SMU - MS in Quantitative Finance

SMU MQF program covers a wide range of topics essential to being a quant, from Stochastic Calculus to Derivatives to Portfolio Management. Many of the courses involves some assignments and/or projects in Python, so even if you are a complete beginner, you will have plenty of chances to practice your Python and work on projects that you can put on your Github.

The professors are good, with many of them currently still working at various hedge funds and banks during the day, and coming to teach during the night/weekends.

That being said, how much you take away from this course is a direct function of how much effort you put in. If you barely attend classes and simply leech off your course mates for projects and assignments, rest assured you will still get that piece of paper with "Master of Science in Quantitative Finance" and your name on it in return for the SGD60k you paid. As mentioned, the lecturers are full-time working professionals themselves, there is no incentive for them to fail students as that usually involves more paperwork on their part.

As suggested by the paragraph above, student quality is a mixed bag. You will find enthusiastic people trying their best to learn something useful and get a job, and you will find people treating the course as a 1 year holiday. Which group you want to be in is up to you.

On job prospects, admittedly most students are unlikely to work in pure quant related roles. Practically speaking, SMU/NTU/NUS probably churn out over 500 MQF/MFE students a year combined and there is nowhere near that many quant jobs in Singapore. A lot will end up in asset management, risk or discretionary trading which a normal Masters in Finance or Bachelors in Business student can do. That being said, having a mix of finance and coding skills will help you stand out in this difficult job market and if you come from a non-finance or non-coding background, this program will help you learn a lot.

As for career service support, they do help with resume preparation and basic interview preparation but please manage your expectations, they can't be expected to give you an interview for Jane Street or Goldman Sachs... Anyway, if you are hoping for a quant role, you should be practicing leetcode/hackerrank on your own, as well as practicing the interview questions from "Heard on the Street" and the other interview prep books. The alumni network of the MQF program is strong, and the school is within walking distance of the CBD, so some alumni do turn up from time to time for events, and you can learn more about the various kinds of jobs available out there.

I joined with an impression that it will be mostly machine learning and alpha research. I am partially correct, because while both are taught, I also get to study stochastic calculus and derivative pricing as well as risk management.

The program focuses on bringing the more well-used side of these theory into python code. While I did not get to study the more esoteric equations of stochastic calculus, we get to implement the modelling of volatility surface using tick data of deribit.

The students are a hit or a miss. This is because at a base level, those that can make it in a Quant field is already low. One of the first task you will want to do is to make sure you are a reliable group mate, and the task after is to find other reliable group mate.

I also heard that the cohorts after mine benefits from getting a credit bearing internship too.

By itself, it is unlikely to be enough to push most of the graduates into a Quant role. Graduates will have to make sure they stand out amongst the other target school. It gave me an edge to be recognised for a Quant Developer role, and the risk and stochastic modelling lecture proves to be useful when my company implements option on their exchange. However, it also took my existing strength as a com sci graduate and work experience to land the job.

All in all, those that are willing to put in real effort, and already have what it takes, will benefit from this program. It could be the final push/foot in the door for Singapore job hunting (although the job market is very bad at the time of writing). Those looking to take this just for the degree, for connection, or without knowing what they want from the program, may only leave with a lighter bank account.

SMU MQF has a broad coverage of skills and knowledges needed in the quantitative finance field. Modules are useful, interesting and provide a good foundation for any master students.

However, if you are thinking of using SMU MQF to spring board into Singapore as a quant related analyst, then you might be in for a disappointment. The numbers of quant roles in Singapore are limited and without any experience and relationship, it is almost impossible to secure a role.

Having said that, if you are pursuing the degree for extra knowledge, I highly encourage you to consider SMU MQF as it is a good course and I highly recommend it.

SMU MQF has just graduated its first cohort this August. While there were some administrative hiccups, as one might expect in a new program, the combined faculty from SMU and Cass Business School had provided a solid education for many students.

Personally, I made a successful transition from a qualitative background into an quantitative role at an oil major. All in all, it was time and money well spent over the past one year.