Quantitative Finance and Actuarial Science

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.

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).

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