A team from the Faculty of Math has placed second worldwide, and first from Canada, in the Bloomberg Global Trading Challenge. In the challenge, teams of 3-5 students led by a faculty advisor compete in an investing challenge, using the provided Bloomberg Terminal to “define market assumptions, develop a return-generating strategy, and execute trades within a closed network.”
A total of 13,029 people in 2,520 teams from around the world participated, including 22 teams from the University of Waterloo. The winning team, GWNB, consisted of team lead Wei Gao (Math/Business Administration), Ruohan Jin (BCS), Wenqi Yu (Actuarial Science), Yidan Wang (BCS), and Kaiwen Xu (Math-CPA ), assisted by faculty advisor Olga Kanj (assistant professor, teaching stream in Statistics and Actuarial Science).
“I am incredibly proud of our university’s winning team in the Bloomberg Trading Competition!” says Kanj. “As a finance professor, it is truly rewarding to see our students excel in applying their trading skills and classroom knowledge to achieve such a remarkable accomplishment.”
Wei Gao with the Bloomberg Terminal the team used throughout the challenge
This is Gao’s second year participating in the competition – last year, as a first-year student, his team placed tenth. As team leader this year, he simultaneously set the strategy for the team’s trading while also mentoring his team mates – none of whom had competed before. The team also had to balance trading with attending classes, since the competition took place in real time over more than a month, from October 7 to November 15.
“Ranking 2nd globally was incredibly encouraging, but it also served as a reminder that there’s always more to learn and refine,” Gao says. “In the modern financial markets, the integration of advanced technology has elevated the importance of mathematical and computer science expertise. The development of most trading strategies now hinges on quantitative algorithms powered by applications like machine learning, neural networks, and the discovery and evaluation of quantitative factors. These processes demand not only a deep understanding of mathematics but also the technical ability to translate models into efficient, executable trading code.”