Summer Session

Financial Math: Quantitative Portfolio Management and Algorithmic Trading

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.  

Program Details

This class will be delivered via remote instruction for Summer 2022.

    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 13 through August 12, 2022. 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 course will be Juneteenth on June 20 and Independence Day on July 4.  Due to these, the synchronous sessions those weeks will be held on Tuesdays, June 21 and July 5. 

    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.

    Course Outline

    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

    Midterm Exam

    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

    Final Exam

    For details on costs, financial aid, 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, 2022.  See the Current UChicago Students page for more information on Summer Quarter registration.

    Visiting Undergraduate and Graduate Students

    The application for Summer 2022 programs is now open. See the Visiting Students page for more information or - go here to continue your application.

    Academic Prerequisites

    Required Helpful
    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.

    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. 

Mark Hendricks

Meet Our Instructors

Sebastien Donadio

Mark Hendricks

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

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.


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