The Value of a PhD and MFE Degree

Since writing for this blog in January about the HFT/algo job market, I’ve received many inquiries from students asking about the “requirements” for quant jobs on Wall Street. “Do I need a PhD?” is a frequent question. Each time I receive one of these inquiries, I struggle with the answer. My instinct is no. But when I look at who is working in these jobs, I do see a predominance of PhD’s in the top positions. PhD’s in mathematics, physics, operations research, EE, etc. are common in the quant community. So it’s tempting to tell students that a PhD is helpful, but it feels like the wrong answer. In my gut I know that the people getting these jobs are not getting offers because they have extra letters after their name. The people in these positions are there because they have proved over their academic and professional lives that they are:
List 1

• Very smart
• Quantitative thinkers
• Good at figuring things out with minimal guidance
• Dedicated

But the above is a generic list of attributes for hiring into just about any job. So what is it that makes someone hirable as a quant? The list isn’t long:

List 2

• Education in advanced math (stochastic calculus, statistics, probability, etc.)
• Good software development skills
• Good data analysis skills

Okay, now combine the two lists, and you have the list of qualifications for a quant.

So, back to the question of whether to get a PhD. Should I get a PhD?, asks one student who is angling for a career in quantitative finance. Will it help me? Is it necessary? No, it’s definitely not necessary. Will it help? Empirically, it seems to help. But does it? I’ve finally come to clarity on the subject with the help of a conversation today with the director of a quant group supporting credit trading for a major investment bank. Of the two lists above, the important qualifications are on the first list. This list has nothing to do with your education. Your success in any field depends on the first list. The 2nd list consists of skills, skills that can come from your education or experience. They are enabling skills, but they are not dictators of success. All career success comes from differentiating oneself with respect to the elements on the first list. You can get a PhD, spend the money and the time, but if you don’t differentiate yourself in the fundamental elements of success, the PhD won’t help.

So why are there so many PhD’s in quantitative roles, anyway? I think the answer is pretty obvious. Very smart people with quantitative instincts are drawn to the PhD path. Later they find that they are well suited to a career in finance. They satisfy both lists and hence are successful in quantitative roles in finance. Almost without exception, these are individuals who pursued a PhD based on their interests and passions (EE, Physics, Applied Math, etc.), not people who pursued a PhD as a means to a job in finance. QED: A PhD is not a requirement for a career as a quant in finance.

The MFE

I feel this article isn’t complete without addressing the MFE degree. The MFE provides students with the fundamental skills utilized in quantitative jobs. If you can afford it, it’s an easy way to satisfy List 2. However, it’s by no means a ticket to success in quantitative finance. I’ll explore the MFE further in my next post, “The MFE, Is it a Contra-indicator?”

As always, you can reach me at peter@affinityny.com. LinkedIn profile: www.linkedin.com/in/peterwagner123 (I keep a listing of active quant roles here).

 

[pacman]

Thank you for the nice read. How about a PhD in statistics, I see that you didn't include that in your list?

Do you think it matters a lot where you get your PhD?

Also with so many MFE graduates in the job market wouldn't a PhD be a way to differentiate yourself?

 

[John Cordeiro]

Thanks for the article. Was going through this dilemma of choosing an MFE or PhD. I think I've made up my mind for a MRes + MPhil / PhD in Financial Computing. I hope I made the right choice. I will finish my master's degree in computer and trying to be accepted into a good program.

 

[Lamer Chrystle]

I agree. Going to Ph.D. to get quant role is not very efficient. Simply, getting in and getting out of Ph.D. is totally different. You may get in Ph.D. easily with your fabulous background, but when it comes to get out of Ph.D. degree, it is totally different comparing to MFE degree.

People go Ph.D. because they just love their field. Most of them ends up at academia with glorious 'university faculty' member. As far as I know, people who goes to quant after Ph.D. is either didn't get a job from university or they happen to love financial industry during or after Ph.D. degree, not before.

 

[cheggy kilahala]

When all is said and done, PhD will help one to be successful in his career as it helps one develops a lot of list 1 skills. Depending on one's ambition level, PhD will help you towards senior positions

 

[Barny]

I think people confuse cause and effect here. People do not hire PhD's because they have the letters after their name. Getting a PhD for the sake of having a PhD is pointless. People hire people for their skills. It just so happens that you can learn incredible data analysis and modelling skills during a PhD, and these are the people who go on to good roles in banks. You can also not learn much in the way of data analysis. I know from personal experience, I am orders of magnitude better at modelling and data analysis now, in the final year of my PhD, than after my masters, and I did the world's top ranked masters in maths at the world's best university. This is mainly because of the rigour and high standards you are held to when publishing cutting edge science. Passing hard exams is one thing: pushing the boundaries of human knowledge is quite another. You learn the latter in a good PhD, which is why it can be useful for quant work.

 

[Daniel Duffy]

I think people confuse cause and effect here. People do not hire PhD's because they have the letters after their name. Getting a PhD for the sake of having a PhD is pointless. People hire people for their skills. It just so happens that you can learn incredible data analysis and modelling skills during a PhD, and these are the people who go on to good roles in banks. You can also not learn much in the way of data analysis. I know from personal experience, I am orders of magnitude better at modelling and data analysis now, in the final year of my PhD, than after my masters, and I did the world's top ranked masters in maths at the world's best university. This is mainly because of the rigour and high standards you are held to when publishing cutting edge science. Passing hard exams is one thing: pushing the boundaries of human knowledge is quite another. You learn the latter in a good PhD, which is why it can be useful for quant work.

And then the real test is how to apply that knowledge in industry, where the problem is the driver of the solution whereas in academia the research group has a 'solution' for a range of problems. It's a kind of comfort zone outside of which many academics fear to tread.

And it is not a given that having any degree means that a graduate will be good in business.

 

[Daniel Duffy]

One mentor once said

"You are only as good as your last project."

 

[Barny]

And then the real test is how to apply that knowledge in industry, where the problem is the driver of the solution whereas in academia the research group has a 'solution' for a range of problems. It's a kind of comfort zone outside of which many academics fear to tread.

I don't think it is industry vs academia. The best academics in the world have no problem with that concept. The trap many people in academia fall into is: "What problems can I solve with what I know/the tools I have?" where as, actually, the important question is: "What problems should I solve, regardless of the tools I need to use to solve them"? It's just that in industry, since dollars are on the line, the latter situation is vastly more common.

 

[MauroMeneses]

" http://www.quantfinancejobs.com/jobs/entry-junior/

Almost half of the job postings have PhD as a Must, and almost all of the rest a Msc from toptier/WorldClass University. "

Hello, I've been reading this and the "large number of internationals in MFE" and, being a student from a developing country and really wanting to work in quant finance, I'm feeling, more than necesity, the obligation to study a Master program from a top tier university, otherwise, I will be overlooked no matter how strong my abilities could be.

And may be some people could say "Hey man, you have to show yourself, prove what you´re capable of and you will see"
but that´s exactly my problem, here (in Mexico) the quantitative finance culture is virtually inexistent, granted only to a little elite, so my knowledge of how and what can i do to enter in a quant position is (still) limited.

So, getting to the point, How can someone with bachelor's or not worldclass Master avoid to be overlooked by this recruiters?
I do not mean how can i be as good as them, but how can avoid my resume being thrown away just because Phd is a Must and i dont even have a Master from toptier rogram.

Thanks a lot!

P.D. Sorry if i have grammatical errors or if its hard to read; Im working in improving my english skills.

 

[CasanovaJ]

" http://www.quantfinancejobs.com/jobs/entry-junior/

Almost half of the job postings have PhD as a Must, and almost all of the rest a Msc from toptier/WorldClass University. "

Hello, I've been reading this and the "large number of internationals in MFE" and, being a student from a developing country and really wanting to work in quant finance, I'm feeling, more than necesity, the obligation to study a Master program from a top tier university, otherwise, I will be overlooked no matter how strong my abilities could be.

And may be some people could say "Hey man, you have to show yourself, prove what you´re capable of and you will see"
but that´s exactly my problem, here (in Mexico) the quantitative finance culture is virtually inexistent, granted only to a little elite, so my knowledge of how and what can i do to enter in a quant position is (still) limited.

So, getting to the point, How can someone with bachelor's or not worldclass Master avoid to be overlooked by this recruiters?
I do not mean how can i be as good as them, but how can avoid my resume being thrown away just because Phd is a Must and i dont even have a Master from toptier rogram.

Thanks a lot!

P.D. Sorry if i have grammatical errors or if its hard to read; Im working in improving my english skills.

I think it depends on what you're trying to do exactly... In my case I've been an entry-level risk quant for a few years, much of my job has been to maintain code that was written to implement mid- and senior-level people's models, literally all of them have PhD's or MFE's, and they all built the things using statistical and optimization techniques that I just flat-out haven't learned how to do yet with just my Bachelor's degrees in computer science and finance... My motivation for getting the MFE isn't just that it's "impressive window-dressing on my resume to impress recruiters"-- it's that right or wrong, I've concluded that I actually need to genuinely learn this stuff in order to fully understand what it is that I'm proposing here to do for a living.

If you think that you're "smart and capable enough to qualify for a job even though you don't have a graduate degree," that's fine... I'd recommend fully understanding what the jobs are, though, before determining that you're qualified for them. Being a smart guy who evaluated integrals real well in Calc1 and "thinks finance is cool" doesn't qualify you to do research for an investment bank.

 

[MauroMeneses]

Hi CasanovaJ, thanks for the reply!

I think it depends on what you're trying to do exactly... I've concluded that I actually need to genuinely learn this stuff in order to fully understand what it is that I'm proposing here to do for a living.

Great Insight!

I'd recommend fully understanding what the jobs are, though, before determining that you're qualified for them. Being a smart guy who evaluated integrals real well in Calc1 and "thinks finance is cool" doesn't qualify you to do research for an investment bank.

I didnt want to sound pretentious, im not qualified at all since im just about to finish my undergrad (Act. Science), I will take Stoch. Calc., Regr Analysis and maybe PDEs. I know there's a huge gap between applying and researching(although, as you said, i dont fully understand it yet) but thats not the point, the point is that, for an average student who was born in a small town where 80% of the people earns $450/month or less, have an average of 8 yrs education and that the student didnt know about Quant finance and high level math until college, it will be really hard to enter in a top program or get a quant position (still quite possible if you put the effort) even if high-end skills can be developed, but i understand, more than an obligation or a fancy title, a MFE/PhD should be taken to really understand the deep mechanics of quant finance(am I wrong?)

Btw, what's wrong with "thinking finance is cool"?
Thanks a lot for the insights.

 

[bigbadwolf]

I will take Stoch. Calc., Regr Analysis and maybe PDEs. ... a MFE/PhD should be taken to really understand the deep mechanics of quant finance(am I wrong?)

There's no "deep mechanics." The game keeps changing and so the tools used keep changing. You'd be better off with statistics, numerical analysis (including some PDEs), and optimisation. Mixed with coding (C/C++/R/Python/Matlab).

What's "cool" about finance?

 

[CasanovaJ]

Hi CasanovaJ, thanks for the reply!


Great Insight!



I didnt want to sound pretentious, im not qualified at all since im just about to finish my undergrad (Act. Science), I will take Stoch. Calc., Regr Analysis and maybe PDEs. I know there's a huge gap between applying and researching(although, as you said, i dont fully understand it yet) but thats not the point, the point is that, for an average student who was born in a small town where 80% of the people earns $450/month or less, have an average of 8 yrs education and that the student didnt know about Quant finance and high level math until college, it will be really hard to enter in a top program or get a quant position (still quite possible if you put the effort) even if high-end skills can be developed, but i understand, more than an obligation or a fancy title, a MFE/PhD should be taken to really understand the deep mechanics of quant finance(am I wrong?)

Btw, what's wrong with "thinking finance is cool"?
Thanks a lot for the insights.

I don't claim to be an expert on this (at all), but if you're qualified and have the motivation, why do you think it would necessarily be any more difficult for you to come up here from Mexico to do this than it would be for anyone else to come over from any other developing country? Plenty of people seem to pull it off somehow from India and China...

 

[Ling Zhang]

Thanks for the article. Was going through this dilemma of choosing an MFE or PhD. I think I've made up my mind for a MRes + MPhil / PhD in Financial Computing. I hope I made the right choice. I will finish my master's degree in computer and trying to be accepted into a good program.

I am going to NCSU for my PhD in Industrial Engineering and I want to be a quant in the future. But this PhD thing is really a tough choice for me.

 

[Peter Wagner]

Thank you for the nice read. How about a PhD in statistics, I see that you didn't include that in your list?

Do you think it matters a lot where you get your PhD?

Also with so many MFE graduates in the job market wouldn't a PhD be a way to differentiate yourself?

Thanks for your reply, you pose some good questions. A PhD in statistics is *very* marketable, especially with the current focus on data.

Does it matter where you get your PhD - certain schools catch the reader's eye, but when you meet with a lot of very smart people and they probe what you know, I don't think the school is at all important. Princeton may get you an interview that Rutgers wouldn't, but it won't get you the job in the end.

Agree wholeheartedly that there are a lot of MFE students in the job market, and a PhD does differentiate you. However, I stick by my premise that it's not the PhD itself that gets you the top job.

 

[ElephantParks]

One mentor once said

"You are only as good as your last project."

One comedian once said
"I don't want to belong to any club that will accept people like me as a member."

 

[Daniel Duffy]

One comedian once said
"I don't want to belong to any club that will accept people like me as a member."

Groucho Marx

 

[Liam]

I think people confuHe cause and effect here. People do not hire PhD's because they have the letters after their name. Getting a PhD for the sake of having a PhD is pointless. People hire people for their skills. It just so happens that you can learn incredible data analysis and modelling skills during a PhD, and these are the people who go on to good roles in banks. You can also not learn much in the way of data analysis. I know from personal experience, I am orders of magnitude better at modelling and data analysis now, in the final year of my PhD, than after my masters, and I did the world's top ranked masters in maths at the world's best university. This is mainly because of the rigour and high standards you are held to when publishing cutting edge science. Passing hard exams is one thing: pushing the boundaries of human knowledge is quite another. You learn the latter in a good PhD, which is why it can be useful for quant work.

The article and this post sum it up succinctly - this is point I've been trying to get across to people, particularly MFE graduates that can't get a quant job for whatever reason. Usually it's because they didn't get those skills, as you say they did MFE to have something "good to have" and it's about skills not qualifications. Then when they fail they start looking for a PhD confusing number employed with PhDs with the PhD "being better".

Even with those skills, doing a non-mathematical job (people sometimes do a "stop gap" as getting a quant job can take months and months or people get misdirected/moved in the wrong direction) is one way MFE or PhD becomes useless, as your skills go to waste. I'm running a data analysis site now. Having not really used any maths professionally in 7 years when I started, relearning equations took some time and the analytical and thinking side was something that took a little (to be fair that wasn't too bad). What many maths grads in this situation would do is plug away at whatever - networking, job applications, dumb career advisor after advisor, and miss the point underestimating how bad their CV is. It seems that because of the culture of assuming anyone that can solve an equation can do any maths job including quant, relearning is sniffed at, and at best people do well meaning but useless 3 month courses in modelling. By then letters after your name have faded into insignificance - again a reskilling, not a poxy course, is usually best. What about building models at home? If you're that good you will put in the extra things that are useful and showcase real jobs skills, where no course will, and publishing on quantlib is something that employers like to see. Also networking with practitioners you can keep up to speed with what's useful.

Also it's quite similar in other fields where skills and freshness are important, not letters. I mean the amount of times people who have no experience of maths or biostats tell me I'll have no problems getting a biostats job as I "have an MSc" and a "bit of experience" is staggering -maybe it was that simple in a bygone age but not now. Unless they're an expert in a field which I know well, total BS. For starters, even with bookreading I'm not gonna have the same skills of someone who's been testing and writing for clinical trials.

What I find frustrating beyond belief is when I explain this to people and they go "No you're wrong it's not about qualifications". Thing is I'm actually agreeing with what they're saying, it's just the whole thing is beyond their understanding. Even with the underlined bits (join those bits up and ask yourself do people listen?) people will miss my point. Harsh as it is to say, people need to grow a brain to understand this.

 

[Phil]

Hmmm ... very good points raised here.

I would like to add that I've also met my fair share of PhD's who can't perform on the job. I'll tell that instance through the following question:

Suppose you looking for an analyst to help a PM on global macro strats. Who would you rather hire? The Harvard Math / Econs 3.8 GPA undergraduate who readings quant strat literature in his free time Or the state school PhD in digital signal processing?

Okay, so you got a PhD in a somewhat related field, signal analysis, and you've probably picked out a lot of data analysis and modelling skills in your 5 years there. But it isn't that uncommon that the PhD will lack certain things the Harvard guy has

Harvard guy could have ...
- a feel of financial markets
- an understanding of a number of intermarket theories made possible with classes, but not necessary research, in stochastic calculus and general equilibrium.

All relevant skills on the job. (notice none were client facing skills). Yes, I agree that the PhD guy could do things better, ie being the forefront in a specific area. However, being in this industry for a couple of years, I can safely say that with graduate level math classes and two weeks of reading, I can actually understand some of the academic research in financial markets out there. (that's exactly my profile).

Sure, a PhD helps. But I don't see it as a necessity if the technical details of your strategies go only so fair.