Quantitative Track

Cultivate advanced fintech skills in the Quantitative Finance Track

Technology and innovation are dramatically transforming the financial industry. Our Full-Time MS in Finance/MBA delivers deep financial expertise along with the advanced mathematical and technical skills that employers seek.

In the Quantitative track, you'll develop advanced mathematic and technological skills. Your curriculum integrates finance with other disciplines such as economics, mathematics, and computer science. You'll also explore the advanced communication skills necessary to lead teams, address complex financial challenges, and collaborate with business leaders in a global context.

Apply your in-class learning to D'Amore-McKim's signature experiential learning opportunity: a 3-, 6-, or up to 12-month corporate residency.


To earn an MS in Finance/MBA with a track in Quantitative Finance, you must complete the MS in Finance/MBA core curriculum (16 credits) and 18 additional credits of Quantitative Finance Track coursework. Your remaining 33 credits will be your concentration in Corporate Finance or Investments plus your electives.

Quantitative Finance Track Required Courses

Familiarizes students with domestic and international financial markets and the securities traded therein. Discusses a variety of techniques for valuation of financial assets and relies heavily on quantitative methods. Critically analyzes such qualitative concepts as market efficiency, intrinsic value, and risk. The contents of this course, descriptive, theoretical, and applied, should provide students with the ability to build unique valuation models to suit the particular investment alternative they wish to scrutinize. Also provides students with an understanding of how investment theory and investment practice relate.

FINA 6203 | 3 Hours

Introduces the essential fundamental mathematics needed for the study of modern finance: probability, stochastic processes, statistics, and regression analysis. Also focuses on theory and empirical evidence useful for investment decisions. Topics include financial risk factors, financial models, financial markets and equilibrium models of security prices, market efficiency, and the empirical behavior of security prices.

FINA 6332 | 3 Hours

Introduces Python and its use as a financial data analytics tool. Python has become one of the most widely used open-source, cross-platform programming languages. Covers the basics of programming in Python and key libraries (NumPy, Pandas, Matplotlib, etc.) used in data analytics, then focuses on implementing various financial models in Python. Topics include single and multifactor portfolio models, portfolio theory and the efficient frontier, algorithmic trading, options and futures, and value at risk.

FINA 6333 | 3 Hours

Examines statistical methods used to analyze financial data and test financial theories. Offers students an opportunity to learn how to access various sources of financial data, design empirical tests, and apply basic programming skills to analyze the data and arrive at conclusions. Specific topics include regression analysis, time-series analysis, event study methodology, panel data analysis, and limited dependent variable models.

FINA 6334 | 3 Hours

Introduces derivative assets, financial engineering, and risk management. Explores specific hedging use of options, forwards, and futures. Focuses on the determinants of forwards, futures, options and swaps, and various exotic derivatives pricing using computer-based numerical methods in a Monte Carlo setting and in closed form using elements of stochastic calculus. Also explores risk-management strategies using positions in derivative securities, static hedging, and dynamic hedging in continuous time.

FINA 6335 | 3 Hours

Concentration Options

Complete one of the following concentrations:

The following is a sample curriculum and is subject to change. Enrolled students should reference the academic catalog for current program requirements.