Transforming the future of financial services

Interest in financial technology is exploding around the world. The societal demand for timely, impactful Fintech research output and graduates to serve the evolving financial services industry is growing. The dynamic world of finance requires highly skilled personnel with strengths in:

  • Applications of machine learning, predictive analytics in finance
  • Modern practical optimization techniques for asset allocation, portfolio management, and rebalancing
  • Efficient computational techniques for evaluating and hedging complex portfolios of financial instruments. Includes new methods to efficiently compute first and second order sensitivities
  • Techniques for high-frequency trading including transaction cost analysis and single asset and portfolio strategies
  • Emerging technologies (e.g., blockchain)
  1. Jan. 28, 2020University of Waterloo Data Finance Certificate Program

    University of Waterloo Data Finance Certificate Program is offered at the Manhattan Institute of Management (New York City)

    Data finance is revolutionizing the practice of computational finance. New and efficient data driven methods are being employed to address many of the fundamental problems arising in the finance industry. This new approach is ‘data driven’ and rests on recent exciting developments in the areas of machine learning, artificial intelligence, and applied optimization.

  2. July 24, 2018FARM program awarded PRMIA accreditation

    The Faculty of Mathematics is pleased to announce that PRMIA has just granted the University of Waterloo’s Financial Analysis and Risk Management (FARM) Program (Risk Management Specialization) University Risk Accreditation Program status.

  3. May 16, 2018Students win $25,000 at the Waterloo Data Open

    Over 100 undergraduate and graduate students gathered in Mathematics 3 early Saturday morning to tackle large datasets at The Data Open, a competition that brings together the best minds in mathematics, engineering, science and technology to collaborate and compete using the world’s most important data sets. Students received the data sets at 8:00 a.m. and, in teams of three to four, had until 3:30 p.m. to analyze the data, extract meaningful insights, and propose solutions to a socially impactful problem.

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