i'm not the type of person to put all of my eggs in one basket, i like to diversify my job marketability while studying something i enjoy. in that spirit, these are the two subjects that interest me enough to pursue a phd. just wondering if anyone can provide input about a couple of concerns: 1) which of these seems like better preparation for a quant career (i realize apmath is extremely broad, but i would tend toward probability and algorithms) 2) which seems the most versatile in general in the modern job market. by the way, i'm just finishing a comp sci master's.thanks for any input.

try string theory

any applied math or OR phds here who can comment?

I am a Ph.D. student from an OR/FE program. We have some students who do financial math and others who do OR, Statistics and such. As far as industry placements into quant finance roles go the people who have the classic hardcore financial math background have a slight advantage because that is the most sought after skill set. The people who do internships on Wall Street tend to do well no matter what their specialty.There are OR positions in industry outside of finance. But you've got to ask yourself if being a part of the OR group at an airline, a railroad, a hotel, UPS, Fedex, etc, etc. is for you. These are typical back office positions with limited upside. There are a few OR software companies as well and of course consulting companies. I have heard now of two cases where part of the OR function got outsourced to low-wage countries. That is something to consider as well. One notable difference between OR and applied math is that OR will keep the door open for faculty positions in business schools.

thanks for the very helpful response. i appreciate it. i don't think i'm totally clear on the differences between the terms "front", "middle", and "back" office other than the fact that the they're ordered in terms of decreased favorability, but i can google about that. ok i googled it and understand it now.

QuoteOriginally posted by: iconI am a Ph.D. student from an OR/FE program. We have some students who do financial math and others who do OR, Statistics and such. As far as industry placements into quant finance roles go the people who have the classic hardcore financial math background have a slight advantage because that is the most sought after skill set. The people who do internships on Wall Street tend to do well no matter what their specialty.There are OR positions in industry outside of finance. But you've got to ask yourself if being a part of the OR group at an airline, a railroad, a hotel, UPS, Fedex, etc, etc. is for you. These are typical back office positions with limited upside. There are a few OR software companies as well and of course consulting companies. I have heard now of two cases where part of the OR function got outsourced to low-wage countries. That is something to consider as well. One notable difference between OR and applied math is that OR will keep the door open for faculty positions in business schools.========================================== You will find a large number quants and profs. from OR esp. from Stanford U..

i'm not too happy that i used the term back office as it might imply that the position doesn't have anything to do with the actual business a company is involved in (something generic such as accounting, IT and such). that's certainly not what i meant. in many of these roles you actually have quite some business impact. being paid accordingly is a different matter. as an OR analyst you are part of the corporate cost center and will be paid accordingly. you might find that you will make with a Ph.D. as much as someone with an MBA from a second tier school makes.

ok thanks for the clarification. that's very helpful info. i wonder if anyone knows of good books which provide overviews of these various professions (well i guess applied math isn't a profession in itself).

OR is not a profession either. I'd be more interested in interviewing an OR guy than an app math guy, since the former are more rare. The knowledge of some unusual black magic such as mixed integer problems is in my eyes definiely a plus on a CV - you just have to acquire your probability and finance knowledge thru self-study, as OR alone will not pull you through.

A main difference also is that ORFE uses techniques from industrial engineering that is very very applied and applied math takes more from pure math. But there are the exceptions. I would go for applied math since it gives you better mathematical tools than engineering, such as the analysis that you need for stochastic calculus and numerical analysis. I think its always harder going from very applied to more pure than going from applied to more applied.

IBM says '... continuing to spend heavily on research..building a new business around "analytics and optimization"....including electric power transmission and securities trading.''...expects to hiee up to 4,000 mathematicans and other specialists over the next three years...open financial-analystics centers in NY, London and Tokyo this year.' WSJ 4/29/09 p. B3 Sounds like OR people may have some real shots.

QuoteOriginally posted by: emmeljayi'm not the type of person to put all of my eggs in one basket, i like to diversify my job marketability while studying something i enjoy. in that spirit, these are the two subjects that interest me enough to pursue a phd. just wondering if anyone can provide input about a couple of concerns: 1) which of these seems like better preparation for a quant career (i realize apmath is extremely broad, but i would tend toward probability and algorithms) 2) which seems the most versatile in general in the modern job market. by the way, i'm just finishing a comp sci master's.thanks for any input.i was a OR PhD candidate 9 years ago, even passed qualifiers, but never completed the degree. in those days, I also did a number of courses (out of my own interests) in both applied math and fin engg. later after gradually slogging my way through a prop trading desk and worked up my way into a senior level position in a hedge fund (currently). a couple of facts from my perspective about job prospects in FINANCE:1. when i did fin engg courses at Columbia, the program had started 2-3 years ago and held great promise. but the era of quant finance aka derivatives pricing is more or less dead. stuff like exotic options were dead long before & you din't required quant finance to trade vanilla options. a few quant finance guys ended up in structuring desks like CDO and Mortgage desks still pricing stuff (but most non-stochastic calculus type) and that's now mostly dead too. in addition that there are host of applications & softwares that have already coded in the most complicated stuff anyway. my feel (& i may be wrong) is that majority of the current fresh-out-of-college quant finance guys (PhD or otherwise) end up as junior analysts doing crap work in excel, no better than say an app math or OR background - and I know a huge number of PhDs in hedge funds heavy with PhDs are just doing plain excel or rudimentary matlab/C++ coding which does not require any heavy-duty quant background. 2. The second area is risk management, model validation etc. Here too, most of the risk management is done by subscribing to external firms - and most people in risk management are just printing & generating risk reports - often doing dirty data cleaning type work too. of course quant fin knowledge here is useful but often mostly to talk the language, communicate in the language etc. i don't want to demean the risk management role, but thats mostly the reality.3. the places where still a heavy duty quant work (not necessarily quant finance!) are in areas such (but limited to)(a) asset allocation decisions (b) model development (often linear) in process driving trading e.g. equity market neutral/stat arb models, cta trending models etc. --- in most of these areas the most useful thing i found are areas like deterministic OR (e.g. advanced linear/integer prog), a lot of knowledge in advanced matrix algebra (e.g. if you can work out variants of mean-variance opt in matrix forms). some basic prob & basic programming skills will help you survive.anyway these are my opinions ...

QuoteOriginally posted by: pb273QuoteOriginally posted by:i was a OR PhD candidate 9 years ago, even passed qualifiers, but never completed the degree. in those days, I also did a number of courses (out of my own interests) in both applied math and fin engg. later after gradually slogging my way through a prop trading desk and worked up my way into a senior level position in a hedge fund (currently). a couple of facts from my perspective about job prospects in FINANCE:1. when i did fin engg courses at Columbia, the program had started 2-3 years ago and held great promise. but the era of quant finance aka derivatives pricing is more or less dead. stuff like exotic options were dead long before & you din't required quant finance to trade vanilla options. a few quant finance guys ended up in structuring desks like CDO and Mortgage desks still pricing stuff (but most non-stochastic calculus type) and that's now mostly dead too. in addition that there are host of applications & softwares that have already coded in the most complicated stuff anyway. my feel (& i may be wrong) is that majority of the current fresh-out-of-college quant finance guys (PhD or otherwise) end up as junior analysts doing crap work in excel, no better than say an app math or OR background - and I know a huge number of PhDs in hedge funds heavy with PhDs are just doing plain excel or rudimentary matlab/C++ coding which does not require any heavy-duty quant background. 2. The second area is risk management, model validation etc. Here too, most of the risk management is done by subscribing to external firms - and most people in risk management are just printing & generating risk reports - often doing dirty data cleaning type work too. of course quant fin knowledge here is useful but often mostly to talk the language, communicate in the language etc. i don't want to demean the risk management role, but thats mostly the reality.3. the places where still a heavy duty quant work (not necessarily quant finance!) are in areas such (but limited to)(a) asset allocation decisions (b) model development (often linear) in process driving trading e.g. equity market neutral/stat arb models, cta trending models etc. --- in most of these areas the most useful thing i found are areas like deterministic OR (e.g. advanced linear/integer prog), a lot of knowledge in advanced matrix algebra (e.g. if you can work out variants of mean-variance opt in matrix forms). some basic prob & basic programming skills will help you survive.anyway these are my opinions ... To the point and totally makes sense. The best place one could end up today with a phd in heavy quant field is in fixed income or high frequency research. Pretty much, most of the other job like derivatives are not the same as it used to be some 10 years ago. Better still learn finance more than learning quant skills.

QuoteOriginally posted by: cryptic26QuoteOriginally posted by: pb273QuoteOriginally posted by:2. The second area is risk management, model validation etc. Here too, most of the risk management is done by subscribing to external firms - and most people in risk management are just printing & generating risk reports - often doing dirty data cleaning type work too. of course quant fin knowledge here is useful but often mostly to talk the language, communicate in the language etc. i don't want to demean the risk management role, but thats mostly the reality.==============================One risk firm that I don't think fits that description is QRM www.qrm.com

QuoteOriginally posted by: jfuqua ==============================One risk firm that I don't think fits that description is QRM www.qrm.comThere are many firms that do not fit the description of risk management as mentioned by "pb". I think he was referring more to the risk management roles within a bank who subscribe to the vendor services such as QMR, Barra, Risk Metrics, KMV, etc etc. These firms are well known for their heavy quant work in risk management space.