Master of Financial Mathematics | metrics table at end of article
Students who complete the two-year master’s in financial mathematics at Monash University, based in Melbourne, have a familiar career decision to make: buy side, sell side, or beyond. For Gregoire Loeper, academic director of the programme, aspiring quants would be well advised to consider a natural advantage that Australia offers: its $3 trillion government-mandated pension savings system, one of the world’s largest. The country’s superannuation scheme supports a well-established asset management industry – and it’s here that the most fruitful job opportunities lie, Loeper believes.
While Monash’s two-year programme isn’t orientated specifically towards buy-side training, it aims to give students a strong grounding in the relevant areas.
“It’s a different range of skills to the sell side,” says Loeper. “[The buy side] involves much more statistical training, forecasting, data handling, risk management and that sort of thing – especially in Australia.”
“The investment banking industry already has strong quant teams,” he adds. “The buy-side industry is much less developed. I think [the buy side] is where we’ll see the best job opportunities.”
The course is split into three phases: the first, an orientation studies component, which offers a set of introductory classes including financial mathematics, econometrics and applied mathematical modelling. Students who are already familiar with these areas are able to skip directly to the second semester.
In the second phase encompassing semesters two and three, students tackle advanced courses in interest rate modelling and computational methods for finance and then specialise through a series of optional modules featuring material on partial differential equations, Markov chains and statistical learning. The “hardcore financial maths” begins here, says Loeper.
The third phase – during the full fourth semester – is devoted wholly to industrial placements with a variety of financial companies. Some students undertake internships in Melbourne; others go abroad, particularly to China, says Loeper.
Loeper contrasts Monash’s relatively new programme with older and better-established courses, which, he says, are often preoccupied with derivatives pricing.
“We try to prioritise quantitative investment strategy knowledge rather than focusing on pure derivatives pricing,” he explains.
It’s a familiar area for Loeper, who spent nine years at BNP Paribas in hybrids quantitative research and structured products pricing, before switching to academia. He has led the master’s programme at Monash for four years. But he has leveraged his sell-side connections by helping set up a research body at Monash, the Centre for Quantitative Finance and Investment Strategies, which is partnered with BNP Paribas. The link-up is part of the programme’s wider aim to achieve an appropriate balance between theoretical and practical content, says Loeper.
“We’re building a strong research institute for data analytics,” he says. “We want students to work as much as possible with actual data, not only with models. We’re exposing them to modern post-GFC issues – they need to grasp the complexities of the new financial world.”
Loeper characterises himself as “old-school”, but is clear on where he sees finance heading in the next few years. It’s an area that will require a solid grasp of both old- and new-school techniques.
“ESG investment is going to be absolutely fundamental in the next year,” says Loeper, referring to funds that focus on environmental, social and governance factors. “It means being able to gather a lot of data, company by company, screening companies and constructing investment portfolios based on that. And understanding the role that finance has to play in climate-related economic evolution is something we as academics need to teach our students. That’s not really happening anywhere.”
While he’s confident in the job prospects of graduates from programmes such as Monash’s, Loeper sees the quant finance qualifications themselves as diminishing in popularity.
“What you see now is people going less into financial maths masters, and directly into pure data science programmes. It’s a big trend, definitely,” he says. “A lot of the candidates that would have joined maybe five years ago now prefer to go into data science and work for different companies – Amazon, Facebook, etc.”
“That means the competition for good students is becoming even more difficult,” he adds.
As multiple programme directors interviewed for the 2019 guide have pointed out, the tech sector is a source of stiff competition when it comes to the recruitment of quant grads. And the same is true in Australia, however many thousands of miles it is to Silicon Valley.