Quant Guide 2020: programmes tap banks for teaching talent

Universities are adding machine learning and data science courses, but need instructors to teach them

Running a quantitative finance master’s programme is hard work. Adapting courses to meet the rapidly changing roles quants play in the banking industry is challenge enough for quant academia. Finding the right experts to teach newer skills can be harder still.

In recent years, the biggest structural change most programmes have faced is the migration of machine learning and data science topics from optional modules to a core part of the curriculum, with such skills now considered foundational by employers.

With many programmes taught over 12 months, adding more core courses into an already packed schedule is a headache. But where employers’ changing expectations present challenges for programmes to meet, they also present opportunities – namely, nabbing senior market executives back from the financial industry to serve as instructors.

As Risk.net’s guide to the leading quant finance master’s programmes shows, the job market for adjunct professors is hot. Cornell University has hired Marcos López de Prado, one of the leading lights of quant investing, to teach on its quant master’s programme. New York University’s programme at the Courant Institute has added Ken Winston, a former chief risk officer at Morgan Stanley. Princeton’s top-ranked master’s has a course on asset pricing under asymmetric information taught by visiting professor Michael Junho Lee of the Federal Reserve Bank of New York. And Boston University’s programme has hired Sheldon Chan, a director at BlackRock, to teach a module on advanced programming for finance.

“The industry is changing rapidly, and students now need to know a bit less from the fundamentals of stochastic calculus and things that people were doing 10 years ago,” says Antoine Jacquier, the director of the MSc in Mathematics and Finance at Imperial College London. “They need a wider range of knowledge.”

Like many programmes, Imperial’s MSc is taught over 12 months. The programme has made room for new courses by cutting the length of all elective modules in half, with classes now running for 15 taught hours each, instead of 30. This truncation has allowed MSc to make more room for the topics students and employers want to see, says Jacquier – deep learning, market microstructure and Python programming are among its new courses this year.

But, crucially, it has also made the prospect of teaching on such modules more attractive for industry practitioners, he adds: it’s easier to commit to teaching 15 hours than 30. As a case in point, the programme was able to secure the services of Vladimir Lucic, head of volatility quantitative investment strategies at Macquarie Group, as a visiting professor this year

Other programmes – especially multi-departmental master’s, which can lean on professors from other faculties – like to trumpet their advantages here. Richard Bryant, director of Carnegie Mellon University’s Master of Science in Computational Finance, claims his programme has less need for industry practitioners, having the luxury of being hosted by four departments, rather than one. Given the expertise on hand in the departments of data science and statistics, information systems, mathematics and business, the programme has a lot of bases covered, he argues.

Carnegie Mellon’s Richard Bryant sees the role of industry practitioners as complementing tenured faculty, providing “street smarts – unlike some of our competitors, who have to depend on adjuncts for entire disciplines”

Bryant sees the role of industry practitioners as complementing material taught by tenured faculty, to provide “street smarts – unlike some of our competitors, who have to depend on adjuncts for entire disciplines”.

Adjuncts – who often teach classes during evenings after they’re done with their day job – are, by definition, less easy to rely on compared with permanent academic staff.

A director of one programme notes that a star lecturer recently left his post as a managing director at one bank to join a hedge fund, adding that he may now lack the time or the permission of his new employer to continue his teaching post.

“An admitted disadvantage is that things come up. It’s unclear whether he’ll continue,” says the programme director. “That kind of thing happens.”

Other multi-departmental programmes in this year’s quant guide include Columbia University’s MAFN at 15th place – co-sponsored by the departments of mathematics and statistics – and EPFL’s Master in Financial Engineering at 21st, a behemoth course that leverages no fewer than six university departments.

For other programmes, tenure is overrated. New York University’s Tandon School of Engineering, which ranks fifth in this year’s guide, has a high proportion of practitioner staff: for 88% of its instructors, academia is a secondary occupation.

One recent addition is Sandeep Jain, who joined in January this year. Prior to his new role, Jain – an alumnus of the Tandon programme – spent 16 years at UBS, and now teaches a compulsory module on machine learning in finance.

Editing by Tom Osborn