Preparing for a job in finance/risk management

  • #1
Hi guys,

I am physics student close to my masters and I intend to enter finance after my PhD (Investmentbanking, Hedge- / Quantfonds, Riskmanagement).
For that, I am aware that a PhD in theoretical physics would be preferable since it will increase my programming and modelling skills.

However, I think I lack a necessary mathematical background. Financial and insurance mathematics is based on stochastics. Now, I only had the undergraduate introductory stochastics course with the mathematicians as well as my basic knowledge of probability related stuff in theoretical physics (e.g. statistical mechanics).
But I did not hear pure probability theory / stochastic calculus, nor statistics.

I tried searching for books. First for financial mathematics, but most of them seem to be addressed to economics students and not to the typical mathematician / physicist working in finance.
Similiarly, while there are lots of statistics books out there, lots of them address psychologists or medicine students, while others address mathematicians.

Do I have to learn the "hardcore" version of the mathematicians for my desired job field or is the basic stuff for psychologists enough?
Do I have to learn stochastic calculus?
And, more importantly, do I have to work through these fields first before getting a book about say financial mathematics or is the undergraduate stochastics course enough to understand financial / insurance maths?

I would like to have some guidance in what to learn next and what kind of book to buy. I have ~4 years left to learn in my freetime.

Thanks!
 

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  • #2
FactChecker
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Hi guys,

I am physics student close to my masters and I intend to enter finance after my PhD (Investmentbanking, Hedge- / Quantfonds, Riskmanagement).
For that, I am aware that a PhD in theoretical physics would be preferable since it will increase my programming and modelling skills.
I find it very hard to believe that a PhD in theoretical physics would be better preparation for a career in finance than your current PhD in Investment banking, Hedge- / Quant funds, and Risk management.
However, I think I lack a necessary mathematical background. Financial and insurance mathematics is based on stochastics. Now, I only had the undergraduate introductory stochastics course with the mathematicians as well as my basic knowledge of probability related stuff in theoretical physics (e.g. statistical mechanics).
But I did not hear pure probability theory / stochastic calculus, nor statistics.

I tried searching for books. First for financial mathematics, but most of them seem to be addressed to economics students and not to the typical mathematician / physicist working in finance.
Similiarly, while there are lots of statistics books out there, lots of them address psychologists or medicine students, while others address mathematicians.
I would think that optimization and monte carlo simulation would be beneficial. I know that those subjects are central to the field of Operations Research but I would have expected you to have also learned them in your current PhD field.
 
  • #3
I find it very hard to believe that a PhD in theoretical physics would be better preparation for a career in finance than your current PhD in Investment banking, Hedge- / Quant funds, and Risk management. I would think that optimization and monte carlo simulation would be beneficial. I know that those subjects are central to the field of Operations Research but I would have expected you to have also learned them in your current PhD field.
Hey FactChecker, thanks for your reply.
It seems I was not clear enough: I am not doing a PhD right now, but the final term of my masters. Afterwards, I intend to do a PhD (theoretical physics?) and after the PhD, I would like to work in finance. The brackets in my first sentence are with respect to my expression that I would like to work in finance :)
 
  • #4
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Hey FactChecker, thanks for your reply.
It seems I was not clear enough: I am not doing a PhD right now, but the final term of my masters. Afterwards, I intend to do a PhD (theoretical physics?) and after the PhD, I would like to work in finance. The brackets in my first sentence are with respect to my expression that I would like to work in finance :)
I am not an expert in finance, but I am sorry to say that I think theoretical physics is poor preparation for your desired employment in finance. Classes in finance would give you much better preparation for working in Investment banking, Hedge- / Quant funds, and Risk management. I would also recommend classes that include optimization, computer science (as required), and Monte Carlo simulation methods.
 
  • #5
I figured as much... there are PhD programs in financial mathematics open for physicists, but I am afraid to leave physics forever and regret it afterwards :(
Also, there are several theoretical physicists in finance, aren't there... so I thought it would be a good idea to stay flexible, make a PhD in th. physics and learn the necessary mathematics on my own. And in a theoretical physics PhD it should be easy to become experienced in Monte Carlo simulations for example...
 
  • #6
FactChecker
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And in a theoretical physics PhD it should be easy to become experienced in Monte Carlo simulations for example...
I doubt that very much. There may be some occasional encounters with optimization, computer science, and Monte Carlo simulation techniques in theoretical physics, but not nearly with the intensity that you would get in a class on those subjects or that you may need in a job of financial risk analysis. I learned those subjects in Operations Analysis.

Hopefully, some theoretical physicists can either correct, confirm, or add to my thoughts on that.
 
  • #7
I doubt that very much. There may be some occasional encounters with optimization, computer science, and Monte Carlo simulation techniques in theoretical physics, but not nearly with the intensity that you would get in a class on those subjects or that you may need in a job of financial risk analysis. I learned those subjects in Operations Analysis.

Hopefully, some theoretical physicists can either correct, confirm, or add to my thoughts on that.
The question is, wouldn't some encounters be enough. I mean, there seem to be a lot of physicist in finance, I doubt they all did a second PhD in financial maths. Probably have lots of programming, a few nice simulation techniques and a good background in statistics (which I still lack).
 
  • #8
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1) There are not a lot of physicists in finance; in fact, physicists are essentially non-existent in most areas of finance. However, it is not terribly rare to find a physicist in the *very specific* area of finance you're interested in.

2) Physicists in quant/risk type areas rely heavily on their computer programming background, among other things. If you don't have that, or won't after your phd, then you won't get those jobs. You're more worried about the math, but I'd suggest you be worried about the programming - unless you already have it.

3) The whole "physicists in finance" thing was much bigger 2000 - 2008. Not saying it's not there now, but the financial collapse changed the game a lot. When you're gathering information, make sure it's current.

4) Make sure you know what you're getting into. There's something exciting about doing cutting edge work in fintech spaces. There's something much less exciting about doing regulatory work to satisfy regulators. The second does tend to be more stable though. Well, until the law changes.

5) Reconsider binning yourself into finance. Anyone who combines strong statistics or optimization background with good programming skills can break into a very wide range of industries, many of which are very lucrative. Consider finance, robotics, engineering, business analytics, actuarial work - anything that suits your skills. Decide between them based on the offers in hand.

Best of luck.
 
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  • #9
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5) ... Anyone who combines strong statistics or optimization background with good programming skills can break into a very wide range of industries, many of which are very lucrative. Consider finance, robotics, engineering, business analytics, actuarial work - anything that suits your skills. Decide between them based on the offers in hand.
I agree with this. Unfortunately, @SchroedingersLion seems to think that casually picking up some occasional experience with these subjects will be enough. That concerns me.
 
  • #10
1) There are not a lot of physicists in finance; in fact, physicists are essentially non-existent in most areas of finance. However, it is not terribly rare to find a physicist in the *very specific* area of finance you're interested in.

2) Physicists in quant/risk type areas rely heavily on their computer programming background, among other things. If you don't have that, or won't after your phd, then you won't get those jobs. You're more worried about the math, but I'd suggest you be worried about the programming - unless you already have it.

3) The whole "physicists in finance" thing was much bigger 2000 - 2008. Not saying it's not there now, but the financial collapse changed the game a lot. When you're gathering information, make sure it's current.

4) Make sure you know what you're getting into. There's something exciting about doing cutting edge work in fintech spaces. There's something much less exciting about doing regulatory work to satisfy regulators. The second does tend to be more stable though. Well, until the law changes.

5) Reconsider binning yourself into finance. Anyone who combines strong statistics or optimization background with good programming skills can break into a very wide range of industries, many of which are very lucrative. Consider finance, robotics, engineering, business analytics, actuarial work - anything that suits your skills. Decide between them based on the offers in hand.

Best of luck.
Thank you for your input! Now, to point 5). What do you think with respect to FactChecker's concerns? Will it usually be enough to get some experience in these fields in a theoretical physics PhD + some free time study on my own or do you agree with him that you probably need a professional education in that matter, like a PhD in mathematical finance. I mean, of course you would be better prepared after taking such a program. But from what I see in these areas they are hiring physicists and mathematicians and I doubt all of them have taken a special PhD program. Large investment banks like Goldman Sachs simply demand a "highly quantitative" background on their analyst positions. Quantfonds might want more than that, I am aware.
 
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  • #11
FactChecker
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Thank you for your input! Now, to point 5). What do you think with respect to FactChecker's concerns? Will it usually be enough to get some experience in these fields in a theoretical physics PhD + some free time study on my own or do you agree with him that you probably need a professional education in that matter, like a PhD in mathematical finance. I mean, of course you would be better prepared after taking such a program. But from what I see in these areas they are hiring physicists and mathematicians and I doubt all of them have taken a special PhD program. Large investment banks like Goldman Sachs simply demand a "highly quantitative" background on their analyst positions. Quantfonds might want more than that, I am aware.
In my mind, this is the key question and input from other people would be good. I do admit that a person of PhD caliber should be able to learn the subject matter on his own but I think it would require intense study. I went into Operations Research after obtaining a PhD in pure mathematics. I studied these subjects intensely for a couple of years under the guidance of the University of Pittsburgh Industrial Engineering Department, in a masters degree program. I left for a job in the economics analysis group of a large (but not a financial) company. But there are many routes to follow and there is no reason that yours needs to be the same as mine.
 
  • #12
In my mind, this is the key question and input from other people would be good. I do admit that a person of PhD caliber should be able to learn the subject matter on his own but I think it would require intense study. I went into Operations Research after obtaining a PhD in pure mathematics. I studied these subjects intensely for a couple of years under the guidance of the University of Pittsburgh Industrial Engineering Department, in a masters degree program. I left for a job in the economics analysis group of a large (but not a financial) company. But there are many routes to follow and there is no reason that yours needs to be the same as mine.
I understand, and maybe there are regions that are so highly advanced that you need a special education in that matter (like some quantfonds). Yet there still seem to be areas that also put some focus on that field, but still are happy with having a PhD of a quantitative field and "good knowledge of statistics and programming". Just read it on a consulting website same as on a large investment bank website. So they either don't go THAT deep or they are willing to train ther newbies. The consulting group also offers part time M.Sc. in financial mathematics at a good international school...

So suppose I am not strong willed enough to do a PhD in that field, mb because I am not 100% sure of my current opinion, and I go into a theoretical physics PhD, trying to learn as much as statistics / financial maths by myself as possible:
Can you answer my original questions? What should I start with and what should I focus on, given the limited time I have to learn (evenings/weekends)?
 
  • #13
FactChecker
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Get some textbooks on the subject you want to work in and see if you are prepared for the job.
 
  • #14
Päällikkö
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Investment banks and quant funds have different needs.

Roughly speaking, the former are in the business of making markets, so they hedge their risk and are "pricing in Q", which is why all the traditional models are about no-arbitrage and replication, and to actually write something down that gives numbers, stochastic calculus, changes of measure and all that jazz.

The buy side, on the other hand, are generally "pricing in P", which is to say that they are looking for historical indicators of performance and signals to generate alpha, i.e. to beat the market. As they will not be entirely hedging their portfolios, but rather taking a view and hoping it goes their way more often than not (but here of course I am simplifying, there are many strategies), no-arbitrage pricing is often at a fundamental level irrelevant and so stochastic calculus less important.

The hiring practices, then, too are different: There are degrees, Master's in Financial Engineering etc, that cater directly to the needs of derivatives pricing, i.e. banks. There are thus plenty of introductory books in the space, say Hull, Baxter&Rennie, or Oksendal cover the bare minimum. To get hired then, yes, you should know the maths and the products at the level of your competition, and a physics PhD in and of itself is not of much use. The one thing physicists often have going for them, though, is that they've done a fair bit of programming during their degrees, but this is assuming you're actually programming and knowledgeable of all the software engineering practices that it implies as opposed to just writing small scripts and snippets of code for a singular purpose. The buy side is obviously much more secretive about their methods as you always have to stay ahead of the curve to make money, but machine learning and general statistics as applied to finance should be useful.

Almost all the numerical methods in finance are in principle easy and most are covered in a typical physics undergrad (interpolation, solving ODEs, SDEs and PDEs, simulating Monte Carlo). The difficulty lies elsewhere.

If you insist on going through the physics PhD route, what you should do is to show with your publication record that you could well have made a career out of academia, but in the end chose not to. That should be your primary goal. Second to that, if you are already convinced that you want to work in quantitative finance, you should figure out which part of it is most appealing to you and then concentrate on the skills that the interviews in that are might ask about: is it stochastic calculus, say, or is it machine learning and statistics?

As for job postings: They need not be accurate and a PhD without some proven track record of interest will not be invited to interviews even if the posting may only ask for people with quantitative backgrounds to apply. And at any rate, the practice is often to hire fresh graduates, or PhDs, as interns, and then convert some of them into full-time. People studying for an MFE, say, are better connected and finding these internships is easier for them and can even be a part requirement for the degree.
 
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  • #15
Thank you Päällikkö for the detailed answer!

I am still too new to the world of business and finance to completely understand your explanation of who needs what and why.

Investment banks and quant funds have different needs.

Roughly speaking, the former are in the business of making markets, so they hedge their risk and are "pricing in Q", which is why all the traditional models are about no-arbitrage and replication, and to actually write something down that gives numbers, stochastic calculus, changes of measure and all that jazz.

The buy side, on the other hand, are generally "pricing in P", which is to say that they are looking for historical indicators of performance and signals to generate alpha, i.e. to beat the market. As they will not be entirely hedging their portfolios, but rather taking a view and hoping it goes their way more often than not (but here of course I am simplifying, there are many strategies), no-arbitrage pricing is often at a fundamental level irrelevant and so stochastic calculus less important.
Do both of these passages refer to investment banks? That means there is a buy side of a bank that uses less stochastic calculus and a sell side that uses high amount of stochastic calculus. What about statistics there? And what about Quant funds? Sorry, but could you try to explain that again without 'bank' slang =(?

The hiring practices, then, too are different: There are degrees, Master's in Financial Engineering etc, that cater directly to the needs of derivatives pricing, i.e. banks. There are thus plenty of introductory books in the space, say Hull, Baxter&Rennie, or Oksendal cover the bare minimum. To get hired then, yes, you should know the maths and the products at the level of your competition, and a physics PhD in and of itself is not of much use. The one thing physicists often have going for them, though, is that they've done a fair bit of programming during their degrees, but this is assuming you're actually programming and knowledgeable of all the software engineering practices that it implies as opposed to just writing small scripts and snippets of code for a singular purpose. The buy side is obviously much more secretive about their methods as you always have to stay ahead of the curve to make money, but machine learning and general statistics as applied to finance should be useful.

Almost all the numerical methods in finance are in principle easy and most are covered in a typical physics undergrad (interpolation, solving ODEs, SDEs and PDEs, simulating Monte Carlo). The difficulty lies elsewhere.

If you insist on going through the physics PhD route, what you should do is to show with your publication record that you could well have made a career out of academia, but in the end chose not to. That should be your primary goal. Second to that, if you are already convinced that you want to work in quantitative finance, you should figure out which part of it is most appealing to you and then concentrate on the skills that the interviews in that are might ask about: is it stochastic calculus, say, or is it machine learning and statistics?

As for job postings: They need not be accurate and a PhD without some proven track record of interest will not be invited to interviews even if the posting may only ask for people with quantitative backgrounds to apply. And at any rate, the practice is often to hire fresh graduates, or PhDs, as interns, and then convert some of them into full-time. People studying for an MFE, say, are better connected and finding these internships is easier for them and can even be a part requirement for the degree.
This I understand better... so I should try to make a good PhD at the best school possible and make sure I keep improving my programming skills.
Concerning the maths: So it seems like there are branches that rely more on statistics and others that rely more on stochastic calculus.
Do the books you mentioned above cover both the stochastic calculus and statistic parts of finance and should I start with them?
Or would it be a good idea to work through an undergraduate course of stochastic calculus first and then decide where to go (statistics vs stochastic calculus).
A solid background of stochastic calculus is needed for advanced statistics, isn't it?

Thanks again for your detailed answer, sorry for my newbieness.
 
  • #16
Päällikkö
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Thank you Päällikkö for the detailed answer!

I am still too new to the world of business and finance to completely understand your explanation of who needs what and why.


Do both of these passages refer to investment banks? That means there is a buy side of a bank that uses less stochastic calculus and a sell side that uses high amount of stochastic calculus. What about statistics there? And what about Quant funds? Sorry, but could you try to explain that again without 'bank' slang =(?
They do not: Sell side, buy side, market maker. So in some very simplified world, you have a hedge fund (buy side) that thinks that the stock market is going to go down, way down. They have this view because maybe they did a historical analysis of past asset returns or they know that the flattening of the yield curve is a leading predictor of recessions, or they believe what their machine learning algorithm said will be so. They want to take a big leverage on this, for example, so maybe they buy out of the money put options instead of selling short. They either do an OTC deal or go through an exchange to find someone to sell this to them. Suppose no hedge fund has the opposing view (this is of course very simplified, but some product types are actually structurally sort of like this, like mortgages and linked products are going to be a very one way market I'm sure), but you'll find a bank (sell side) ready to sell to you. Why's the bank ok with selling this to you? Because they're going to buy the same thing from someone else. But here of course I cheated: I just told you nobody else is selling this stuff. So what they're going to do instead is, in our simplified world, use Black-Scholes and buy/sell the underlying stock instead of the option (where the market is more liquid both ways) as the market moves, thus hedging through a dynamic replication strategy.

That sounded complicated. Let's make it even simpler. You take historical values of some stock, today valued 100, put it through a linear regression and predict that a year from now its value is going to be 110. Instead of paying 100 today and waiting next year to sell at 110, realizing a profit of 10 (which you'd discount to today), you want to enter a deal where you don't pay anything today to enable you higher leverage. So you agree with me to a forward contract: a year from now you are going to buy 100 shares from me at price X. You're obviously going to immediately sell them a year from now (and assuming no bid-ask spread), and if your prediction of 110 was correct, you are going to make a profit of 100 * (110 - X). Maybe you'd then be happy to buy if I sold at 105, depending on the standard deviation, and your risk appetite, right?
Well, you shouldn't in the no-arbitrage world. Simplifying further by assuming zero interest rates, I can replicate the payoff: I borrow to buy 100 shares @ 100 today and hold them until maturity at which point I sell them to you and pay off my loan. I can lock in the price of 100 today and if I can sell them to you at 105 I know for sure that I made money regardless of what the stock market does. But you, too, will have made money if your prediction of 110 turns out to be correct. You are "pricing in P", i.e. the physical measure by trying to essentially predict the probability density function of the stock a year from now, whereas I am "pricing in Q", the risk-neutral measure by deducing the prices of more complicated contracts from prices of simpler things that I can directly observe (even though conceptually they are very different, doing a bit of math you'll see that the frequentist P and the replicating Q are related). So banks are happy to be selling derivatives contracts (such as the forward above, or the put option before) if they think they can manufacture them, ie. replicate the payoff, so that they make a profit.

I know this is very simplified and stylized, but I hope it somewhat clarifies the different players. If my explanation didn't make sense, I suggest you pick up a book, or maybe just read online what Black-Scholes is about (the main thing they said, in maybe more modern lingo, is that the replicating argument works for more than just linear payoffs like the forward above).

This I understand better... so I should try to make a good PhD at the best school possible and make sure I keep improving my programming skills.
Concerning the maths: So it seems like there are branches that rely more on statistics and others that rely more on stochastic calculus.
Do the books you mentioned above cover both the stochastic calculus and statistic parts of finance and should I start with them?
Or would it be a good idea to work through an undergraduate course of stochastic calculus first and then decide where to go (statistics vs stochastic calculus).
A solid background of stochastic calculus is needed for advanced statistics, isn't it?

Thanks again for your detailed answer, sorry for my newbieness.
All the books I listed are introductory stochastic calculus-type of finance books. Well, the last is more of a maths introduction to the subject, so if you have some maths background, that may be the easiest one to approach. The first two have more about specific products, as well, and are relatively standard reading, requiring no maths background to pick up.

There's not that much to stochastic calculus itself: Ito's formula, conditional expectations and change of measure will get you most of the way. Also knowing how to solve some of the simple SDEs that you may have already encountered in your statistical physics classes, like Ornstein-Uhlenbeck, is useful.

As for stats, depends on what you're doing, but stochastic calculus does not usually play a big part.
 
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  • #17
Thank you very much again!

I understood it better this time, however I seriously need to learn about all the concepts and ideas.
Are the first two books you recommended good for this (to learn about the names, the ideas, the institutions, more than the math)? I would start then with one of them, as well as an introductory book about statistics because I think there are more positions out there (also outside of finance) that use statistics. I have also searched for firms in finance or consulting that go in the direction of financial / risk modelling. Most of them demand statistics.

May I ask about your background? Are you working in that area?
 
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  • #18
FactChecker
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In my opinion, you can't go wrong by knowing as much statistics as you can stand, no matter what field you end up in. Certainly that is true of so many financial fields. And statistics requires some familiarity to avoid many traps and misconceptions.
 
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  • #19
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I agree with FactChecker about statistics, and also want to encourage something more (preferably much more) than "traditional" statistics such as hypothesis testing or time series. Look into the more modern stuff. An example of "older" modern material might be Elements of Statistical Learning, which contains a great deal of material you don't get in most stats classes, and lays a good foundation for even more recent developments.

Also, programming and modern cloud computing architecture are highly recommended.
 
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  • #20
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Thank you for your input! Now, to point 5). What do you think with respect to FactChecker's concerns? Will it usually be enough to get some experience in these fields in a theoretical physics PhD + some free time study on my own or do you agree with him that you probably need a professional education in that matter, like a PhD in mathematical finance.
This is entirely dependent on your competition. In finance you have financial engineering degrees. So if you are applying for a job and have the same technical background as another applicant, but they also have domain knowledge, you may well lose, all other things being equal. Part of your job is to make them not at all equal.

On the other hand, where I work we look for smart people who have done difficult things using skills we leverage. I've been specifically told not to worry if applicants experience/research/education is far afield from what we do. Domain/business knowledge is critical to success, but it's honestly probably the easiest part to pick up on the job. I'm betting there are plenty of financial groups out there who, when faced with a candidate with exceptional skill and who seems like someone they'd love to work with, but who does not have much domain knowledge, would be willing to give them a try. I could be wrong, but if I am, that's their loss.

You can also often pick up some significant domain knowledge very quickly. If you have three interviews in three different industries, a few hours a night may give you some basic understanding you can leverage in the interview. You can't develop the underlying skills as quickly.

So my vote is that you pick something heavily mathematical that leverages at least one real programming language, do it exceptionally well, keep track of various other fields as a hobby, and then as you approach graduation look for interesting places to intern.

But that's just some random internet person's opinion. Take it as you will.
 
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  • #21
Thanks Locrian!

I think I have made up my mind then. Theoretical physics PhD with emphasis on developing good programming skills as well as learning one or another simulation / optimization method, and additional studies of statistics/finance/stochastic calculus. And aiming for internships.
And if I don't end up in the part where they use the most advanced maths or techniques, I will also be happy with the next best.
 
  • #22
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I think you're missing a piece of the puzzle. If you don't love physics, pursuing a physics PhD purely to get a role in a financial institution is a really stupid idea. Why spend 4 years on a PhD when you can get the skills in an MFE in a year or 2 and gain a lot more like an internship, industry connections.

I hope you love physics and can stand doing physics research for 4 years before ending up in a financial. Because if you don't, you're going to hate life.

Good luck.
 
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  • #23
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It can be very disappointing to get a PhD in one field and then have to treat it like it is just a hobby when you get a job in another field. And a theoretical physics PhD is extremely difficult to complete. You may have some very hard decisions to make.
 
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  • #24
I think you're missing a piece of the puzzle. If you don't love physics, pursuing a physics PhD purely to get a role in a financial institution is a really stupid idea. Why spend 4 years on a PhD when you can get the skills in an MFE in a year or 2 and gain a lot more like an internship, industry connections.

I hope you love physics and can stand doing physics research for 4 years before ending up in a financial. Because if you don't, you're going to hate life.

Good luck.
I definitely love physics (seriously, I have posters of Einstein in my room, on the table next to me is "Surely you're joking Mr. Feynman" and to christmas I wished for the two textbooks about quantum mechanics of Claude Cohen Tannoudji lol), which is why I didn't go with FactChecker's advise to simply do a PhD in the financial engineering field immediately.
I am afraid to start my PhD in financial engineering and then figure out that I miss physics too much. What I dislike about physics, however, are the very limited options to make career (as big as in business/finance). Academia job perspective is terrible and I would hate to work in a lab for some company later on, only for economy guys (who had a far easier field and far less time to study) to decide what I am going to do, while they are the ones making big money and climbing up the management ladder. So I was searching for a tradeoff: The career perspective of business (like consulting) in combination with a job that is similiar to what a physicist does, for example modelling complex systems. And this led me to finance.

But I appreciate the advice!
 
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  • #25
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I should clarify my post #23. I left mathematics after getting a PhD and it caused me a lot of grief for decades. But I am glad now that I did. It worked out well and really expanded my areas of interest. Forums like this allow a person to remain active in many areas of interest -- and it can be more fun than a job. So I believe that your decision can turn out as well as mine did.
 
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