Quantitative Finance/Fintech Track

Finance is built on fintech.

Explore the impact of innovative and disruptive technologies on the financial sector. Financial institutions need professionals with deep quant and tech skills.

  • Develop financial acumen and complement it with courses in R and Python
  • Learn to apply artificial intelligence and machine learning to quantitative financial analysis

Contact Us

Graduate Programs Office
School of Management
University at Buffalo
203 Alfiero Center
Buffalo, NY 14260-4010

Tel:  716-645-3204
Fax: 716-645-2341
som-apps@buffalo.edu
Meet our Team

  • Understand how the principles of big data and analytics relate to the fundamentals of investment decision-making
  • Apply mathematical methods and computer simulations to currency markets, hedge funds, investment banking and derivatives
  • Study quantitative models in finance and obtain the knowledge to implement them on multiple computing platforms and across large datasets
  • Learn to use complex financial instruments, construct portfolios for a range of investment goals, and gain powerful quantitative skills

The MS Finance is a STEM (science, technology, engineering and mathematics) curriculum with 36 credits typically completed in three semesters. Some students extend to a fourth semester, and UB undergraduate students may be able to complete the program in two semesters. All majors are welcome; however, business, math, economics and engineering majors are ideally suited to the program, provided you have the requisite calculus background.

Curriculum

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Fall Start

MGF 696LEC Portfolio Theory and Strategy

The course covers sophisticated approaches to investing and it has an introduction and three main parts. The introduction covers measures of performance and risk and methods to calculate them in closed form or from historical data. The first part of the course covers investment strategies across several asset classes, from traditional ones such as value or growth investing to strategies employed by hedge funds, such as arbitrage, option trading and other quant strategies. The second part of the course addresses portfolio construction, from assessing a utility function to the investor to constructing an optimal portfolio maximizing that utility. Investor types covered are individuals saving for retirement, speculators, university endowments or foundations, or pension funds. Finally, the last part of the course addresses the topic of risk measurement and management, with an emphasis of risks faced by decentralized organizations, such as funds of funds, foundations, or pension funds.

Credits: 3.00
Semesters offered: Fall 2019
Co-Requisite: MGF 633.


Select 1:

MGF 644LAB Supervised Research - Fixed Income

Students will learn to conduct empirical research on fixed-income securities. The initial learning process involves a limited number of lectures on fixed-income pricing theory, followed by formal training on how to access data from fixed-income database(s). Using the above resources students will also build an investment portfolio for a given risk level. Students will then develop testable hypotheses that employ mathematical modeling, and develop and apply statistical analyses to data to test the hypotheses. Finally, students will develop and present a professional research report based on the empirical findings.

Credits: 3.00
Semesters offered: Fall 2019


MGF 645LAB Supervised Research - Equities

Students will learn to conduct empirical research on equities. The initial learning process involves a limited number of lectures on asset-pricing pricing theory, followed by formal training on how to access data from equity-related database(s). Students with then develop testable hypotheses that employ mathematical modeling, and develop and apply statistical analyses to data to test the hypotheses. Finally, students will develop and present a professional research report based on the empirical findings.

Credits: 3.00
Semesters offered: Fall 2019 | Spring 2020


+ 2 Electives

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