complicated human variable that switches from on to off at some point . The modelers had only seen the first case for the last 30 years so they only modeled for that and now assume that the current activity is a 10 standard deviations outside the norm and should only occur once every trillion years. That's because they refuse to admit that they made the error of leaving the crash scenario out of their use case.
Our current financial problems are caused by: [...]
You missed the big one: that when something is overvalued, its price tends (in the long run) to correct back down to being undervalued before rising again to somewhere in the region of the correct price, and that these over/underestimates of correct value are cyclic and unavoidable, because they are essentially part of human nature. And that the upshot of the 1990s and early 2000s is that both the housing market and the stock market became a long way overvalued.
OK, so that's a contentious theory that many economists dismiss out-of-hand, but you have to admit it has a certain appeal in explaining the current situation.
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
Elementary particles also behave in a quantized fashion, not continuous. If you have a lot of them, their overall activity can be modeled quite successfully using continuous mathematics.
While I'm all for good mathematical modeling, the notion that our financial problems are caused by bad math is a distraction at best.
Do not arouse the wrath of the great and powerful Oz!
Pay no attention to that man behind the curtain.
And then there is math and there is math. If I remember correctly, stats are exquisitely nuanced thing, mostly dealing minute details of the underlying assumptions. One thing to master the mechanical math underlying stat, actual mapping to an application in social context is a black magic.
Fuck systemd. Fuck Redhat. Fuck Soylent, too. Wait, scratch the last one.
And for god's sake, smoke weed.
Everything is better with a bag of weed.
You are welcome on my lawn.
I know it's slashdot but read his books before you sum up his crusade against statistics. Taleb is not anti-math. Instead he points out that we use a model (normal distribution) that doesn't properly apply in this arena and that the assumption that it does makes our further analysis based on that model dangerous. Also for those that are asking why he can make money in an arena he claims is unpredictable it's because the payoff for making a correct "bet" is way out of scale to the amount risked. So by making a bunch of little bets on these incredibly rare out of scale events (aka Black Swans) he waits for one to hit and provide a return on investment that is out of whack with the amount he risked. He can't predict the event but he is predicting that the return will result in a massive payback. Then he's betting on a bunch of these events. Also with some fundamental analysis (as opposed to quantitative analysis) he is able to find likely candidates for these black swans.
I'm not even sure I agree with him or don't but get his argument more or less right before you start making random comments. Also his success or failure isn't an indication he's right otherwise all those guys who made millions then lost it all were "right" at least up until they weren't. I tend to think there is more structured thinking behind Mr. Taleb's work and I respect it but success in one instance is not vindication...that's called luck as often as not.
if the worry is that people don't trust the current models, wouldn't now be the perfect time to pursue studies in that field? Who wants to learn a bunch of things that people are certain of?
-- 'The' Lord and Master Bitman On High, Master Of All
What's a gaussian random number between 0 and 1? What probability density function does it have?
WTF? How did you get from studying mathematics to nuking North Korea? That was beautiful old-skool trolling, man :)
You should indeed be shooting for better than average (haha).
But in the real world if you make money just slighter greater than 50% of the time (let's say 51%), if the volume is large enough then guess what: you make a whole lotta money.
It's a viable strategy.
You have to be a psychopath though ...
The guys getting the multi million dollar bonuses weren't playing the market with their own money. Don't do that, nobody (except maybe Warren Buffet) can beat the market in the long term.
The talent of the big money guys is that they can convince people to let them play with their money and get a big commission no matter whether or not their customers come out ahead.
We used to talk about 'social engineering' to refer to getting lusers to give us their passwords. The 'financial wizards' are just a lot better than the average hacker at social engineering.
So ... learn to convince people that you are the greatest financial modeler of all time. Make sure they pay you really big bucks. Don't spend the money on high living and you will be rich my son. Just don't do anything that's actually illegal like Bernie Madoff. You don't have to.
There will be a continuing need for mathematical analysis in finance. They will continue to need to assess risk and model alternatives. There will also continue to be people who try to get rich quick and want analysis to show that their approach doesn't have the risks that it actually does. So the need for good analysts will continue, as will the risks that you'll be pushed to use your analysis to support incorrect arguments. But that's going to be true of anyone in finance, quantitative or not. It's an area that is inherently subject to high pressures to do irresponsible things.
Focus on math that has application to the widest range of things that interest you. It's not a single purpose tool. Sophisticated data analysis has wide usage. Of course, I'm prejudiced in that after many years of analysis of seismic data, I'm now launching into an intensive effort in using statistics on other types of oil industry data using R.
However, it's more important to remember that a doctorate is different from a masters. The real purpose of the Phd is learning how to teach yourself.
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.
Financial mathematics didn't become popular because it isn't effective, and large financial institutions don't get that way for being financially ineffective. They might have to change the job titles to appease minority shareholders, but there's no way they'll get rid of mathematicians who make money with magic.
The real problem wasn't that the math was done, it's that a lot of people were doing it poorly and with too little oversight because, as with any fad, people forget that they know better and trust every crackpot with a grain of insight. Everything in moderation (including moderation).
Try not to take me more seriously than I take myself.
It is the dumbass MBA managers that do not understand the math that is the problem.
Excuse me, but please get off my Pennisetum Clandestinum, eh!
Well, you need both. If nobody understands math, then you'll just get an infinitely long bubble of rising debt... It's those with enough math who ultimately refuse to keep going.
Bubbles bursting don't have to cause a wider crises, and I'd argue that they ususally don't. Markets seem to almost continuously pick some sector to overvalue, at the expense of the now-unfashionable previously-overvalued sector. The damage is usually limited to those who fell for it.
It only takes a little bit of conservative sense to ask "wait, what happens in the 1% worst case - do we at least survive?" You know, the sort of question every competant engineer asks every day. It's not that the large banks misjudged how likely it was for real-estate prices to fall, so much as they refused to consider it at all. So now instead of a few large idiot banks being allowed to fail, we're stuck because they sold insurace against just this situation to a ton of other banks, and didn't have the capital to back it up.
Socialism: a lie told by totalitarians and believed by fools.
..for a Ph.D. thesis? How can Mathematics be applied to safely invest without damaging Society?
Well, first, NNT is "outraged" at the "inappropriate use" of Quantitative Analysis (according to his books and the articles I've read), not the "utility" of Quantitative Analysis. The reality is that investments' values fluctuate. The role of the Mathematician is to limit the losses, therefore the risks, involved in investing. This is a legitimate role. If you had been working for 45 years and were about to retire, wouldn't you want to know that your retirement funds were as safe as they could be?
Part of the problem comes from the fact that investment value is affected by information other than the worthiness of the investment. This value activity has created an analytical branch of its own, and subsequent buy and sell orders are based on the activity rather than the underlying fundamentals. NNT's argument in "The Black Swan" is based on the idea that since these random events are indeed "random", by definition they are unknowable, unpredictable and un-assessable. So, when these events occur, no contingency plan behaves correctly. Keep in mind, that this is only a problem if the event(s) affect the investment values, or the perception of investment values, in a negative way. Unfortunately, the use of these QA tools creates an aberration in perception, and may be creating it's own perception, and this "perception" may not conform to "reality", therefore leading to aberrant behavior based on an aberrant strategy. (Oooh, the stock market has become psychotic...) Skynet takes over the market and tries to wipe out the humans because its programming tells it that is what humans want.
Mathematics can tell us a lot about what reality "is", and there is a lot of room for a creative Mathematician to alleviate the downside and limits of financial decision-making. I say, if you like Math and this is an area that interests you, go for it. Try to be the best. Be creative and innovative instead of being a sheep.
Malcolm Gladwell's book, "Outliers" deals with somewhat the same problem in a different domain, and Ayers, "Super Crunchers" gives a good layman's view of how well Math can work for us in certain areas. Graham and Dodd, "Portfolio Analysis" may still be the best overall book touching on your field. Benjamin Graham's, "The Intelligent Investor" may still be the best basic investment book. If you want to get out there a little ways, try Prector's, "The Elliott Wave Theory." I had a friend working at Lockheed in Artificial Intelligence, who was responsible for the computer analysis of the market for Prector's newsletter. Every year they would run a 3-month test of the application, and it would consistently make money well in excess of the inflation rate (even in '89). It's been 15 years since I talked to him, and I have no idea how well it's done in the last few years, but the field seems fascinating.
I say go for it, and good luck!
"The mind works quicker than you think!"
Just glorified accounting. So find something real to study during your post-grad years.
That said, actually making money trading in many circumstances requires solid mathematical background and disciplined application of scientific method. The other alternatives are being a member of old boys club or go Madoff style.
Trading 'from the gutts' now is just asking to be ripped of by robots.
I think that the main thing that people miss in this whole debacle is the need for change.
When you're working on algorithms that involve variables like these, it needs to be an on-going process. There is no equation that will remain constant (statistically and/or theoretically) for the foreseeable future. What needs to happen is people with the practical background in the field and the people with the technical background in the program to come together and see what is and isn't working on a regular basis. Finding both in one is rare, but not non-existent.
The problem was that the people who should have known better (with the practical background) should have known better than to trust what the computer was telling them. There should have been someone, lets say the big-man or higher up in the financial mathematics field, who said: "Hey! This model doesn't work anymore."
The fact is that the model itself wasn't broken at the time, but the system was. If you have a system in place which reviews and systematically tests the formulas being used in the field, it would have made a significant difference in the outcome that we're dealing with now.
There should be no feed x,y,z into the black box and the results are law.
I say don't drink and drive, you might spill your drink. Before you get behind the wheel just stop and think.
Having studied and worked in a range of topics from mathematical control theory (one of whose subtopics is portfolio optimization), i would like to point out that a PhD in a field does not necessarily preclude you from working in another field. The main use of a PhD is to inculcate a precise and keen method of thinking about a problem. Ergo, dont go into financial mathematics just because you think it pays well, and you should understand that even if you choose a PhD in financial mathematics, the skills that you learn: optimization, stochastic calculus, stochastic control, are all VERY transferable to a range of problem areas.
You quoted NNT, so you should understand that his methods are to hedge his bets such that he ends up knowing his worst acceptable loss possible in advance. Perhaps you should do the same, do what you love and keep your mind open about the range of fields which can use your skills.
Also, it is unlikely that the buying , creating and selling of financial instruments is going to go away soon. Every big company likes to hedge their risks and they would always need people who are willing to sell them such an insurance policy.
SIX TIMES NOW, business has picked up, and pulled us out of recession when the business tax _rates_ were lowered. The third time it was tried it was called "Reganomics".
It's clear this worked. You're about to see what I saw, living though the Jimmy Carter Administration, where all taxes are high, and the government looks around wondering:
1. Why aren't we getting more money?
2. How long will this recession last?
This isn't pie-in-the-sky. You can't spend your way into prosperity. It's *never* worked.
So can we stop trying it, before the entitlements literally CRUSH this economy?
Derivatives shall remain in Finance, and to really understand them, you will need to understand the maths behind it.
There is no doubt that some quants used over simplified approaches (Gaussian Copula, assuming historic correlation) to price CDO's, and it helped amplify the lending bubble.
But to just blame maths is distortion of facts. This was not the only cause of current crisis.
There were numerous points of failures:
1) Low interest rates from Fed (with Greenspan's policies)
2) Over borrowing by the US and UK consumers.
3) Failure of management of the banks to really understand the maths properly. (If others are making money doing this, surely we must as well. Often overruling the concerns raised by quants and risk professionals for sake of profit)
4) Failure of various fund managers etc who were buying these CDO's to understand the maths.
In other words, economists, MBA types, were all involved. (and actually screwed up more)
So, regardless of the blame game, (in which more media savvy MBA's have been winning against the nedy quants) it can go both ways.
Maybe, the future finance professional (at ALL levels, esp senior levels) will NEED to be multi dimensional and will require Maths, Finance and IT skills.
"how advisable it is to pursue a PhD in this topic?" What is your tolerance level for torches and pitchforks ?
As a current Ph.D. student in statistics, I would say YES!!! The errors do not come from the statistics itself (the theory is very rigorously worked out from assumptions) or from deciding to use statistical modeling in a given situation (the problems almost always come when people incorrectly think a process is non-random), but from people applying models incorrectly. In other words, what we need are more people who know the complexity and assumptions behind these, not more statistical monkeys who treat statistical modeling like a tweakable black box spitting out answers.
In my viewpoint, the problems and the current financial process were not surprising at all. We have a saying that "all models are wrong; some models are useful", and the problems have no idea how useful it is and where it could go wrong, given the assumptions they work under.
--
Those of us clean up after you think differently.
Does having a witty signature really indicate normality?
http://creditspectrum.blogspot.com/2008/12/state-of-financial-engineering.html#0
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.
Although it's become trendy to blame the Gaussian Copula function for the collapse of the bond market, I wouldn't be so quick to judge.
The function appears to have allowed investors to "re-package" risky debts to appear less risky by the system that was used to rate the bonds, which (very predictably) blew up in everybody's face when it came time to pay off the loans.
However, the "white-hat/black-hat" argument comes to mind. Although the exploit was bad, it seems as though somebody should have stepped in and fixed the bond rating system so that it could no longer be abused and manipulated.
-- If you try to fail and succeed, which have you done? - Uli's moose
Don't go getting a masters or PhD if money is the objective. I see WAY too many people who are just hoop jumpers. They are going on to get a higher degree to get a better job. Some of these people get their PhD and then do post doc work not because there's still research they want to do but because they still can't get the job they want. Never occurs to them maybe education isn't the problem, it might be their complete lack of problem solving skills or the like.
A masters and more so PhD are NOT for everyone, they are not even for most people. They are supposed to be when you really want to specialize in an area and do new research on the topic. If that isn't what you are about, then don't go for it. Unless you are going in to a field that has a specific minimum, and most don't past a bachelors, then there's no reason to go for a higher degree just for its own sake.
Any time a friend or family member talks about wanting to get a masters my question for them is always: Why? Not as a petulant "Don't do it," thing but as a challenge. I want them to give me the reason they want to do it. If they can't, or the reason is "To make more money," then I'm going to tell them it is a bad idea. If the reason is "Because this interests and excites me," then I think it is a great idea, even if there isn't going to be a return on the money spent. Education for the sake of learning about what you want is wonderful. Just make sure that is really the reason you are doing it.
The problem is that there is no such thing as a model for an individual that you can integrate. The point of a black swan is that nobody saw it company because it is intrinsically unpredictable. You are correct that the reason for this is a lack of information. But neither you, nor me, nor the CIA, or all the quants on Wall Street can obtain the information you would need to predict at the level needed. This is one of Taleb's key points. Hari Seldon only exists in fiction.
Think global, act loco
after all, the y axis is appropriately named;-) the cost of a good is plotted on the real axis, the market price is the resultant, theta is a measure of perception...
(Mathematical) models are as good as the assumptions they are based on. Now, who had to make sure the assumptions held, that's a different story. I don't think it was the quants.
Using a random decision within a mutually accepted "safe" range, and allocating smaller amounts of resources to "unsafe" ranges so it will be impossible to end up being overextended can be seen as an equivalent of "co-operation" in prisoner dilemma. On the other hand, trying to over-allocate resources that player (or other players) believe to be "unsafe" while trying to reduce risk with things that may or may not be reliable can be seen as an equivalent of "defection" in the same terms.
So yes, a conscious effort to choose random decisions to increase the amount of co-operation can be a better strategy as long as it does not suck resources completely away from more risky but vital for development parts of the market. You still have to evaluate risk, the difference is that it's OK to make decisions collectively instead of trying to find less risky moves that others are not aware of (and subject yourself to unpredictable and therefore usually underestimated risk of being wrong).
Contrary to the popular belief, there indeed is no God.
This was true until about 2002. Before then you could make nice money buying at the close if the close is up, shorting if it is down. Since then the opposite has been true, but less strongly (based on raw NASDAQ COMPOSITE data ^IXIC from yahoo finance).
My guess is that the increased use of computers by day traders and others has fundamentally changed the way the market operates in the short term.
Contribute to civilization: ari.aynrand.org/donate
In the insurance world, they use the beta distribution for probability because the curve can be shaped pretty much any old which way.
This is my sig.
Hmmm, I think connections are worth way more than formulas, so much so that one could say formulas are for people who do not have connections. Having a drink with one guy who just reviewed the innards of a big company and found them interesting in some way can produce immediate profitability.
This is my sig.
If you find financial math interesting, go for it. The market for good "quants" will be there forever. It will go up and down, but finance firms want to estimate the future as well as they can, and that means plenty of "quant" jobs in the future. The more important thing about the Ph.D. is finding a subject that will motivate you after everyone you know is completely bored by your topic.
-John Van Voorhis
My advice is to go ahead with studying what interests you. Taleb is not a quant. In fact, he is not much of an investor because he lost his job due to poor investment returns. Hence his bitter writing about others. There are many job openings for people that can model investment data in the US and worldwide. Modern investing requires detailed understanding of mathematical models.
Here is a book that shows the field of market prediction mathematics will not directly make a person money by playing the market.
But on the other hand, it is an area of mathematical study that involves the most exciting developments in applied mathematics in the last 50 years, namely chaos and fractal theory.
"The (Mis)Behavior of Markets. A Fractal View of Risk, Ruin, and Reward" by Benoit Mandelbrot and Richard L. Hudson, Basic Books, New York, c. 2004.
Smash capitalism through international socialist revolution!
1. All work requires energy. Energy is the capacity for doing work.
2. Oil and gas production is at or near peak, and we get 85% of our energy from it, and Coal is not a "good idea".
3. Therefore: our capacity to do work will decline if efficiency doesn't meet decline rates, and work can't grow if the efficiencies don't exceed decline rates.
4. decline rates in major oil fields are extreme - The North Sea, Mexico's Cantarell, are both in double digit decline rates. The USA has been declining since 1970. The only places left are mostly i nthe middle east, and some of them are at or near peak, and their production cannot be increased to match the declines elsewhere.
Therefore, a loan, which is a claim on future labour, is not a "great idea" when we know that the sum total of work can only decline from present levels, if efficiencies are not implemented immediately, and decisively.
From this, and the excesses of the system at the peak of energy development, the logical direction of survival would be for the USA to:
1. Nationalise the banks, immediately.
2. By nationalising the banks the USgov repudiates the bank debt.
3. Disband the Federal Reserve. The USgov will be responsible for its money supply.
4. Nationalise USA Health Care. Face facts: This whole nonsense about âoeyour health care decisions should be between you and your doctorâ is BS. You know who makes your health care decisions? The insurance company. The USgov should eat the health care industry directly (on the one end) and get really pretty damn stiff with fat ass Americans on the other end.
5. Gas should be USD$5 gallon. Minimum. If gas is cheaper than that due to over production or demand destruction, then the remainder goes directly into alternative energy systems.
6. Car makers should go chap 11, and restructure under strict supervision. There should a be a refocusing of vehicles away from the armoured chariat and more towards the beefed up bicycle/tricycle. 7. The USA must abandon its Empire. The Pentagon must cut its budget by 50% a year until it is the size of the Chinese rate, or less, of spending. American Troops must be brought home, decomissioned, and retrained for the powerdown.
8. Crash Infrastructure improvements geared around livable homes and communities worth caring about. LOTS of insulation. Lots of geothermal, etc..
Now if you see an opening for financial mathematics in that, you're better than I am.
I don't think "financial mathematics" has much of a future. Any more than public relations or drama therapy.
Shoes for Industry. Shoes for the Dead.
The growth field will be regulation, and that needs to be quantified, too.
Li tried to point out the limitations of the Gaussian copula, but was thoroughly and pointedly ignored. It seems the bankers were perfectly happy to rake in the cash today in exchange for other people suffering tomorrow (so what's new?).
It may be worth considering that the worst outcome would be to similarly come up with a useful tool and have bankers promptly mis-use it horribly. Then you get to know for several years that ruin is inevitably coming and your invention is being abused to pave the road. You further know that however unfairly, those same bankers will happily blame the whole thing on you and your formula.
I wonder if that's why Li went back to China in 2008?
No shit, right?! It seemed completely rational until he dropped a bomb on north korea.
Almost want someone to mod it funny for "craziest sentence out of left field".
Whether David Li's Gaussian Copula formula was crucified or not in some-not-so-bright-journalist's 'Recipe for Disaster: The Formula That Killed Wall Street', the fact is that it works, and Wall Street and other banks made tons of money by utilizing more efficient risk-management strategies produced by quants & statistics. Market-crashes happen every ten years or so, quants or no quants (and Great Depression happened even before the term financial mathematics was crafted). So, to answer the question, the Wall Street will jump back onto quants right after this is over, and there is great future in applying statistics to market modeling. Never ever banks & fin. inst. will get back to non-math approaches. However, given you asked this question, I think it'd be best if you don't take on this line of pursuit for your career - or I fear you'll be among those who will lead world finances to another screwup by mindlesly using formulas that you don't understand in situations they should not be used.
Hate to break this to you, dude, but writing SQL queries and tuning their performance is a really complex topic. SQL is basically a programming language that throws out Turing completeness in exchange for guaranteed termination--but the grammar of conditions and scalar expressions that it supports is every bit as complex as any programming language.
And then understanding how your queries are optimized and executed gets hairy--syntactic transformations based on relational algebra equivalences, the multitude of table access paths and join methods, the way the eligibility of each of those depends on join conditions (equijoins, semijoins), the process of generating candidate execution plans and estimating their cost based on statistics about the data and the hardware, etc. The relationship between the SQL query you write and what the computer actually does is a lot more indirect and complex than the relationship between the C program you write and what the computer does. There are plenty of people who understand the latter very well, and are hopelessly lost about the former.
And don't get me started about all those programmers who look down on SQL as easy stuff, when they suffer from pretty basic ignorance about it. Like, programmers who can't tell you how many joins their query will need (much less what kind of joins!), don't know how to write subqueries, don't know that EXISTS exists, don't know about the CASE function (or know about CASE but think they should index the columns mentioned in the search clauses), and so on.
Are you adequate?
I think it's better to put it in more general terms: make a bet where if the markets go down sharply, you win money proportional to the decline, but if they stay flat or go up, you only suffer a small, constant loss. Basically, a long put.
Are you adequate?
The salaries for good financial mathematicians will be boosted to new levels. Good risk departments predicted the risks which hit the banks right now. But the competition may be stronger. Idiots studying economics will not be allowed to make big decisions without getting a mathematicians signature.
(this comment was written by a physicist with some experience with extremal value distrubutions and nonlinear physics, who has no interest in money, but who nevertheless watched with a growing peculiarity buch of idiots who were using tools far beyond their intellect, created for them by mathematicians. Even simple questions aboud bondary conditions in the underlying PDEs usually remained unanswered.)
People will always use statistical models, there's potentially, too much money in it. In a world where no-one else uses such models the one person who does wins most of the time. To use an analogy:
Psychohistory works, but only if the Second Foundation knows it and the entire galaxy remains ignorant. As soon as people are aware of the predictions their behavior is changed by foreknowledge.
If everyone becomes aware of the model,you have to build a higher level model which predicts the original model, and how people will react to it. Thusly we wind up in an arms race to build ever higher level models, because the person with the highest won wins (most of the time.) Thusly we end up in with an insane stack of cards.
Ludwig von Mises and others like him have known of the fallacy in using statistics/history when understanding economic theory. Read 'Human Action' and you'll find that Mises devotes a lot of time addressing this fallacy.
I don't think it would be wise to become a PhD quant because most probably there is going to be a worldwide communist revolution as a result of the economic crisis and not only there won't be any banks to employ you but currency and finance will be outlawed too so there will be no need for financial mathematics at all.
Don't be constrained by your degree in applying for work. The degree does not define you. You and your interests define you. If the world does not appreciate the contribution you could make in one field. Deny the world the benefits of your contribution and work in another field. If you do it as a hobby, you can do it your way. If you limit yourself to one field, you are simultaneously dis-empowering yourself. Do what you love while realizing you can love many fields since the problem solving methods fit many fields and there are interesting problems in all fields. Bring your Physics perspective to a new field. I find that programming allows me to bridge multiple fields. I get to meet new and interesting people who are experts in their domain. Personally, I bounce around a lot and have been told I have an interesting resume by a recruiter. Just for kicks, apply for 25 jobs in quantitative financial analysis and see what happens. If they criticize you for lack of experience, calmly state that you felt the industry could benefit from your fresh perspective.
The world doesn't owe you shit, but it will shit on you so step lively.
I think its important to note that Nicholas does not say that Financial Maths is hocus pocus - he says that the way banks put all their faith in them puts them at risk. He has an excellent analogy about a pilot flying a plane with instruments that are 99% correct - he reckons, this pilot must also look out of the window every once in a while and not fly blindly by his instruments.
I work at a bank, and there is certainly no talk about dropping or revising the use of financial maths. In fact, the result of this crises will probably be more strict controls over the maths used by traders, which will probably make a "quant" even more valuable as a tradesman.
The article is a bit long. If anyone feels like doing a synopsis please do: http://synop.it/
I am an industrial mathematician in Finance and this is not a rant. Mathematical finance pays well. Not as well as people say (ususally) and those jobs that do pay are generally hellish in terms of hours and stress. That said there is no comparing pay in finance and, say, topology. That is part of why I entered the field in 1998. Now the landscape is different. There are thousands of new quants entering the field every year, and the university departments make money on the tuition of almost every one, especially the math departments. The best people will be paid very well. However the long term outlook for the quant labor market has negative pressure on wages becuase of increasing supply and diminishing demand. Alphas are going down for quant strategies. There is lots of data available to support this. That means less money coming in. The game is getting harder. I would look at areas to look at large data sets such as Bayesian methods or times series or other computational mathematics. The revolution in biology is going to make some very hard math problems, as will the enormous databases that are popping up all over. Check out Hal Varian for a taste of what I mean. http://freakonomics.blogs.nytimes.com/2008/02/25/hal-varian-answers-your-questions/ By the way, take numerical analysis. The course sucked for me, but it has proved so helpful in so many situations it is not even funny. Best of luck, Edwastaken
factor in Murphey's law...
create a limit that measures the inaccuracy of the graduated copula
and as inaccuracy -> infinity, odds of a market crash -> certainty.
Theory of probability works great in physics, electronics, biology, etc, etc but does not work in economics! Great conclusion!!! I'm in shock!
If someone at finances steals billions and billions, builds pyramids and so on, puts world on wars and loose mega-billions, and all this nasty things are covered by US government, mathematics will not work certainly.
geek or not
In honor of Mr Taleb, I propose they be known collectively as The Taleban
Enjoy your time on earth.
is this from someone who already passed away, following a retaliation strike from NK?
Can I put a spell on those who can't spell?
Your wheels are loose and they're losing their grip, good you're there.
...are well-intentioned but incorrect.
I have a PhD in physics but have been a quant for 5 years. Generally speaking, derivatives are like exotic games in a casino, and there are people who know what they're doing and realize what they stand to gain (and lose), and then there are those who have no business whatsoever at that table. I believe what Taleb is really criticizing (and if not, it's what he should be criticizing) is the blind use of derivatives in the financial markets. They don't let butchers perform open-heart surgery, do they? (ed : only the ones with medical degress) Anyway, as far job prospects in finance go, obviously right now is not a good time, but in a few years it will pick up because 1) memories are fleeting and 2) people are greedy
If you have done a PhD in any computational/ mathematical subject from a respectable university (and from your post it sounds like Caltech), then you are extremely well-positioned to become a quant later on. If you so choose to take up this dark path, you can (and probably should) study a little of the financial maths like black-scholes and hedging and martingale theory before sending out the CV, but to dedicate your entire post-graduate path to this field - unless you have a passion for it - is a mistake.
And here's a general piece of advice on PhDs and careers and the like : do a PhD in something that really interests you, because that will keep you going when things get really horrible. However, it's also desirable that you develop a set of practical skills that can be leveraged into an industrial setting if in the end the academic world turns your belly (e.g., in finance this would be strong programming and numerical skills, as well as methods for solving stochastic equations).
good luck
Bubbles bursting don't have to cause a wider crises, and I'd argue that they ususally don't. Markets seem to almost continuously pick some sector to overvalue, at the expense of the now-unfashionable previously-overvalued sector. The damage is usually limited to those who fell for it.
No, the problem here though is that _everything_ became overvalued and remained overvalued for a long period.
The problem is with improper use by those who use the (admittedly) sharp tools provides by quantitative financial modelers. One might intuitively expect bankers who are being paid six to seven-figure salaries to be able to see through and beyond financial models based only on statistical correlations, but alas, another hope dashed. Apparently the capacity for independent thought is not for sale even at those salary levels.
In the case of David Li, his copula is merely a convenient and computationally tractable way of doing calculations with multivariate Gaussian distributions. The problem is that the covariance matrix that describes the correlation of risks was calibrated on the *past* 10 years. Which is just fine if your multi-variate time-series is stationary. But misleading if there is e.g. a sudden regime change in your time-series.
Now the past 10 years were years of growth, wealth, and plenty of borrowing and the US of A are a rich country and its citizens can borrow an awful lot before there is any problem.
As a result all the banks saw a _low_ correlation between person X being unable to service his mortgage and person Y being able to service his mortgage. Therefore the road to financial safety for mortgage lenders was: make your portfolio big and make it varied. As big as possible and as varied as possible. And then make it even bigger. Because the Law of Large Numbers guarantees that the probability that large numbers of your clients will be unable to pay is quite small.How small? Well ... you can calculate that to a nicety using 10 years worth of financial data. And you can budget for that. Great huh? The only thing you need to assume is that next year will be just like the past 10 years.
Any bank manager who wasn't prepared to sail as close to the wind as the financial models indicate was possible was sacked by his boss for not being result-oriented enough, which is a fairly broad hint for the remainder.
Unfortunately the correlation matrix, on which all those risk calculations hinge, is not a constant. It may change if massive numbers of borrowers suddenly hit their collective borrowing limits. When *that* happens, the probability of person X being unable to service his mortgage suddenly becomes a reasonably accurate predictor of person Y not being able to pay up in time. And the amount of risk you run with your huge mortgage portfolio suddenly increases dramatically. Which is exactly what happened.
Suddenly all the correlations went up, and banks found themselves sitting on enormous amounts of mortgage-backed loans which they couldn't even assess the risk of because they didn't really know the new correlation matrix.
This had two effects: first of all no bank would be happy to lend to another anymore because if it was hard to assess one's own risk position, it was impossible to assess that of another bank. So lending to any other bank was basically taking an unknown risk. Which they either refused to do (if they were short of cash themselves) or which they charged much higher rates for. That was the birth of the liquidity crisis.
The second effect was the use of "slicing" the mortgage portfolio. That works as follows. You take a mortgage portfolio and you (hypothetically) sort its elements by probability of defaulting. Then you package the 10% highest risks as a new investment product. And then you sell it. You (honestly) tell your clients that it's high risk (and you tell them how high the risk is), but you offer a commensurate yield. Repeat that for the next 10% and so forth. In principle this is a fine idea which make
If you've got a good (meaning clear and predictive) mathematical model and it turns out wrong, then that's also useful to know. If you've got intuitions and stories and pictures of data which seem good when explained by the right person, and those turn out wrong--well, that could just be that you were misunderstanding, couldn't it? Can you prove that the approach was wrong when it's fuzzy?
Math will always have an important place in finance, because it can be understood and judged. The alternative way to judge results is by looking at who makes the most money, but even that can be very noisy and misleading at times.
The question isn't whether math will be important in finance. It's (a) whether the particular tools/models commonly used will change a little or a lot, and (b) whether the people using the models will have the understanding and wisdom to apply them only as appropriate.
(a) means if you want to be involved in financial math, try to be broad enough that very different approaches won't be completely alien to you, and (b) means you should study some economics / psychology / business so you can connect the models to the reality.
My suggestion to the submitter is to try a more honorable career, like record-company executive or drug-dealer.
I recommend prostitution.
At least you're being honest and up front about screwing your customers for money.
In my opinion, modelling such a non-linear dynamic system as market is not possible. By not possible I mean , one will make so many simplifications and neglections on nonlinearities that the final linear (1st, second or higher order) system will be almost useless. something like research made on gases (but calculated only in vacuum ;) )
the biggest problem with those models is that they can only be used by mathematicians , and only mathematicians know where's the border of believing that if we have matrix output A , we obtain matrix output B , a this means .... etc .etc..
Credit requires growth to function. People see an initial increase in any random market, borrow and invest in that market, so it booms a little more, paying back the original investors. There is a self reinforcing cycle of borrowing and investing until the resulting exponential growth hits some limit, and the last people into the market lose their shirts when the pyramid (and it IS a pyramid scheme) fails.
This is basically how I manage my portfolio and why I'm 5% up when just about everyone else is 50% down. Look at the exponential bubble and step out when it's getting to it's limit. Timing the completely inevitable crash is crucial of course, but it's mostly a matter of looking at the flow of new loans going into a sector compared with the current debt and interest load. You have to understand what money is, it's amazing how few people do. I recommend you read Mises theory of money and credit, he basically had it pinned 100 years ago.
The essential nature of a free market is still unpredictability.
The toss of one coin is unpredictable. The toss of 100 coins is easy to predict with some certainty. Putting money in any one stock is gambling, putting money in 200 is investing. Couple that with an understanding of what money actually is and you can avoid the worst the governments and banks can do, or even take advantage of the manipulation.
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I'd say the opportunity is more limited in investment banking, but there is still demand on the asset management side. Investment banks are the ones that created the now infamous CDOs and other complex mortgage-related structures.
Asset management using quants is done by mostly by hedge funds but also some traditional (a.k.a. "long only") investment managers (think mutual fund managers). Note that investment banks have traditionally traded their own money (known as proprietary trading), but that's happening less now because their a) many of their traders have done terribly and b) they don't have the same volume of money to trade.
To add one more wrinkle, most sizable investment banks also have asset management units where they take client money and invest/trade it for them, and/or create hedge fund structures/strategies to do the same.
I still see a lot of new job postings for PhD math people by asset managers, and occasionally investment banks. Try theladders.com or eFinancialCareers.com to get a flavor for what's out there. Another broad site that works very well for jobs if you can figure out the right search terms is indeed.com.
As another poster said, just because you specialize in one field doesn't mean you can't do something else with it. Lots of the math applies different places. If you really wanted to go whole hog you could do a math PhD in anything and then get an MBA with a finance concentration.
Note that Taleb is a smart guy, but he does have a product he's selling. He has many valid points, but there are firms still making millions or even billions of dollars using the statistical models he vilifies. Over the longer term there's too much skew/kurtosis/etc. from the human element (finance is a social science), but over shorter time periods there are lots of situations that work just fine with the kind of models you'd be able to build. Just always beware of leverage!
Really? I believe the first failure of the financial industry is to be short sighted.
I don't have a PhD in this subject but I do have a masters and also am a certified accountant. I work with "quants" rather often so I'm pretty familiar with the options and lifestyle.
In the current scenario, how advisable it is to pursue a PhD in this topic?
If you've got the talent, very advisable - provided you are interested in working as an analyst. You want you also have to decide how much work life balance you want because it tends to tilt heavily out of balance the closer you get to Wall Street.
Regarding jobs, financial firms tend to over-hire and over-fire. Don't count on working for one firm for 20 years. Competition for jobs can be fierce during down times (like now) but they'll throw money at you (if you've got the ability) during bull markets.
What would my options be five years down the line?
Working as an analyst in a financial or large firm, (accounting, investment banking, market analyst, hedge funds, etc) or possibly management if you have the inclination or perhaps working in academia. You could become a controller, banker, consultant or work down a CFO path. The options are pretty decent. The lifestyle? Well, that varies considerably but odds are you'll be working a lot of hours.
Will the so-called 'quants' still be wanted by the banks and other financial institutions, or will they turn to more 'non-math' approaches?
Yes. No question whatsoever. Math is not going away from financial analysis EVER. Anyone who tells you otherwise has no clue. Yes there are flaws in how math is applied but that doesn't mean the models are useless or going away. That said, demand rises and falls with the market just like in any other industry. Financial analytics is no different.
Would I be better off specializing in less volatile areas of Applied Mathematics?
Math is math. Don't know why you think finance is some sort of special case. Frankly you may have an easier time getting a job with a financially oriented PhD than many alternatives outside of academia. Plus the financial math career options tend to be rather lucrative. The real question to my mind is one of lifestyle. The hours are likely to suck and there is a good chance you'll miss a lot of extracurricular life.
In short, what is the future of Financial Mathematics in light of the current financial crisis?"
Very bright. There is always a future for a good analytical mind. No financial crisis will ever change that. Math in the financial world is NOT a fad and never will be. There are specific analytical techniques that become fads but that's not the same thing. If you really are interested and enjoy the subject and don't mind working a lot of hours go for it.
1. "Here There Be Dragons" may be anathema to a working quant like Taleb, but it's a magnet for research.
2. Don't assume you'll spend your entire working life refining your dissertation. Don't get too specialized -- a good general background will let you move wherever you want. Remember, most current quants started out as physicists.
3. Quants are the financial equivalents of Palace Astrologers -- they tell the Powers that Be what they want to hear. The current mess is not so much the result of bad analysis as bad management decisions. Prepare to have your results misunderstood, misrepresented, and just pain misused.
4. As others have said, follow your heart. Life's too short to waste on something that *might* make you a lot of money if things don't change radically in the next few years. Which they will -- the current financial system is broken, and all the pieces haven't hit the floor yet. Depending on your own attitudes, this is either exciting or terrifying.
Welcome to the Turing Tarpit, where everything is possible but nothing interesting is easy.
I am in a very similar situation as I too have been admitted to an Applied Math PhD program at a school of good repute. I have worked as a trader in Chicago for 4 years and at the moment I hold a 'quant' like position until school begins in the fall. Independent of what some popular books may say, the current demand in Chicago for a PhD certified quant is quite large. Furthermore, the potential income is orders of magnitude larger than what you would earn applying your PhD elsewhere. I think if you find it interesting, do it. The beauty of mathematics is that we take the same ideas and apply them to any field we like WLOG. Drop me a line if you wish to talk about this further....
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.
Hey, everyone knows mathematicians are way out on the far right.
.evom ton seod gis eht
Taleb gets a lot of attention from laymen, but he's scum among professionals. He barely knows what he's talking about; he offers criticism but not solutions. He talks about the ancient "gaussian" models all the time, yet never mentions the stochastic volatility models everyone uses.
Most quants are well aware of the shortcoming of their models.
Quants are well-respected in the industry. Financial math is pretty interesting in its own right. However don't feel like you need to study financial math to get a job. Smart people are picked up regardless of background; there's plenty of math/CS phds who had no clue about finance before starting. (The truth is it only takes a few months to learn the key stuff.)
Is it a contentious theory? My understanding is that most economists view a given market as equilibrium-seeking (as you describe) -- not that markets are always and everywhere in a perfect state of equilibrium...
Is Capitalism Good for the Poor?
Hm yeah, future of idiotic ideas? Not so bright. Future of math in financial analysis? Bright if you are bright. Look elsewhere if you are not. Ding! Shame on you! Next question!
In that sense you are correct, and that has been one of the major weaknesses of our financial system, since the last vestiges of a gold standard was finally destroyed by Nixon 37 years ago. If you look at our monetary system since, credit (and so necessarily, debt) has grown out of control.
As I stated earlier in this thread, if the market were based on purchased assets, rather than leveraged borrowing, then crashes are not inevitable. It is speculation financed by credit that causes the house of cards to topple. If assets being sold are owned, rather than leveraged, then even if a deal goes bust there is no huge chain of debt to collapse.
"The toss of one coin is unpredictable. The toss of 100 coins is easy to predict with some certainty. Putting money in any one stock is gambling, putting money in 200 is investing. Couple that with an understanding of what money actually is and you can avoid the worst the governments and banks can do, or even take advantage of the manipulation."
As for investing, that's what we were told, sure. Tell that now to all the people who "invested" for 30 years and now have no more 401K. That idea seemed solid, but now we see that it was not.
As for "The toss of 100 coins is easy to predict with some certainty.", that is only true in a narrow sense. An average can be calculated with some degree of reliability, but only an average. Many people misinterpret this and think that because there has been a long string of "heads", then a "tails" must be coming up next. This is known in mathematical circles as "The Gambler's Fallacy", and has been responsible for lost shirts in Vegas and "investors" jumping off of buildings.
Once again, your idealized market of "investors" is only possible given certain conditions: absence of manipulation and corruption, and short-term market behavior that is effectively random.
A colleague who lived in London and had a PhD in Physics had a sign made which read
DOCTOR VISITING
and put it inside his windshield whenever he double parked visiting friends. I understand he never got a ticket. The sign was after all not a lie, just didn't specify the kind of doctor he is.
PhDs are also occasionally useful in dealing with officious bureaucrats, since they entitle one to be called "Dr. soandso". A woman colleague also remarked they save her from having to decide whether to introduce herself as Mrs. or Miss or Ms...she just uses Dr.
The more crucial situation is that we are in the midst of an enormous battle to rend our existence down to pure feudalism - even further down from the neofeudalism which prevails today (does any society which would truly espouses progress - which few do today, and definitely not the USA - still have "interest" and "rent" ???).
Capitalism is dead. The economy is over, yet Geithner, Summers and Bernanke continue on with the Bush Administration's neocon economic prescription - super-massive transfer of wealth to the upper 1/10th of 1 percent and reducing the rest of us to serfdom.
It is far more than simple errors in financial math; essentially it is a planned design for an historically colossal fraud (and NO! - these aren't "exotic instruments" which no one understands - Ponzi schemes and fraudulent tontines are comprehensible to most of us). The phrase from that Wired article to focus on - and to try to fully understand - was that an unlimited number of credit default swaps may be written against one borrower (disclaimer: quote from memory, may not be exact)- which says it all.
Marx was an optimist - believing that industrial capitalism would end up supreme, dominating the financial aspect and utilizing it to its own ends. Instead we have financial capitalism, which seeks to minimize and trivialize industrial capitalism (read technocracy, or something to that effect), which is nothing but a gigantic scam and fraud.
Should those interested, follow Prof. Michael Hudson, the most brilliant banking historian and financial economist in the Western Hemisphere. Prof. Taleb had it perfectly right in a past NPR interview when he stated that "..the banks have taken over and the only thing socialized is their debt." (BTW, that was the last interview I've ever heard on NPR with Prof. Taleb!)
Sgt. Doom's hobbies: network penetration & forensic economics
It would still be right to say that they're caused by greed-fueled optimism, and that bad math is one of the causes for it.
Remove the math and its related jargon, and maybe some people (at least) would think twice, rather than take the word of some "expert" with numbers.
Dude, have you noticed any lack of respect for economists lately? It doesn't matter how many things they get wrong, or how many train-wrecks they cause, they continue to make money explaining away how they screwed-up last time, and why they're latest maneuver is going to work better.
Nicholas Taleb's criticism is worth looking at, but he's not really all that great a thinker. "The Black Swan" is good book, but it's far from perfect. Taleb is in a position where if almost anything goes wrong he can say "See, I told you so!" He doesn't really have a lot in the way of positive recommendations to make. He favors barbell strategies, and likes venture capital firms, that's about it.
True, the sum total invested in financial math WAS TRUE, but that has since been "magically" transformed into worthless derivatives - Geithner's fraudulent PPIP program to the contrary.
There should be mass mobs on their way to Basel to burn down the Bank for International Settlements, and to D.C. to burn down the Federal Reserve Bank; both detrimental to any true progress and human development.
Credit does not require growth to function.
Nerd rage is the funniest rage.
Is it so hard to use < to get <? I had flashbacks of doing FORTRAN on punch cards.
Oblig Family Guy song:
http://www.youtube.com/watch?v=ZHy4zN0fe_E&feature=related
As a mathematician/statistician, I try to encourage people to approach the subject from a wholistic and philosophical standpoint. I prefer applied maths, and for most people I think that is the direction which tends to be most rewarding. The way that I make these interests dovetail is by trying to understand the foundations of various branches of applied mathematics and how they relate to each other. For instance, there is a great deal of overlap between statistical physics, machine learning, bioinformatics and mathematical finance. A rigorous understanding of probability theory, information theory and real analysis will take you a LONG way in any branch of applied maths. Stochastic modeling is at the core of everything applied, and being able to decompose a system into components (be they wavelets, sinusoids, eigenvectors, polynomials or what have you) is incredibly important as well.
Ultimately, if you are going to spend the next 4-7 years in a PhD program you should be really passionate about what you are doing. Several other people have mentioned this as well but it's important enough that I think it bears repeating.
You may want to attend this conference: http://www.perimeterinstitute.ca/Events/The_Economic_Crisis_and_Implications_for_Science/The_Economic_Crisis_and_its_Implications_for_The_Science_of_Economics/
Concerns over the current financial situation are giving rise to a need to evaluate the very mathematics that underpins economics as a predictive and descriptive science. A growing desire to examine economics through the lens of diverse scientific methodologies - including physics and complex systems - is making way to a meeting of leading economists and theorists of finance together with physicists, mathematicians, biologists and computer scientists in an effort to evaluate current theories of markets and identify key issues that can motivate new directions for research. Perimeter Institute was suggested to be the gathering point and conference organizers plan to foster a very careful, dispassionate discussion, in an atmosphere governed by the modesty and open mindedness that characterizes the scientific community.
otherwise you'd be calculating how good of an idea it is; not asking us. ;)
The financial markets run on sentiment. But no PHB will ever be caught admitting as much. Because it basically means they will lose their million dollar bonuses. They hire mathematicians to come up with a mathematical model to explain their decisionz. Taleb talked about it in his book "fooled by randomness". You can be assured that your job in the financial district is secure because the PHB don't understand math. But they want to be seen taking logical decisions. And what better field than math to "prove" that the decision is logical. Q.E.D
O this learning! What a thing it is - William Shakespeare
Interest bearing credit works well when you have growth, otherwise you are going to have trouble paying off the interest. The interest payments have to come from somewhere... of course, you can devalue the original credit with inflation, but that's not really healthy for an economy either.
All you need is to make sure the party doing the borrowing has productivity that is higher than their consumption. Credit and growth happen to go hand in hand much of the time, as borrowing is used to finance activities or whatnot that hugely enhance productivity and growth, and because someone who knows their production is going to grow will be more willing to borrow against their current production.
If there wasn't any consumption, the interest might be a problem, but there is plenty of consumption (the vast majority of GDP is consumed).
Nerd rage is the funniest rage.
It seems that the fundamental problem with financial mathematics is that most models rely on the 'efficient markets hypothesis' which assumes our market is deterministic. Until we find (which we have not) that the human decision making process IS deterministic, the results of EMH backed formulae will never produce consistent results at predicting market behavior. Some of the most interesting research into trying to predict things that normally seem nondeterministic (like markets, human behavior, etc.) was being done by Orlin Grabbe (with fractal modeling techniques), but unfortunately, he seems to have died too early to finish that work.
If your interest is only passing, you probably shouldn't bother.
I work as the chief quant of a fairly large hedge fund (>3B). We are likely to hire one or maybe two quants as we expand over the next couple of years, and I expect those will be very experienced individuals made available by Wall Street's troubles. They will also almost certainly be math or science PhDs. Frankly, almost no one hires Masters graduates in FinEng to be quants, because in most cases the migrants from real math and science are more capable, or quickly become so.
You did previously see many FinEng grads hired as quant developers, desk quants/spreadsheet jockeys, and trader trainees. How that is going these days I can't say. Some kinds of stat arb and market making are doing very well these days but there's an unbelievable push into them so I foresee a big bust in those areas in the next 2-5 years.
An unrelated area that is having a bit of a boom but perhaps more sustainably is biostatistics/biomathematics.
This is demonstrably untrue. A party can be much more productive than their consumption, then mis-spend the assets on something other than debt. AS WE HAVE SEEN.
Our entire financial system today has been based on credit and debt, and the assumption has been that credit (and debt) would continue to increase forever. Very obviously, that is a fool's game. At least, it is a fool's game for those other than the finance companies, which made their officers rich off the debt of others, mis-spent the assets, then "required" a bailout.
The statement that growth is not required for a debt-based financial system is just plain false. The system is a house of cards, true, but that doesn't make it any less real.
It may be demonstrably untrue, but that isn't what you did.
Nerd rage is the funniest rage.