We had a talk yesterday at lunchtime from an alumnus who graduated with a physics degree, got a Ph.D. in Physics, did a couple of post-docs, and then decided to give academia a miss, and went to Wall Street where he’s been a financial analyst for the last 12 years. He talked, mostly for the benefit of students, about his path to the world of finance, and what’s involved in financial jobs.
This was terrifically interesting, and really useful. Given the way academia works, people who manage to get tenure-track faculty positions almost never have any first-hand experience of the other career possibilities for students with a degree in physics, so even second-hand information is great to have when it comes time to advise students.
This was also an excellent talk, because he didn’t sugar-coat things at all. He talked about jobs in finance in a very direct and detailed way. Distilled to a few bullet points, what he said was:
- Wall Street has jobs for people who are good with numbers
- Finance jobs are very hard work
- Finance is not science
- There is compensation for all the hassle
Expanding on these:
Wall Street has jobs for people who are good with numbers. He laid out a bunch of different kinds of financial analysis positions, and noted that physics majors are suited to any of them, if they want. There are jobs for people with undergraduate degrees– generally entry-level analyst positions that only last 2-3 years– and jobs for people with Ph.D.’s– generally starting at a higher level in the company.
He noted that MS degrees in physics are still widely regarded as a consolation prize for not getting a Ph.D. When I asked whether that would be held against an applicant, he thought for a moment, then said “We would prefer you to come to us after succeeding at something than after failing at something.”
In general, he said that computer skills are absolutely essential. Most of their programming is done in C++, and most of it also needs to interface with Excel, so it can run in the background for people who didn’t write the code. There are also a fair number of tasks involving the Web-, such as automatically downloading data updates from government agencies, and that sort of thing. Students interested in finance jobs should emphasize those skills.
Finance jobs are hard work. He noted that people on Wall St. can expect to work 11-12 hours a day, plus occasional weekends. It’s not just entry-level people who do the 60-hour weeks, it’s everybody in the company. He said that students who have any ambition to do something else– community service, hobbies, generally having a life outside of work– should stay away, because that doesn’t fit with the job.
He said that at his last job, he would have no hesitation about calling an analyst on a Saturday morning, and saying “I know you had plans for the weekend, but we need you in the office today to work on something that will take you until late Sunday night.” His boss would have no qualms about calling him and doing the same thing.
Finance is not science. Relative to the computational problems in physics, the actual computational details of what financial analysts do are “baby stuff.” He said that unlike science problems where most of the work is in figuring out how to do a calculation, in finance the work is all in doing the calculation. The techniques they use are all pretty standard, and don’t vary all that much, the trick is getting through all the necessary steps in time to meet deadlines and make deals.
He said that what he does is challenging and interesting, but that the nature of the challenge is not at all like the intellectual challenge of doing science. It has a completely different feel.
There is compensation for all the hassle. The work is hard, the hours are long, there’s no job security, but if you’re good, you make shitloads of money. Period.
He said that there’s also a certain amount of thrill to the job, in knowing that code you write will drive decisions that make or lose millions or billions of dollars for people and companies. Working on Wall Street puts you at the center of the engine of capitalism, and it’s a kick to take a limousine home at two in the morning.
All in all, his talk confirmed that I could never have gone the Wall Street route. It did make the process a lot more clear to me, though, and some of the things he said will be essential information the next time I teach our senior thesis seminar (hence this blog post, as a permanent record…).
One final note: After the students had cleared out, I asked jokingly “At what point in the modelling process does one become convinced that giving subprime mortgages to people with no assets and no income is a great investment?” He rolled his eyes, and said “Look, I’ll have you know that the group I was with was shorting mortgage-backed securities last February.” He said that anybody with any sense knew this was a catastrophe coming, and that the whole mess had a lot more to do with psychology than financial models.
As a side note, there is sufficient demand in computational finance that schools are (just) beginning to offer formal degree programs in it. My old advisor is responsible for putting one such program together, as a joint venture between the finance and computer science departments.
I can’t quite decide if this is going to be another academic fad (where the schools think there’s a market for 5,000 graduates over the next ten years and really there’s a market for maybe 250) or not, though.
This also might be a Chicago/New York thing, with the Merc and the NYSE driving demand.
Yeah, the speaker mentioned the recent appearance of programs in “financial engineering,” which seems to be basically the sort of thing you’re talking about. The programs he mentioned are MS programs, but they haven’t been around long enough for it to be clear what the real impact on the job market will be. Probably slightly less demand for people with undergraduate degrees in physical sciences, but I doubt it would affect the options of those with Ph.D.’s.
He rolled his eyes, and said “Look, I’ll have you know that the group I was with was shorting mortgage-backed securities last February.”
Right. He was also at Woodstock, saw Wilt score 100 points and came out ahead on every trip to Vegas.
Amazing how everyone on Wall Street saw this disaster coming, has been shorting CDOs for two years, and yet somehow all the Wall Street firms are losing billions.
Yeah, ours is a master’s program (I had to check– I had thought it was an undergrad program, but I guess not.) It’s not a PhD program, but I’d expect that doing a PhD in that area is mostly a matter of finding an advisor willing to take you on, and setting up the right committee, which would be easier at a university with an MS in place.
I still have mixed feelings on the whole thing. On the one hand, I know my former advisor and I was there for a lot of the foundational meetings and such just because he was my advisor. He’s deadly serious about it. The university is serious enough to commit resources, and there is co-commitment from the business/finance college. I know there’s support from the Chicago area because neighboring universities are helping, and the number of Merc and associated contacts the guy has is astounding.
On the other hand, this is a university that also gives CS degrees in video game design…
NINJA mortgages were a fantastic investment, as were the Hunt brothers cornering the silver market, http://en.wikipedia.org/wiki/Long-Term_Capital_Management, and the dot com boom. Each was the perfect sure thing… until positive feedback fed back.
Professional management maintains the status quo of short term gains. Every business school demands profits today trump debt service tomorrow. Alas, tomorrow usually arrives yesterday.
A big part of the sub-prime mortage crisis was the looming threat of anti-discrimination lawsuits if African-Americans are more likely to be in the group of “people with no assets and no income” than Whites, which they are.
Either you lend money to people with a certain level of assets and income, and accept that this will result in racial imbalances in having people lend you money; or force lenders to lend to all groups equally, and accept that you have now given out money to a group that can’t repay them (poor Af-Ams).
Is he with Goldman-Sachs? Because those are the only guys who saw the mortgage crisis coming and acted on it.
The old rejoinder to the concept of “financial engineering” is that you cannot come up with an example of a bridge failing because too many people had used the same design. Its odd – the blindness of these financial models to the implications of everyone in the market doing what the models say they should do. The 1987 crash was caused largely by portfolio insurance, a synthetic option replication strategy, driven by the equations used in stochastic mechanics. Then you have LTCM and now the subprime fiasco. The danger of course in not the quants themselves, but the Wall Street Firms who for their own interests use the models to give a veneer of science to selling crap to investors.
I know an economist who moonlighted in the derivative /hedge business, and #8 has part of it right. But if you can run the other guy’s model and thus predict (PREDICT) what he is going to do in certain situations, much profit can be made from their losses.
I think #6 ignores the detail that Blacks were not the only target (plenty of whites were as well), and fails to make clear why an investor repackaging financial instruments would care one bit about what is going on in a completely different business (lending money). Sub prime is not about lending the money, it is about laundering the paper to conceal its value from the ratings agencies.
If you stated that your business model is to loan money to people who cannot afford to pay it back, then sell the loan to me so I can repossess the property and sell it for significantly less than the loan amount – meaning I lose money while the broker who ‘originated’ the loan makes money – it would be really hard to sell me those securities.
Wall Street (Or London’s financial district, or Tokyo, etc) is not the ‘engine of capitalism’; the engine(s) are the mines, factores and offices where resources are extracted, goods are made and services provided. The fact that the financial services industry has gone from a subsidary function of matching capital to businesses to an end in itself is a *very bad thing*.
The best single era for the average Westerner was the post war long expansion 1945-1973; a time when the financial services sector was nailed down by regulation as a direct reaction to the disaster of the 1930s. The wave of deregulation since then has been followed by static real wages, spiralling income inequality, low productivity growth, low economic growth and high personal/national debts; all perfectly predictable if the bulk of excess profits are creamed off by a massive financial sector instead of being available for reinvestment.
I’m sure your aluminus even thinks he has ‘created value’..
Well, he probably did. If the wheels of the financial system turn smoothly, then the companies have better access to credit, for example, and can invest more, increase their output and bring more goods to the consumers for lower cost.
I am particularly surprised by the quote in the blog note:
” Relative to the computational problems in physics, the actual computational details of what financial analysts do are “baby stuff.” He said that unlike science problems where most of the work is in figuring out how to do a calculation, in finance the work is all in doing the calculation. The techniques they use are all pretty standard, and don’t vary all that much, the trick is getting through all the necessary steps in time to meet deadlines and make deals.
He said that what he does is challenging and interesting, but that the nature of the challenge is not at all like the intellectual challenge of doing science. It has a completely different feel.”
Try working in credit derivatives. There is not a single model which I would say is particularly good. They all just barely capture what really happens in the market. There is a strong incentive to come up with your own model which does it better. And since lots of guys doing this stuff are ex-physicists (like me), they will use the thinking process they have learned in science to try to come up with something better than Gaussian copula.