Breaking into quantitative finance has become much harder in recent years, as the supply of candidates from academia, engineering and software development wishing to apply their skills in a more lucrative, fast-paced setting has exploded.
However, most of these people have no experience in finance and struggle when applying for jobs in the sector, particularly the jobs in investment banking. For those who have yet to enter the job market, the array of roles in quantitative finance can also seem daunting, which makes preparation during studies a tricky proposition.
There are generally three roles of interest in the quant finance community. They are the quant trader, financial engineer (quant analyst) and a quant developer (quant dev). There are also more recent roles in risk management that make strong usage of quantitative techniques. It is often difficult to become a quant trader straight out of university as the skills necessary take a significant amount of time to develop. Financial engineering (i.e. derivatives pricing) was extremely popular prior to the 2007-2008 crisis, and there is still some demand from investment banks. Quantitative developers are still in very much in demand, though, particularly for individuals well-versed in C++, C# or Java.
One path into quant finance, particularly in the investment banking sector, is via a Masters in Financial Engineering (MFE). Most courses require a solid grounding in undergraduate mathematical abilities, usually gained in a maths, physics or engineering context. The courses provide education in stochastic calculus for derivatives pricing, portfolio/risk management and some software development.
MFEs also provide a good springboard for those wanting to make a career transition. The courses are primarily designed for roles in investment banks as they do not often emphasise algorithmic trading, extensive research skills or programming ability. Some teach C++ or MatLab, but not often to the standard required by banks. The courses also come with a price tag in the region of $50,000, so this also has to be weighed up against future job prospects.
While quant hedge funds still want to employ quant developers to build trading infrastructure and manage data, the skills required for quant trading are not often included on MFE courses. In particular, quant funds tend to pick the ‘best and brightest’ for their quant traders, many of which will have a PhD or similar research degree.
Signals analysis, statistical/machine learning and a deep understanding of computational hardware (for high-frequency firms) are all skills sought by the best funds. These skills can be gained while working through a top-tier mathematically-based undergraduate programme that leads onto further academic research positions.
Note that quant funds are always searching for something new, as this is how they make their money. Therefore, they are not interested in seeing the same old tricks and you will need to stand out or bring something fresh to the table.
Competition is extremely fierce for the top positions, so it is absolutely essential to set yourself apart from the pack. There are a few ways of achieving this. If your heart is set on quantitative trading then developing your own trading algorithms will give you plenty to discuss in interview. Having a prior track record of making money in quant trading always helps too. If you are interested in a financial engineering position then implementing pricing algorithms currently discussed in the literature will help in interviews. For the software developers, contributing to or creating open-source quant libraries is favourably looked upon. Building your own infrastructure for trading/pricing will also allow you to discuss your expertise in software development methodologies.
Michael Halls-Moore is a former quant developer and founder of QuantStart, a portal that advises MFE, PhDs and career-changers on how to break into investment banks or hedge funds. He is author of C++ for Quantitative Finance