This article is dedicated to those of you who are just starting out with quantitative finance or thinking about a career as a quantitative analyst. If you are already a "quant", then you probably want to brush up on your financial engineering via the article section.
First of all, I'd like to congratulate you on making the choice to pursue a career in quantitative finance. You may not be aware of it, but investment banks and hedge funds are constantly on the lookout for new mathematical/programming talent, so no matter what stage of your education you are at (and even if you have no financial experience whatsoever), I believe you're making a wise decision for your career.
Before we continue, I'd just like to introduce myself. My name is Mike, and I became a quantitative trading developer back in April 2010. You can read more about my story by clicking here. Obtaining a career in quantitative finance was certainly not easy for me. Many of you will find that you have to go through a substantial number of interviews at many firms before being offered a job. This can be very demoralising, but remember that the more preparation you put in, the better you will be compared to the other candidates!
I started this website to be a straightforward (and free) resource to make the process of gaining a quant job less difficult for you and to hopefully be around to answer any questions you have while trying to gain that elusive first position. Since I started this website back in 2010, it has had over 15,000 visitors and I'd like to thank you for adding to that!
If you have any questions about what quantitative finance is all about and how becoming a quant can benefit you, click here.
Fundamentally, becoming a quantitative analyst, developer or trader involves gaining a job at an asset management firm (almost certainly a hedge fund or fund-of-funds), an investment bank or a specialised software development company. This is the gateway through which you need to pass in order to be called a "quant" and to springboard your career. Thus, this website is geared to helping you navigate all of the potential educational options, the career paths, the recommended study guides/books and how to work with recruiters, which is the de facto way into a quant job.
I will assume that you are in one of a few key stages in your education or career and wish to ultimately become a quant:
If none of these apply to your situation, I would love to hear from you, so I can make the guide more comprehensive! Click here to contact me. Otherwise, you can read on to see if any of the following sections are similar to your situation.
As an undergraduate reading a mathematically-based degree, such as mathematics, physics, engineering or computer science, you are in a very good position towards gaining a career in quantitative finance. Investment banks spend a great deal of time and money attempting to recruit the top students from Ivy League (in the US) or Russell Group (in the UK) universities. Asset management firms, such as hedge funds, tend to do their recruiting in a more subtle fashion and only from the very best universities.
Your position is good because you have a lot of time - and freedom - to correctly prepare for a position as a quant, if this is definitely what you want to do. For instance, you can steer your course choices towards those which will be beneficial in a quantitative career. Those courses include (but aren't limited to):
Other courses which may be useful, will be Partial Differential Equations, Measure/Integration Theory, Scientific Computing and anything involving mathematical modelling of physical systems, as this is very similar to what you'll be doing in financial engineering.
You should also consider starting to read some of the books off the quantitative finance reading list. If you click here you will find that I've put together some articles about best books to read as a beginning quant.
The biggest decision you need to make is whether to embark on a career immediately upon graduation, or go into a PhD program. My advice is simple here. If you are absolutely sure that you want to be a quantitative analyst, then I would consider applying for a PhD in Mathematical Finance, as opposed to another scientific discipline. This will benefit you in two ways.
Make sure to learn your math(s)...
Firstly, you'll be able to sit in on all of the courses you missed as an undergraduate, particularly any extra mathematics that may be of use AND any mathematical finance courses run for Masters students taking a Masters in Financial Engineering. Secondly, you will be in a much better place to apply to hedge funds (which tend to offer better environments, compensation and intellectual stimulation) than a front-office desk at an investment bank as you will have additional time to spend on your career.
Quants are not often recruited upon graduation from an undergraduate degree, but you may find you're ready for the world of work immediately and so you could enter a bank as an analyst. This is a bank's term for an entry-level position and has nothing to do with the phrase quantitative analyst. You will often be rotated through different desks and roles (such as sales, trading, structuring etc). Make sure you go for a front office role as this puts you "close to the money" (as the phrase goes!), which means you'll be nearer the trading and intellectually challenging work.
One of the big issues as an undergraduate that you will definitely be thinking about is an internship. Here you have two major choices: An investment bank or a fund. It is far trickier to get an internship at a fund, but significantly more worthwhile. Whether it is discussed or not, there is a perceived hierarchy in finance (especially in fund managers' minds!), and working at a fund is seen as more prestigious. It is far easier to transition from a fund to an investment bank than the other way around. Irrespective of the firm you intern with, getting an internship is a difficult process. I will eventually have a lot of material on the site on how to go about getting one.
If you have any questions about what to do as an undergraduate, there are plenty of articles to check out or you can contact me. I'm always happy to hear from students who want to pursue a career in a quant finance. After all, I was one a long time ago!
As a postgraduate you will, like an undergraduate, have a lot of time to consider your career plans. If you have determined that academia is not for you, then a stimulating career in quantitative finance can be a very attractive option. I was working in an aeronautical engineering department, on fluid dynamics simulations. Even though I was extremely keen on the subject, I was less enthused by the prospect of having to consistently seek a post-doctoral position every couple of years and wait for one of those positions to materialise into a permanent post. Hence quantitative analysis was attractive to me.
If you are in a similar situation and wish to consider finance, then the best approach is to get hold of some of the introductory books from the quant finance reading list and study the material, to see if you find it enjoyable. You will need a background in undergraduate mathematical calculus (known as real analysis in the UK), some probability and an awareness of differential equations (particular PDE). Chances are, if you are taking a mathematically-oriented PhD, you will be aware of this material and so can revise/brush up on it.
In addition, many numerate PhD programs require you to learn some programming. In fact, you'll have plenty of opportunity to implement your models in any language you wish while doing a PhD course. Depending upon your field, you will probably be using C/C++, Python, MATLAB or R. Fortunately, these are precisely the languages that tend to be found in mathematical finance. This is not really a surprise given that the financial engineering community is largely made up of former mathematicians, physicists and engineers from academia!
Cambridge University - Many funds recruit PhDs here...
However, academic programming is very different to professional software development, which is not often emphasised at all while implementing models in academia. Further, object-oriented design patterns, version control and other professional programming techniques are often shunned in favour of quick-to-build (and unmaintainable!) procedural codes. If you want to stay one step ahead of your peers when applying for those competitive roles, you should make sure to learn some professional coding techniques. I have written plenty of reading list guides, predominently for C++/Python, but I will be adding other languages in the near future.
Although I've tried to write this article without referring to "current market conditions" (whatever that means!), I have noticed that hedge funds and other quantitative asset management firms are the ones really doing the hiring. A PhD from a top school is pretty much a requirement for one of these positions. By top school, I mean an Ivy League (US) or top-end Russell Group (UK) university. There are certain subject areas that are more relevant than others in the current market. I've listed the most prominent below:
Other areas that are valued include numerical linear algebra, GPU/CUDA programming and algorithmic research. The main issue will be explaining how your PhD research is relevant to a quantitative employer. You can emphasise similarity of financial modelling to your models, your C++ software development experience and your ability to take complex algorithms and rapidly turn them into implementations under pressure. These are all attractive qualities to a fund or bank.
Your next main task will be to start contacting recruiters near the end of your grad school program. In the UK, this will likely mean when your grant/stipend [or other source of] funding runs out! However, bear in mind that you will balancing the need to write up a thesis as well as apply for jobs. Once you have the job you will have to come home and continue writing up that thesis. Trust me on this one - it is not a pleasant experience to be burning the candle at both ends for so many months!
Many PhD students are now taking a Masters in Financial Engineering (or similar course) after their PhD. If you wish to gain some formal education in financial mathematics, then the next section will provide a lot more detail.
Masters of Financial Engineering courses have become a cottage industry within the last 10 years. Quantitative analysis recruitment is a far cry from the days when individuals were solely hired directly from Bell Labs or university physics departments! Financial Engineering has "grown up", so to speak. Hence, the significant growth in courses. I haven't personally been through an MFE course, but I know many individuals who have. At this stage I haven't added a great deal of resources directly geared to applying for an MFE.
However - if you are considering applying for an MFE or are currently on one, then the -best- resource on this is Andy Nguyen's highly successful QuantNet portal. He has a wealth of great content about MFE courses, including a list of MFE trackers (which help you gauge success/failure of applications), guides to invididual programs, deep links with those running the programs as well as a highly-frequented forum for you to ask questions. Check out the site here.
Arriving too early for an MFE lecture...
Having said that, I will be adding plenty of resources for prospective MFE students over the coming months, so keep checking back here regularly. If you've finished (or almost finished!) your MFE program and consider yourself ready for interview, you will want to start thinking about contacting recruiters. I'll be posting more about that in the future..
Obtaining a role in quantitative finance clearly requires a great deal of motivation, but in my opinion the rewards far outweigh any necessary initial effort. You will be working in a modern, fast-moving environment, surrounded by some of the smartest individuals in the industry, being well compensated for your efforts.
Regardless of the stage you are at, it will be necessary to spend a lot of time learning the basics and really getting to grips with the material - both theory and implementation. That means probability, statistics, stochastic calculus, options/derivatives and C++, Python, MATLAB or R. Allocate your spare time wisely - make sure you are getting sufficient theoretical as well as implementational skill. Read the books, but also program up the models. You will learn so much just by trying to build your first object-oriented options pricer or quantitative trading model.
Once you are ready for interviews, follow the advice in the section above and with some consistent application of effort you will eventually land that elusive dream role! Once you do get a job, I'd love to hear your story of success. It is always really motivating both to the other prospective candidates, and myself, to hear how individuals are getting on.
For ease of access, I've listed the resources I mentioned above, in a single list below:
I hope these resources help you with your search and, finally, I wish you good luck in passing that interview!
-Mikecomments powered by Disqus