Is the Cambridge Master of Finance a course for quants? In short, no, but it’s worth commenting a bit more on this.
Finance has become a lot more quantitative over the years so that the entry level requirements for some knowledge of maths and statistics have increased. But banks still hire people with arts and humanities degrees and the ultimate resource needed is a high level of intelligence. This should not be confused with an aptitude for maths, though these are definitely sets which intersect.
True “quants” are people who do jobs in finance which require a lot of maths and/or statistics and, typically, a lot of programming. Examples would include: pricing of exotic options; detailed risk modelling; and analysis of quantitative trading strategies. The people who are hired for the truly quant jobs typically have PhDs in maths or natural science, not economics or finance (unless they have a prior qualification in say maths or physics). One reason for this, which is well brought in Emmanuel Derman’s excellent book “My Life as a Quant” (well worth reading for aspiring quants and non-quants alike) is that science students do a lot of programming, and this gives them the right sort of background to build models that actually work and that traders can use. Unfortunately the emphasis in academic economics and finance is rather more on elegance and rigour than on usability.
The next level down is the hiring of people with a masters in financial engineering, which means they have trained in finance from a very quantitative point of view. Typically coming again from a natural science or maths background but also from economics, these are people who can do modelling and communicate with modellers but are not quite as advanced in their maths as the PhDs. But they may have other skills.
The emphasis on a financial engineering degree will be narrow but deep and leaves little time to study the underlying theory of finance/economics or the institutional part of the business. While learning on the job can be a partial substitute for this knowledge, it is inevitably a different experience from doing a general finance masters like the Cambridge MFin.
To do a degree like the MFin you need a decent grasp of calculus and statistics but unless you want to take some of the more quantitative courses that is sufficient.
One dividing line between quant and non-quant is stochastic calculus, a branch of maths that is used to model processes involving random variables such as the behaviour of stock prices. Stochastic calculus, through stochastic control theory (which is used to model the trajectory of an intercontinental ballistic missile, hence “rocket scientist”), has now become a mainstream topic in graduate economics courses but most people without a maths or physics first degree will probably not have learned it. It first started appearing in economics in the 1960s, mainly through the work of the late, legendary Paul Samuelson. His student, Robert Merton, used it to provide firm theoretical underpinnings to the Black-Scholes formula (which is why it’s usually known in the academic world as Black-Scholes-Merton).
But even most graduate economists acquire only a working knowledge of the key results of stochastic calculus and are not really trained thoroughly enough in it to be quants, except by doing further specialised study. The barriers to entry here are formidable. If you haven’t already got a knowledge of stochastic calculus it’s a major investment to learn it to the level needed to be a real quant.
An alternative type of quant is one who focuses more on the statistical side, meaning the measurement of data and search for patterns in it. This is the domain of econometrics, which requires a good knowledge of statistical theory but not stochastic calculus. Some hedge funds employ people with this sort of knowledge to find profitable trading rules. The MFin forces everyone to acquire a good working knowledge of econometrics, mainly around time series analysis GARCH and VAR – vector autoregression NOT value at risk, Kalman filtering), and there is scope to do advanced work for those who really can cope with it (meaning the modelling of jump processes for example).
But the majority of jobs in finance do not require advanced quantitative skills. What is definitely valuable is being able to talk to quants and “translate” what they say into a language that the definitely-not-quants can understand. This is a skill that Master of Finance graduates should have acquired and reflects the market research we did ahead of launching the programme, namely the ability to combine a good level of technical understanding with an appreciation of the business context in which the models are actually used.
There will always be a role for the excellent sales person or gifted trader who has minimal quantitative knowledge but it is much better to have a good grasp of the underlying principles of finance and the statistical concepts through which they are expressed. Anyone so equipped should have a longer and richer career potential.