Master in Quantitative Finance | metrics table at end of article
Mindful of the broad interests of its diverse body of students, WU’s two-year master’s in quantitative finance offers the opportunity to choose between a science track and an industry track in the second year.
Students opting for the science track are offered more of an academic flavour, learning research such as how to read and write academic papers, and participating in seminars and a supervised research project run by the faculty.
The industry track meanwhile focuses on practical problems, incorporating a research project known as the industry lab. Students meet with an industry partner to define their project, and the methodology needed to solve a given problem. When it is complete, the students give a presentation of their results to the programme director and a senior manager from the company.
In addition to these projects, 30% of students will typically take either an internship lasting three months or work part-time in their second year. Students are also offered elective courses allowing them to specialise in, for example, asset management or risk management.
Before specialising, however, all students share the same core modules. As well as a dedicated course in the first semester, computing skills are repeatedly applied in classes throughout the programme. With regards to programming, the focus is on R, an open-source language used for data analysis.
“A lot of focus in our programme is on computing and data analysis and we have professors who are strongly involved in the international development community of R,” says Rainer Jankowitsch, the co-director of the programme. “It is a very important data analysis software used in many corporations, banks, regulators, asset management companies and so on. We make sure students have a very deep knowledge of the software allowing them to deal, for example, with big data or to implement various kinds of financial models.”
Of roughly 300 applicants last year, the university accepted 80 students, with a natural attrition rate bringing the final number to 50 – below its target cap of 60. “We have quite a high percentage of international students [72%] but our goal is to steadily increase this,” says Georg Mikula, the programme manager. There are, however, no plans to increase the total number of students. Tuition is free for students from the European Economic Area and €727 ($813) per semester for other students.
Three times per semester, the QFin club invites an industry practitioner to discuss their firm and their role in it, as well as possible job opportunities. It also forms a network for students to liaise with alumni, helping them to find jobs.
Students with a broad variety of backgrounds in business, economics and mathematics are brought together in this programme and encouraged to work as a team. The students regularly form learning groups where mathematicians help non-mathematicians and business economists help non-business economists. Roughly two-thirds of students have business or economics backgrounds while mathematicians and statisticians compose the other third.
Jankowitsch says: “This is a real strength of the programme because if the students help each other and have different backgrounds they learn a lot more than if they had the same background and the same skills.”
Guadalupe Rosas, who graduated from the course in 2011, is now head of asset-based risk management at Raiffeisen Bank International, where she leads a team of risk managers. Rosas previously trained as an industrial engineer. Her first job on graduating was as a quant analyst at the same bank.
“I was looking for a programme with a strong quantitative and financial curriculum which could form a bridge between my two studies and support me to pursue an international career in finance,” says Rosas.
The choice between the industry and science tracks was particularly appealing to Rosas, while the professors were well connected enough to provide students with job opportunities in the industry, she says. Exposure to programming in R was also important, she adds, and, while it is not used universally, learning one language has allowed her to learn others more quickly.
“We see that more and more banks are still going for these open-source languages like R. In my personal experience once you learn one specific language it’s very easy to catch up with others,” she says.
Demand for quantitative financial skills in Austria is high, she adds. Raiffeisen and other banks supporting the university’s quantitative finance programme are sending representatives like Rosas to various universities persuading students to join the programme.
“There is a need for more and more people with these quantitative skills. I do see very high hiring rates for people who are graduating from such programmes, especially here in Austria,” says Rosas.