University of New Brunswick

What you'll study

The programme emphasises core principles of asset pricing and portfolio construction together with applied quantitative techniques for analysing financial markets. Teaching is delivered through a mix of lectures, hands-on computing labs and a capstone project or practicum.

• Core quantitative finance: portfolio theory, modern asset pricing, fixed income and derivatives.
• Econometrics and time series: financial econometrics, volatility modelling, cointegration and forecasting methods used in asset returns analysis.
• Computational methods and data science: programming for finance (typically Python and R), numerical optimisation, Monte Carlo simulation and machine learning applications for prediction and model selection.
• Risk management and regulation: market and credit risk measurement, stress testing, scenario analysis and the regulatory context for institutional investors.
• Applied electives and special topics: algorithmic trading, factor investing, alternative data, ESG integration and high-frequency data analysis.
• Capstone or practicum: a substantial applied project working with real market data or an industry partner to develop and validate an investment strategy or risk model.

Structure

The programme is typically delivered as a full-time master’s with a taught-course component and a culminating capstone project; part-time study may be available. Students complete a set number of credits across core courses, electives and the practicum or thesis, with a strong emphasis on lab-based, reproducible analysis.

Entry requirements

Applicants should hold a relevant bachelor’s degree with substantial quantitative content, such as mathematics, statistics, engineering, physics, economics or finance with strong quantitative coursework. Typical elements of a competitive application include:

• Undergraduate degree: a recognised bachelor’s degree with solid grades; quantitative coursework in calculus, linear algebra, probability/statistics and at least one programming course is expected.
• Transcripts and references: academic transcripts and two or three references, preferably including at least one academic referee who can speak to quantitative ability.
• Statement of purpose: a clear statement describing academic background, career objectives and reasons for pursuing quantitative investment studies.
• English language proficiency: for applicants whose first language is not English, an approved English language test is required (or equivalent evidence of prior studies in English).
• Additional materials: some applicants may submit a CV/resume and samples of quantitative work or coding portfolios. Standardised tests (e.g. GRE/GMAT) are considered where requested but requirements vary by intake.

Career prospects

Graduates move into quantitative roles across the financial services industry and beyond. Common career destinations include:

• Quantitative analyst or researcher in asset management and hedge funds
• Portfolio manager or assistant portfolio manager implementing systematic strategies
• Algo trader and execution strategist at trading firms or broker-dealers
• Risk management roles focused on market, credit or model risk
• Data scientist or machine learning engineer in fintech and financial data providers
• Consulting and regulatory roles involving model validation, stress testing and financial stability analysis

The programme’s hands-on capstone and computing training help graduates demonstrate practical skills sought by employers, including coding, data handling, backtesting and report writing.

Why study at University of New Brunswick

The University of New Brunswick provides a focused learning environment with close faculty supervision and access to computing resources for quantitative research. The university’s strengths in applied economics, business and computational methods support an investment-management curriculum that balances theory and practice.

• Small cohorts and close mentorship: smaller class sizes allow personalised feedback and strong faculty-student interaction.
• Practical, industry-relevant training: emphasis on programming, empirical methods and a capstone connecting students to real-world datasets or industry partners.
• Regional and national connections: links with financial firms, pension funds and fintech companies provide networking and internship opportunities.
• Support services: career services, computing labs and workshops help students prepare CVs, interview skills and technical presentations for the finance job market.