Kings' College London - MSc Financial Mathematics

Summary:
I was originally attracted to apply to this course due to its relatively low level of tuition fees (~£12,000 for home students), and the option to pursue this course part-time. I completed this course while employed full-time in a Data Science graduate scheme across two years, as I wished to move towards a role in quantitative finance. I managed to graduate with Distinction, and a high-ranked dissertation, leading to a quant role within a systematic hedge fund.



Application Process:


Simple process with just a personal statement, however I would make sure that your undergraduate programme has the right level of mathematics taught for this course, you should have some familiarity with Multivariable Calculus and know how to solve differential equations at A-Level Further Maths or 1st year of undergraduate level.

Modules and Course Content:


Throughout the course, you are expected to complete 8 taught modules during the main semesters, and a research project + dissertation running from May-August.

The course begins with a non-credit week on Mathematical Analysis to get students up to speed in the style of teaching expected from a degree in the Mathematics department. I have spoken to plenty of students who did not come from a direct Mathematics/Statistics background that found later modules difficult as they were not familiar with the Definition/Theorem/Proof-style teaching of rigorous maths courses.

Afterwards, the first term consists of 2 compulsory modules (Probability Theory and Risk-Neutral Valuation) which forms the basis of much of the content taught in other modules offered by the department.

The content taught here was well-timed as they form the basis of what’s expected in quant interviews, I recommend students use this material directly for their interview preparation as soon as they start the programme.

The rest of the 6 modules you need to pick are entirely decided upon by you and are held across the different semesters. In particular, I had picked Computational Finance (Python), Stochastic Analysis, Machine Learning, Stochastic Control and Algorithmic Trading, C++ in Finance, and Interest Rates & Credit Risk.

Other options in the department focus on areas such as Statistics in Finance, Financial Markets, and even modules offered by the CS department such as High-Frequency Trading.



The teaching in general can be quite good to sometimes not so great. My favourite modules in the programme were Computational Finance (John Armstrong), Stochastic Analysis (Ryan Donnelly), and Algorithmic Trading (Ryan Donnelly). These lecturers here were very passionate about their work and it made a difference in my own learning of the material.

I would say the enjoyment and satisfaction you receive from the modules and teaching really depends on your work ethic as a student. I personally took time off from my full-time role to make an effort to attend the small group teaching classes for each module, which I found to significantly improve my understanding of the content (and in turn help my exam grades too). 



However these classes were often attended by less than a quarter of the students taking the respective module and many of these missing students often complained about the high difficulty of the material taught - beware, it’s not an easy or free degree in any sense.


Research Project and Dissertation:

With the research project and dissertation, you get to pick from three overarching themes each year, which are quite broad and cover a variety of aspects taught across the different modules.

During my year, the themes covered deep learning for option pricing, algorithmic trading + market making, and fractional brownian motion/rough volatility. Each of these projects do correspond to a couple of different modules taught in the programme, so pretty much anything you learn will be of use somewhere.



How you approach the specific contents of the dissertation is open-ended, but each theme has a specific range of tasks set out for you to get familiar with the theme and research topic, covering about 30% of the available marks. The rest of the marks come from an independent extension of the theme. The project also heavily emphasises on the use of programming to validate your work and to showcase your results, which I find reminiscent of how research in industry is truly done.



Throughout the dissertation, you have the opportunity to discuss your ongoing work with an academic supervisor at a few key stages. It’s really crucial that these meetings are attended with a substantial amount of progress in-between as to get proper feedback on the direction of your work. I made the mistake of not making much progress on my first meeting, but then really pushed far ahead for my second, which provided me with useful feedback for the final sections of my work.

This dissertation served as a major project for me when applying for roles in quantitative finance, and I was certainly able to learn a variety of applicable skills that resonated with different roles and teams in the industry, especially the ability to translate mathematical theory into code.

Top ranking students in the two compulsory modules can get offered to be paired up with an industry partner (Santander) for their projects.

Finally, high ranking students in the research project/dissertation can be offered PhD positions within the university.

Careers Services and Destinations:


KCL is in a good location being in central London which opens up accessibility to many careers events not just at KCL but also at LSE, UCL, and Imperial. If you’re keen on networking then you won’t find a shortage of it in London.

I didn’t particularly interact with the university’s own careers services much and neither did my friends. I would say this area could be something KCL is lacking in if you’re expecting careers support of the level of LSE for example.



As for destinations, many of my closest peers are indeed in strong careers at financial institutions including large banks, hedge funds, and exchanges, across a variety of different roles from quant research to financial risk analysis. 

KCL’s reputation doesn’t quite fall into the radar of the most elusive quant destinations, but one can certainly jump into a successful career from here. 



Though ultimately, I found this to be an appropriate course for what I was looking for and found the career that I desired and it was incredible value for money comparing other similar courses which can run over £25k for even home students. But if offered, I would definitely have picked Imperial, LSE, UCL due to their relevance and reputation in quantitative finance hiring.

The course is good, you have plenty of options to choose from, the professors are highly educated, very knowledgeable have written books, and respected in quant field. But there is little to no help for career services. You are left alone to find the job. You have to self study a lot. The professors in Class just give you like a overview of the topic. It's mostly self study. The paper pattern in the for the Exam is not good. You have for questions and you have to solve any three so each carry like 33 marks. You don't get to choose your own dissertation topic. You are presented with three topics, and you have to choose one so your classmates will be working on same thing, with nearly same results. There are no internal marks. Most of the exams are hundred percent written paper.
As it is a master course, everyone is very busy. No one is networking. Everyone just comes to the unique studies and leaves. I have seen my course mates get good roles in companies and I've also seen people not being able to get a job. It depends on your skill set. There large emphasis on python code, but you also get to code on R and C++.
Overall, it's a good course, but if you get the same subject at LSE or Imperial or UCL, choose that.

The content of this course is high-quality and has a standard similar to that of other big-name universities in the UK, which charge much higher tuition fees. Many of the lecturers are excellent, however there are a small number whose optional courses are avoided with good reason. The application process is simple and rarely requires an interview. The quality of students is varied, with many performing exceptionally well throughout the course and going on to study PhDs or acquire excellent graduate jobs, and others who struggle in many modules. There are career services provided, and the University has an industry partner in Santander UK, with whom a small number of students may be selected to complete their dissertation project in the summer. Overall I would recommend this course especially due to its value for money for home students and the content covered, but it may lack the industry prestige of the top UK universities.