Can a graduate student afford his/her time on gaining industrial skills?

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Discussion Overview

The discussion revolves around the feasibility and implications of graduate students in mathematics pursuing industrial skills, particularly in relation to quant jobs in the financial industry. Participants explore the balance between academic demands and the acquisition of practical skills, as well as the evolving landscape of job opportunities in finance.

Discussion Character

  • Debate/contested
  • Exploratory
  • Technical explanation
  • Conceptual clarification

Main Points Raised

  • One participant expresses uncertainty about the ability to manage graduate-level statistics courses alongside rigorous mathematics coursework, questioning the overall feasibility of balancing these commitments.
  • Another participant warns against the unpredictability of the financial industry, noting that skills needed may change significantly over time and that current trends show declining salaries and bonuses.
  • Some participants suggest that a math Ph.D. may be sufficient for quant jobs, emphasizing the importance of adaptability and the ability to learn quickly rather than specific knowledge.
  • There is a suggestion that developing "people skills" may be more beneficial than additional statistics courses for Ph.D. candidates entering the industry.
  • Concerns are raised about the long-term viability of finance careers, with one participant noting that while salaries are currently attractive, future conditions are uncertain.
  • Another participant reflects on their own graduate school experience, indicating that while research can be consuming, there is room for personal development and exploration of diverse interests.

Areas of Agreement / Disagreement

Participants do not reach a consensus on the best approach for graduate students to prepare for quant jobs. There are competing views on the importance of specific skills versus general adaptability, and the discussion remains unresolved regarding the future landscape of the financial industry.

Contextual Notes

Participants express various assumptions about the current job market and the nature of skills required in the financial industry, highlighting the uncertainty surrounding future employment conditions and the relevance of specific academic courses.

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Hi. I will be starting undergraduate studies in mathematics in this autumn. Given the unemployable nature of the subject only with a bachelor's degree, I will most definitely pursue graduate studies. At this point, I am not wholly determined to go for a Ph.D., and I am not sure about my capability either.

With my masters or Ph.D. in mathematics, I want to apply for a quant job. And I think it will behoove me if I take graduate-level statistics courses during my graduate studies. I am absolutely going to take undergraduate-level statistics courses as many as possible while I am doing my bachelor's degree, but my question is, is it realistic to take graduate-level statistics courses whilst being inundated with graduate-level mathematics courses? I have heard that graduate school is quite stressful, so I am wondering if I can afford time on other courses or even acturial exams.

Or is a math graduate degree, regardless of what the concentration is, just enough to land on a quant job? I highly doubt that because I have heard that the competition is fierce. From this forum, I gained some impression that quant jobs are not as hard as what I would imagine like that of Wall-Street jobs with an MBA, however.

Always appreciate any kind of response to this thread,
 
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set said:
With my masters or Ph.D. in mathematics, I want to apply for a quant job.

Bad idea.

No one knows what the financial industry will be like in five years, and no one knows what skills would be useful in five years. Salaries have been trending down, and bonuses have gotten slashed over the last few years.

And I think it will behoove me if I take graduate-level statistics courses during my graduate studies.

Or you can buy a bunch of books from Amazon and then read them over the weekend.

Or is a math graduate degree, regardless of what the concentration is, just enough to land on a quant job?

A math Ph.D. should be good enough. One reason that banks hire math/physics Ph.D.'s is not for what they know, but for what they can learn quickly. For example, in a project that I was doing recently, I had to teach myself some mathematics that I had never seen before, so I just went on google, downloaded a lot of papers, and then learned what I needed to know.

There is a good demand for people right now. One reason for that is that the regulators are passing a ton of regulations, and trying to deal with those regulations involves some "research-y" topics. What things will look like in five to ten years, I have no clue.

It's probably best, not to focus on anyone particular type of math, but getting good at learning math quickly. It's assumed by employers that if you are a genius at algebraic topology, that if you need to learn statistics, you can do it.

From this forum, I gained some impression that quant jobs are not as hard as what I would imagine like that of Wall-Street jobs with an MBA, however.

It's not that hard for a Ph.D. to get a quant job, because MBA's want jobs in finance whereas for physics/math geeks, a job in an investment bank is a second or third choice. If you have an group of MBA's, then they will climb over each other to work in an investment bank.

However, most physics Ph.D.'s (including myself) would rather work somewhere else if the jobs were available. If someone offered me a position doing astrophysical simulations at a fraction of my current salary, I'd be out of here (and so would most of my coworkers).
 
Also the industrial skills that Ph.D.'s really need to work on are "people skills".

A math Ph.D. is likely to have no trouble doing high level statistics. Learning to figure out when someone is getting bored or annoyed at what you are saying and to shut up, is a different skill. Spending the summer trying to sell real estate or being a telemarketer is likely to be more useful than to take statistics courses.
 
twofish-quant said:
Bad idea.

No one knows what the financial industry will be like in five years, and no one knows what skills would be useful in five years. Salaries have been trending down, and bonuses have gotten slashed over the last few years.

NOOOOOOOOOOOOOOO! I thought differential equations are pretty cool and useful in finanical industry, but if that is the case, I'll become an actuary :). May I ask one last question before developing this thread any further?

How was grad school like for you? Were you always preoccupied with new research ideas? Or could you afford some time for personal development on weekends?
 
set said:
NOOOOOOOOOOOOOOO! I thought differential equations are pretty cool and useful in finanical industry, but if that is the case, I'll become an actuary :)

It's good to be flexible. It's worth noting that salaries in finance are still better than anything else for physics Ph.D.'s. But whether that will be true five years from now is an open question, there is a ton of political and social pressure to cut salaries and bonuses.

This is when "deep thinking" becomes useful. The standard bit of corporate propaganda is that large salaries for people in finance are socially justifiable because it results in efficient allocation of capital. Is this true? Whether or not it happens to be true or not will determine what happens next, so make your own judgment, and put your money where you think things will go.

How was grad school like for you? Were you always preoccupied with new research ideas? Or could you afford some time for personal development on weekends?

The problem then as now was "too many ideas." There are a million interesting things in the universe, and I just don't have time to study any more than a fraction of them. And sometimes you end up with weird and unexpected connections. One thing that I do sometimes is to go to a bookstore and then find a book at random and read it. One time, I ended up with a book on the collapse of Austrian-Hungarian empire. Nothing to do with my research. Except that after a few years later, someone pointed out that a *lot* of mathematicians were Hungarian, and having some background knowledge about the history of Austria-Hungary led me to understand a bit about how that happened and how it fell apart. And then there is Austrian economics

One good thing about graduate school and industry is that it forces me to focus on one or two ideas and see where they lead.

There's a tricky balance. On the one hand, too much focus leads to tunnel vision and a lot of the most creative ideas happen when you get two unrelated ideas colliding at high speed. On the other hand, it's possible to get too distracted and get nothing done.
 
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