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.
- Current UChicago undergraduate students eligible for financial aid during the academic year may apply for Summer Scholarships through the Office of Financial Aid. Visit the Summer Scholarship webpage for more information and link to the application. Summer Scholarship applications are due April 30, 2021
- Dependent children of benefits-eligible UChicago faculty, staff, and other academic appointees are eligible to apply for the Summer Session Tuition Benefit, which provides a 25% tuition scholarship. Visit the Summer Tuition Benefit webpage for more information.
- Visiting undergraduate students may apply for need-based financial aid for this course, which mainly comes in the form of partial scholarships. To be considered for these need-based financial aid awards, all application materials and financial aid form must be completed in your application status page.
- An unofficial transcript from all post-secondary institutions you have attended
- An essay
- Your resume
- International students may be required to submit TOEFL (90 or higher) or IELTS (7 or higher) scores. A copy of the entire report should be provided, not just the final score. Visit the International Visiting Summer Students page for more details about the test score requirement and testing alternatives.
- Financial Aid & Scholarship Program Application with supporting materials (if applying for need-based aid).
- Regular Deadline: March 31, 2021
- Extended Deadline: April 30, 2021
- Rolling Admissions: May 1 - May 15, 2021
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.
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 21 through August 13, 2021. 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.
Since the Independence Day holiday is observed Monday, July 5, the synchronous session that week will be held on Tuesday, July 6.
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
The tuition for the course in Quantitative Portfolio Management and Algorithmic Trading for Summer 2021 is $4,250. Current UChicago undergraduate students will also be charged the Student Life Fee, estimated at $470 for Summer 2021. Students residing more than 50 miles from campus may apply for a waiver of this fee through the Bursar's Office once bills are sent. Contact firstname.lastname@example.org for more information.
Current UChicago undergraduate students may self-register for the course through my.UChicago.edu beginning on March 1, 2021. No separate application is required.
Visiting undergraduate students must apply for this course. Click here to start a new application.
In order to complete our program application, you’ll need the following items:
|Introductory Probability and Statistics||Introductory Programming in Python, R, or Matlab|
|Linear Algebra||Regression Analysis|
|**Students will get a refresher on these topics at the start of the course.||**These will be taught within the curriculum, but background knowledge is helpful.|
Applicants from any academic major are welcome! We are particularly interested in students with no previous background in finance who are interested in exploring Quant Finance as a career option.
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.