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PhD or MFE

Joined
8/6/08
Messages
19
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11
I am new to the forum here but this seems like a pretty good place for information. I have tried the search function but wasn't having too much luck with it so I figured I would just ask my question (which I am sure has been posed before).

Is there that much of a difference between having a PhD or a MFE in the computational finance industry?

I am a senior a Indiana University and will graduate with a BS in Applied Physics and a BS in Mathematics. Right now I am thinking about going to grad school for a PhD in Applied Math. My thinking is that with a PhD I may be more qualified for upper level positions. It seems that a lot of the jobs on Quant jobs, Quantitative Analyst jobs in London, New York were looking for candidates with PhDs. However, I have spoken with a quant who told me I don't need a PhD because the experience I get in the work world will be more valuable than a PhD.

Ultimately I feel that a PhD would give me more opportunity for job growth than a MFE.

What do you guys think? Thanks

Jeff
 
I was in the same situation about 6 months ago. I knew that I wanted to become some kind of quant but couldn't decide between a PhD (3+ years of study) and an MFE (1 year of study). I applied for both and got several offers, so I had to sit down and think hard. This is the conclusion I came to whith the help of knowledgeable friends and people in the industry:

An MFE from a good university will always be an asset and will give you a strong background in quantitative finance. A lot of the stuff that is taught in an MFE, however, can be picked up on the job in a year or so. Also, since I come from a technical background (electrical engineering), it is not as if I needed to be taught programming. To be sure, there is still a lot that I need to learn in C++, but it's nothing that I can't teach myself. At the end of the day, an MFE is a practical hands on degree that will give you a headstart in quantitative finance.

A PhD is not meant to make of you a quant. It's a much more open and general degree. Sure, you will be studying something like the "sampling of sparse signals using exponential spline kernels" but in the meantime you will be acquiring a multitude of transferable skills. You will be able to tackle problems on a whole new level. You will learn to be independent and to find your way without guidance. You will learn to research new areas where nobody has ventured and produce novel ideas instead of following the crowd. And along the way you will probably become extremely comfortable with advanced mathematics.


Wether you have an MFE or a PhD, ultimately you will be competing against PhD's for top level quant positions and you will be hard pressed to find a head of a quant desk who doesn't hold a PhD. This was made quite clear to me by a couple of headhunters I talked to. Another advantage, IMO, of a PhD is that you don't end up 'losing time' while studying. Quants with PhD's usually get hired as associates and even sometimes as senior associates. A PhD therefore allows you to skip the analyst years while cultivating your knowledge.

Some people have told me that an MFE is the quicker way to a career in quant finance, and I'll give them that. For everything else, I'll take the PhD over the MFE.
 
The advice I received last year (from a few few profs and PhD students doing financial maths, people in the industry may very well have a different opinion) when I was in a similar position was that you shouldn't do a PhD if you're looking to find a pot of gold at the end of the rainbow. It's a lot of work, for a long time, and you'll be paid what's barely a living wage - ie, you'll be hard pressed to stay focused if you're not doing it out of an deep-seeded interest in the material you're studying.
 
Telecaster: i put your reply on the wiki (unedited); it seems to be a very good explanation of the issues involved.
 
And along the way you will probably become extremely comfortable with advanced mathematics.

As a doctoral student you may take some advanced courses. The dissertation itself will most likely be narrowly focused. For example, it may involve numerical methods for PDEs. Or some narrow aspect of some area of stochastic theory.

Be warned that the attrition rate for doctoral students is high: it averages 50% in mathematics, with upto 90% in some schools. The reasons vary: lack of motivation (wanting to do it for money or subsequent career is a poor reason); inability to get through prelims; worthless or indifferent research advisor; no headway on dissertation topic. A lot of failed Ph.D.s enter the market as ABDs (All But Dissertation), where they're hired at bargain rates. The (American) Ph.D. can be a very lonely and depressing five or six years. Forewarned is forearmed. Talent is no magic key: I've known a Fields Medallist who almost quit his doctorate halfway through (no headway in research and no support from research advisor).
 
Thanks for all the responses. They have been very helpful. I think I will ultimately pursue my PhD. One of my main reasons is I do not know 100% that the financial industry is what I will want to do and an MFE seems to be a very niche degree (although I could be very mistaken). A PhD in Applied Math will give me a little more flexibility (I hope!).

I am very interested in PDEs and numerical analysis. I am also very interested in parallel computing so I anticipate I will end up doing something fairly computationally intensive as opposed to some very analytical/theoretical. Using math to analyze real world problems interests me.

Jeff
 
The advice I received last year (from a few few profs and PhD students doing financial maths, people in the industry may very well have a different opinion) when I was in a similar position was that you shouldn't do a PhD if you're looking to find a pot of gold at the end of the rainbow. It's a lot of work, for a long time, and you'll be paid what's barely a living wage - ie, you'll be hard pressed to stay focused if you're not doing it out of an deep-seeded interest in the material you're studying.

I concur 100% - and should remember to phrase my thoughts using the rainbow analogy :)

Only do a PhD if an academic career truly appeals to you.
 
The advice I received last year (from a few few profs and PhD students doing financial maths, people in the industry may very well have a different opinion) when I was in a similar position was that you shouldn't do a PhD if you're looking to find a pot of gold at the end of the rainbow. It's a lot of work, for a long time, and you'll be paid what's barely a living wage - ie, you'll be hard pressed to stay focused if you're not doing it out of an deep-seeded interest in the material you're studying.

I think most PhD students are aware that the world isn't a true meritocracy and that salary is often inversely proportional to knowledge. However a PhD will not close any doors. Wherever you could have worked after your undergrad degree you will be able to work after your Phd. Not only that, you will also be hired higher up in the food chain. Plus you will have the opportunity to switch to consulting if along the way you realize that's what you want. So whatever you do, the 'pot of gold' will be, at worst, no further than before, and in some cases much closer.

Assuming you have the courage to finish your PhD, it is a win-win situation. In a world where everybody and their dog now has a bachelor's degree supplemented by a master's degree, I think it's a good idea to invest in yourself to a level more than what most people consider 'enough'. Starting your career 2 or 3 years after your friends might look like a bad idea in your early twenties, but in the grander scheme of things it's not a big deal.
 
ph.d or m.fe

I could have sworn I've seen a thread like this before but I"m having trouble finding it. I'm getting ready to apply for graduate school and I'm debating between going for a ph.d in applied math or an m.fe. Does anyone have any opinions about which is better to have, looking to go into a career in quant. finance? Also, I've heard quants with ph.ds make more money, is this true?
Thanks
 
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