Via Steve Hsu, I ran across the cover article from Alpha magazine, a magazine for the hedge fund industry, entitled The New Math. It describes the role physicists are playing in several hedge funds, developing sophisticated trading strategies. One of the best known of the organizations doing this is Renaissance Capital run by Jim Simons, and its success is now responsible for funding the new Simons Center at Stony Brook.
Two of the theoretical physicists featured in the article were fellow graduate students at Princeton while I was there. One of them, Marek Fludzinski (who was a couple years ahead of me), quickly left academia and went on to a career in finance, ending up founding the Thales hedge fund, which he still runs. The other, John Moody (more about him here), was in my class and so I got to know him quite well, but I had lost track of him in recent years. He worked with Frank Wilczek on axions and left Princeton for Santa Barbara after Wilczek went there to the ITP.
In recent years quite a few mathematics Ph.D.s and a very large number of particle theory Ph.D.s have ended up in the finance industry, and the article describes the kinds of things that they are doing. The impact of the recent melt-down in the credit markets remains to be seen: maybe there will be fewer jobs available in this field, or maybe demand will increase for people with this kind of technical background as companies pursue ever more sophisticated strategies.
Unfortunately, the most likely outcome is that the “eggheads” will serve as convenient scapegoats for the fact that banks had no idea what kind of debt they owned or whether it was any good or not. Cue backlash in three, two, …
The last thing they need is more sophisticated strategies. It’s funny how so-called financial engineers never seem to have heard of the old engineering KISS principle, much less thought paid close attention to the hard lessons software engineers and computer scientists have been learning about building complicated systems for the past 5 decades. And then there is the natural invitation to fraud offered by debt securitization…
Of course, they’ve had to deal with clueless executives and investors breathing down their necks, looking for ever higher returns. That certainly doesn’t help in learning and sustaining good engineering judgment.
Sorry; the rant is over.
There is always room at the top, if I may steal that paraphrase, but in my experience there exists a fairly high washout rate among the physicists, less from aptitude than boredom or culture shock.
However, once you’ve developed your toolbox and demonstrated good results, you’ll basically never want for a job again; the unemployment rate among good quants has been about zed for at least two decades, being more a factor of liquidity than anything else.
In business section of last Sunday’s Times, there is a review of a book called “The Trillion Dollar Meltdown”. It quotes the author as saying, “As a general rule, only the very smartest people can make truly catastrophic mistakes”.
I think Chris W. has it right.
It is not new that banks or financial institutes look for quants who have PhD degrees in physics. Back in year 2000, the physics professor I worked with asked me if I was interested in working in some banks as a quant as they needed such people to do very difficult maths. As my maths was really not good, I refused the offers.
It will be interesting to see how many of the Masters programs in financial math/ risk engineering / whatever will survive the current meltdown. And what will happen to the unemployment rate for new Ph.D.s in both math and physics if this alternative career is no longer readily available.
This article should get play higher up the media chain. Off Wall Street, industries should recognize what geeks bring to the table in terms of strategic sophistication. Many do, but many don’t. The number of math jobs (including those going to the physicists) will increase, not decline.
Chris W.,
I don’t think the current financial meltdown and the sophistication of hedge fund strategies are linked in a particularly meaningful way. The mortgage system that failed us was in place before hedge funds became what they are today — a field of giants in the financial industry. Hedge funds didn’t pressure anyone to make an industry out of sub-prime loans. (And hedge funds have been complaining about Bear for a long time.)
Hedge fund traders have been talking about how scary the leverage at Fannie Mae and Freddie Mac have been for over a decade, but could do nothing about it but talk.
Deane,
As for the smartest people making catastrophic mistakes, that may often be true. But most generally, it’s the people with the most power who make the biggest mistakes. This is just the common investor effect, and sometimes the power holder is not the smartest person in the room.
See this interview with Robert Merton in Technology Review.
I’m not buyin’ it…
Matt: Major players in the financial services industry facilitated and encouraged the growth of the sub-prime mortgage industry, and disseminated securities based on these sketchy loans. They can say that nobody had to invest in these things, but that’s disingenuous; they were marketing them, and they were fully intent on succeeding in those marketing efforts. Clearly there was a lot of fraud and stupidity among subprime lenders, but they’ve paid the price; they’re out of business or shadows of their former selves. The borrowers are hardly getting a free ride either.
The whole “investors and borrowers were stupid, and have themselves to blame” argument is at about the same moral level as a drug dealer who ridicules his junkie customers for buying the product.
Perhaps the only reality check (if not a solution) is extreme skepticism, shading into paranoia, and willingness to look under the hood by anyone who relies in some way on the machinations of this industry. If that smacks of saying screw the banks and hiding one’s savings in a mattress, so be it. Fortunately there are quite a few financial institutions that managed to maintain some detachment from the whole debacle.
[Sorry, Peter.]
I maintain that the most powerful person in the world (say, George W. Bush) is able to make a catastrophic mistake only with a lot of help from extremely smart people. The point is that smart people are able to magnify or leverage a small or diffuse effect into much bigger or more concentrated one.
“The point is that smart people are able to magnify or leverage a small or diffuse effect into much bigger or more concentrated one.”
Yes, but there’s a bit of cancellation going on at the same time. Probably stable overall, but subject to big fluctuations that could have really bad effects.
I think you need to distinguish two categories of math jobs, those related to creating quantitative trading strategies and those used for the securitization of mortgages into what are known as Collateralized Debt Obligations (CDOs).
The former will continue to prosper, but the latter is essentially dead in the water, because it is a major factor in the credit crisis. Enormously complex mechanisms were used to package suspect loans in order to obfuscate the true risk in the underlying securities – essentially nobody understood how dodgy these vehicles were because they were deliberately packaged so as to frustrate any attempt to understand them according to standard financial principles.
Mortgage securitization will recover and continue, but this industry is experiencing a “flight to simplicity”, transparency will be everything so there will be no place for complex and concealing math.
DB –
The most common use for math/science PhDs in finance that I am aware of is in developing derivative pricing models, of which CDOs are but one strand … I do not see this going away any time soon – although the appetite for derivatives is greater in a bull market it does not go away completely in a bear market, especially as existing portfolios still need to be managed.
Re: DB’s comment
“Flight to simplicity” is a good thing, and I hope it continues.
And indeed this will probably lead to reduced demand for quants and less blind acceptance of sophisticated models. But that does not mean that mathematical finance will go away. It will definitely continue to play a significant role in finance, including securitization of mortgages and other assets. However, both quants and their overseers need to be both more honest and outspoken about the limits of what mathematical finance can do.
I believe a lot of quants understand the pitfalls of their models but have been unwilling or unable to express these issues effectively to others.
An early example of the particle theory to finance transition is
eloquently described in Emanuel Derman’s autobiography
“My life as a quant”. I should declare an interest: we worked
together on early electroweak models in 1973, and this is
discussed briefly in the book. (Page 73!).
In the (rather good) textbook “Statistical Mechanics of Financial Markets” one chapter discusses potential uses of earthquake prediction theory and a “Richter scale for market crashes”, as well as ideas of scaling, turbulence theory and so on; so all sorts of ideas are being considered. Getting some real idea of when things are about to go seriously awry is the real trick in this game, and that’s where LTCM in particular failed disasterously. It has clearly been demonstrated that these trading strategies can work effectively if they are not pushed too hard; then steps can be taken if there is a nasty change in market dynamics. But LTCM in particular was massively overleveraged and got immobilized by its sheer mass. The problem is that some managers and investors are always going to want to always push the models and push boundaries to try and create bigger returns, or to outdo each other. The state of the art seems to be getting very advanced now though albeit not infallable. The stuff on genetic algorithms, machine learning and neural nets discussed in the linked article is pretty interesting though.
Another problem is when you have more and more people chasing the same money and opportunities in the markets, using the same strategies. I wonder how much of the financial engineering literature is worth reading since all the best stuff will no doubt be kept very secret. Also, it could be all too tempting and convenient for traditional manages, traders and Wall street types to blame the current mess (or future crises) on the “eggheads” or “rocket science”, especially if things get worse. Some traditional Wall Street types and traders also no doubt feel resentment towards hedge fund managers who make much more than them and of the widening gap between “the haves” and the “have mores”.
The fact that there were a number of well-documented high-profile disasters in the 90s directly due to “eggheads” does not help either:LTCM of course, as well as Joseph Jett and Kidder Peabody, and the Orange County derivatives mess, also due to a theoretical physicist I believe. However, the steady, consistent and sober performance of Renaissance Technologies for example has also given a lot of credence and respect to what responsible quantitative finance or financial engineering can actually do. So I would say it is an open question for now as to whether the current situation will see an increase in demand for physicists and mathematicians or a decrease. |’m skeptical though as to whether aspects of human behavior and psychology can be factored into models. When he lost £20,000 in the South Sea Bubble Newton supposedy said: “I can compute the motion of heavenly bodies but not the madness of people”.
Dr. Oakley’s Lessons To Be Learned From Past Financial Disasters
Orange County: Some derivatives salesmen are really good.
Joe Jett, Nick Leeson & Jerome Kerviel: When large amounts are involved, make sure that at least one person in the organisation outside the trader’s bonus pool knows and understands fully what is going on.
LTCM: PhDs in Mathematics sometimes do really stupid things.
When you think about it, what is the common thread between the following?
String Theory
LQG
LTCM
Subprime Hedge Funds
Unaffected. Financial engineering is a fairly small (in the scheme of physics) area that only a very few PhD’s from the very top schools tend to go into. If it disappeared entirely I doubt it would have any dramatic affect on physics employment rates. The idea that Wall Street is scouring physics departments is largely a myth these days; what might be more accurate is that there area couple of physics departments and a few MFE departments they look to.
Personally I see physicists as being eliminated from that area entirely over the next quarter century as students with more formal training replaces them.
It’s unlikely that “students with more formal training [will] replace” physicists and mathematicians. Despite the availability and employment advantages of specialized MFE and PhD programs in quantitative finance, there is still a wide gap in capability between the students on that bandwagon, and the theoretical math/physics types.
I agree that there is a wide gap in capability between students in MFE programs and theoretical physicists – the MFE peeps are much better at quantitative finance, at least for some significant initial period of time.
Unless you mean “MFE peeps” who already have a solid math/physics background (such as academia opt-outs grabbing a remunerable credential), the physicists leave them in the dust sooner rather than later. For a physicist to replicate the MFE skillset is much easier than the reverse.
I have to agree with ToQ here: I mentored a PhD in Aeronautical Engineering who had no previous finance experience. In less than three months he was able to write code to calculate cross-currency asset swap spreads from first principles. OTOH others I worked with in the investment banking business, some of whom had taught Finance at university level, but had no specific math or “hard science” background did not, IMHO, even understand what a yield curve was.
Everyone knew the housing bubble was going to burst, but actually some of the largest money to be made is right at the tail end of a bubble, which is why you see all these people staying in the game years after they first calculate the impending doom.
The whole art is knowing when to get out in time. To that end, many quant derived highly adaptive and sophisticated computer models were made that were very sensitive to the relevant indicators that should predict how and when that would take place.
The catastrophe occured when every indicator failed at the exact same time. There was no provision for that, and thus the models failed.
Its a good example of when and how computer simulations can go awry.
It does strike me as unfortunate that a high proportion of the people who get these sort of finance jobs are theoretical physicists rather than population biologists, computer network designers or others who work with mathematical models in science. By its nature, physics tends to deal with situations where you can make accurate measurements of variables and where there’s no collusion between states, so they really don’t have an intuitive feel for when they don’t hold, but neither are particularly true in finance. On the other hand they’re dealt with everyday in other fields. I wonder if there is any reason beyond economists physics-envy and the inherent self-promoting bombast of physicists why they’re the primary group recruited into mathematical finance? Is it an apostolic-succession situation?
Physicists are hired for marketing reasons….”Look how smart our people are”