The University of Chicago welcomes students with strong quantitative skills to explore opportunities in the field of Financial Math. This course for current undergraduate and post-baccalaureate students in Quantitative Portfolio Management and Algorithmic Trading provides a rigorous introduction to modern applications in Financial Math through an interdisciplinary curriculum delivered via remote instruction by lecturers and industry experts affiliated with the Financial Math MS program at UChicago. Participants in this program will receive University of Chicago undergraduate course credit. Those who successfully complete this course and eventually matriculate in the Financial Math MS program within the next four years may apply credit earned towards that degree program.
This class will be delivered via remote instruction for Summer 2023.
Financial Math is the application of math, statistics, and programming within the finance industry.
Financial markets have become increasingly complex, requiring specialized skills to effectively predict opportunities for profit and manage risk. Demand has grown steadily for people who can understand, enhance, and develop complex mathematical models. These individuals - known as “quants” - are hired into a wide range of positions at places such as investment banks, hedge funds, trading firms, asset management companies, insurance firms, and FinTech providers.
The demand for quants is global, and individuals can choose from a variety of career paths in the industry, based on their skills and interests. For example, quants conduct research and develop new models to support algorithmic trading at hedge funds and trading firms. Others are performing quantitative risk analysis for large scale insurance or pension funds. Other examples include research and development of investment strategies at banks or portfolio analytics at asset management firms.
Statistics, math, finance and Python programming will be featured. Familiarity in some of these areas is helpful, but there are not strict pre-requisites. In addition, some experience in regression and programming is highly recommended. However, the course is accessible to motivated students still new to some of these areas.
During the course, you’ll have a chance to learn more about careers in Quant Finance through presentations by our Career Development Office team. We can’t wait to help you explore the exciting world of quantitative finance!
The course in Quantitative Portfolio Management and Algorithmic Trading will be held June 12 through August 11, 2023. The course will be presented via remote instruction through a mix of synchronous (real-time) and asynchronous sessions.
Synchronous sessions will be held on Mondays from 6:00 to 9:00pm Central time. Other synchronous sessions with Teaching Assistants or study groups will be scheduled once the course begins.
Holidays that will be observed during this session will be Juneteenth on June 19; the synchronous sessions for this day will be held of Tuesday, June 20.
This course in Quantitative Portfolio Management and Algorithmic Trading teaches quantitative finance and algorithmic trading with an approach that emphasizes computation and application. The first half of the course focuses on designing, coding, and testing automated trading strategies in Python, with particular consideration to market models, infrastructure, and order execution. The second half of the course builds on this by covering case studies in quantitative investment that illustrate key issues in allocation, attribution, and risk management. Students will have the chance to learn classic models as well as more modern, computational approaches, all illustrated with application.
1. Returns: Premium, volatility, correlation, beta
2. Allocation: Mean-variance analysis, risk parity, robust methods
3. Performance Attribution: Replication, attribution, evaluating performance
4. Risk Management Hedging, immunization, Value-at-Risk
5. Factor Models CAPM, systematic risk, idiosyncratic risk, rationality
6. Multi-Factor Models Value, momentum
7. Model Selection LASSO, PCA, regression trees, ensemble methods
8. Overview of Trading and Markets
9. Time series and Momentum
10. Enhancing Trading Strategy and Data Mining
11. Pattern recognition techniques and Decision trees
12. High Frequency Part I
13. High Frequency Part II and Microstructure
14. Trading System Design
For details on costs, billing, and refund or withdrawal deadlines, see the Summer Quarter page.
Current UChicago Students
Current UChicago students must request term activation prior to self-registration. Self-registration for Summer Quarter occurs through My UChicago and opens on March 1, 2023. See the Current UChicago Students page for more information on Summer Quarter registration.
Visiting Undergraduate and Graduate Students
Statistics, math, finance and Python programming will be featured. Familiarity in some of these areas is helpful, but there are not strict pre-reqs. Some experience in regression and programming is highly recommended. But the course is accessible to motivated students still new to some of these areas.
Applicants from any academic major are welcome! We are particularly interested in students with no previous background in finance who are interested in exploring Quantitative Finance as a career option.
Admitted visiting students should review the Visiting Undergraduate Summer Students page for information on essential steps to connect to your course, including setting up your CNET ID (email/system login), UChicago Zoom, UChicago VPN, Canvas, library access, and more.
Meet Our Instructors
Mark Hendricks is the Associate Director of the Master in Financial Mathematics where he helps manage all aspects of the program. His industry experience includes quantitative research for a hedge fund, Racon Capital. He has also done consulting work in finance, (asset management, corporate, real estate,) and data analysis (retail and pharmaceuticals.)
Mark has taught courses, reviews, and workshops at the graduate level for Financial Math, the Booth School of Business, and the Department of Economics. Among other things, he has significant experience teaching portfolio management, dynamic asset pricing, corporate valuation, and statistical estimation. Mark’s courses emphasize active learning with application and data.
As a Ph.D. candidate for Financial Economics at the University of Chicago’s Booth School and Department of Economics, Mark won awards including a Stevanovich Fellowship and Lee Prize. Mark holds an M.A. in economics and a B.S. in Mathematics.
Sebastien Donadio is currently Chief Technology Officer at Tradair. There he is in charge of leading the technology team. He has a wide variety of professional experience, including being the head of software engineering at HC Technologies, quantitative trading strategy software developer at Sun Trading, partner at AienTech, high-frequency trading hedge fund, working as technological leader in creating operating system for the Department of Defense. He also has research experience with Bull, and an IT Credit Risk Manager with Société Générale while in France.
Sebastien has taught various computer science courses for the past fifteen years. This time was spent between the University of Versailles, Columbia University, University of Chicago, NYU. Courses included: Computer Architecture, Parallel Architecture, Operating System, Machine Learning, Advanced Programming, Real-time Smart Systems, Advanced Financial Computing.