Quantitative Finance


Duke Math Department offers courses for both undergraduate and graduate students across Duke Community to prepare them for career and academic research in the field of quantitative finance and actuarial science. Our mission is to equip students with a solid foundation in mathematical analysis and modeling combined with programing languages like Python to solve practical problems in finance. Quantitative Finance is a field of study of using quantitative methods to solve problems in Finance such as those in Trading, Investment, Banking and Insurance. This is truly an interdisciplinary subject that ultimately involves Mathematics, Statistics, Computer Science, Economics and Finance. One example is Algorithmic Trading, in which students learn how to develop trading and investment algorithms that automatically interact with financial markets to execute pre-defined investment objectives. Another closely related field is Actuarial Science, which is the study of mathematical and statistical analysis to the insurance business.  For example, our course on Insurance gives a good introduction to Life Insurance business and related mathematics.

We have strong faculty support for the courses in quantitative finance and actuarial science. In addition to many full-time Duke Math faculty, we also have Executive in Residences (link to a page on executive residence or a page of teaching faculty in quantitative finance and actuarial science) for Quantitative Finance, who brings to students an extensive practice experiences in financial services industry such as trading, banking and insurance sectors.

Our approach to education in quantitative finance are based on these three pillars:

Academic Courses

We offer wide range of foundational courses to equip students with solid quantitative skills from the introductory courses in probability and statistics as well as advanced courses in stochastic processes. We also offer course in Python and Machine Learning to develop skills in practical implementation of financial models.  Currently we have four core courses in quantitative finance and actuarial science.

MATH 581Mathematical FinanceQS
MATH 582Financial Derivatives 
MATH 585Introduction to Algorithmic Trading – Financial Data and ModelingQS
MATH 590-02Advanced Special Topics in Mathematics 

Industry Speakers

In certain courses such as those in Algorithmic Trading, as well as Insurance Business, we have regular industry speakers give lectures on topics relevant to the class and students’ interests. Many students find these lectures very informative as they bring fresh perspectives to the concepts and theories that they learn in the class. In addition, industry speakers also speak about their work experiences which many students find helpful to their career plan.

Industry speakers are important part of the quantitative finance courses given the dynamic nature of the Finance Industry. These industry speakers share three latest development in their perspective fields as well as providing value career and practical advice to Duke students. Here are select recent industry speakers:

“Low Volatility Anomoly and Covid Factors” — Nick Alonso

Mr. Alonso is a Director within the Multi Asset Investments team of Panagora Asset Management, a renowned quantitative investment firm. He is responsible for quantitative model research, development and enhancements for PanAgora’s Multi Asset strategies. He is also responsible for the development and management of the firm’s Defensive Equity strategies, including alternative-beta and factor-based strategies. Mr. Alonso joined PanAgora from Mellon Capital Management (formerly Franklin Portfolio) where he was a Quantitative Analyst primarily responsible for research and management of Market Neutral Equity portfolios. Mr. Alonso is a CFA charter holder, and obtained his MBA from University of Chicago.

Quantamental Investing Using Alternative Data” — Nathan Edwards

Mr. Edwards currently works at Durable Capital Partners as a data analyst. Prior to this role he was a data analyst at Suvretta Capital Management, a multi-billion dollar equity long/short investment firm. He has worked in the finance industry for seven years analyzing “alternative data” to support fundamental investing. He began his career working for the Department of Defense and Intelligence Community as a wargame analyst focused on counter-insurgency and asymmetric warfare. After leaving the Pentagon in 2009, Nathan helped start TickIt Trading Systems, a disruptive software platform that was designed to democratize high frequency trading by removing the advantages of colocation and making it much easier to develop algorithms and trading systems. TickIt was acquired by Trading Technologies. Nathan received a B.S. and M.S. in International Relations from the Georgia Institute of Technology and an M.S. in Quantitative Methods in Political Science from Emory University.

“Risk Management of Systematic Strategies” — Massimo Cutuli

Mr. Cutulli has 20+ years of experience in Financial Services and Management Consulting. His most recent role was as Head of Risk for Citadel Securities, a Chicago based market maker. Prior to Citadel Securities he worked in banking at JP Morgan and Goldman Sachs in NY in various risk management roles and spent time with PWC’s Finance and Risk Advisory practice. Massimo has been a member of the Chicago Mercantile Exchange and London Clearinghouse risk committees and serves on the Board of Advisors of the Burridge Center for Finance at University of Colorado Boulder. He received a BS in Aerospace Engineering and an MS in Space Science (University of London), an MS in Aerospace Engineering (Cornell University) and MS in Operations Research (Columbia University).

Student Projects

For those industry-focused Quantitative Finance courses such as Algorithm Trading, the most important part of the learning is the student-led projects. Students are divided into small groups and work together to solve the real-world problems supervised by faculty of significant financial industry experiences. Students learn the value of teamwork in a simulated commercial environment with real deadlines to meet. Students are required to write a research paper on their project with both underlying theory as well as practical implementation results. An evaluation panel including industry practitioners in relevant fields to evaluate student’s final projects. Here are a select student projects for the Algorithmic Trading Class in Spring 2021:

  • Arbitrage Opportunities between ADRs and underlying securities
  • Pairs Trading Strategies with DBSCAN and other Clustering Methods
  • Statistical Arbitrage strategies involving basket of long and short stocks
  • Momentum Trading with volatility-based portfolio weighting scheme
  • A delta-neutral trading strategy that exploits differences between historical and implied volatilities
  • Pairs Trading Strategies augmented with fundamental analyses
  • Option trading strategies using interpolated implied volatilities surface
  • Credit default risk and implied volatility skew models