
Quant Guide 2021: North Carolina State University
Raleigh, North Carolina, US

North Carolina State University’s Financial Mathematics master’s programme continues to punch above its weight in the latest iteration of Risk.net’s Quant Guide, ranking at 13th place, ahead of many more storied institutions – a product of its excellent student:staff ratio and powerhouse research score.
It has, like many of its peers, experienced coronavirus-related setbacks this year. A number of international students weren’t able to enrol because of travel restrictions, says programme director and mathematics professor Tao Pang, and some chose to defer their places.
However, it has still managed to grow: 58 offers were extended to 273 applicants, and 44 of those offers were accepted. This gives the programme an impressive offer-holder acceptance rate of over 75%. Nevertheless, just 17 offer holders ended up enrolling as full-time students. Pang calls this blow to what could have been a bumper intake the “biggest impact” the virus has had on the programme thus far.
“In-person events, both formal and informal, are important in helping students acclimate, share knowledge and form a cohesive identity as a cohort,” adds Pang. “We’ve had to be creative to foster these outcomes in other ways.”
He says that the programme has moved a number of events online, including roundtables, social events and alumni ‘visits’. Cannily, the programme has also moved to provide additional training in remote interviewing and in giving virtual presentations. Such skills will come in handy over the next few months, says Pang, while a majority of white-collar work still takes place from home.
A new course that has been incorporated focuses on investment in financial markets, with material on: stock, bonds and derivatives valuation; investment strategy; and portfolio performance evaluation. A new instructor, hired this autumn, is assistant professor Andrew Papanicolaou, formerly of New York University’s Tandon School of Engineering. Papanicolaou specialises in machine learning and data science with applications in finance, Pang tells Risk.net.
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