Data-Driven Investments: Equity
Rice University JGSB
Fall 2023
Instructor
Kerry Back
J. Howard Creekmore Professor of Finance and Professor of Economics
kerryback@gmail.com
Class Meeting
Room 317, 2:15 – 3:45, 10/23/2023 – 12/4/2023
Course Description
This course provides an introduction to quantitative equity management. Quantitative management means trading on signals that can be constructed and tested on large panels of stocks. We will use ChatGPT and python throughout the course. Students are not expected to be python experts. A side benefit of the course is the opportunity to learn and practice python. The course schedule by week is as follows:
- Time series models for return forecasting
- Factors and factor models
- Portfolios formed from sorts
- Machine learning models
- Backtesting portfolio strategies
- Risk analysis of portfolio strategies
Grading
Grades will be based on weekly assignments.
Data-Driven Investments Lab
A follow-on to this course, Data Driven Investments Lab, will be offered as a full-semester spring course. The Lab course will involve working in groups to explore other data sources and to further elaborate and test strategies. Strategies will be implemented with paper trading at Alpaca, which is a brokerage with a free python API. I will co-teach the course with Kevin Crotty.
CQA Competition
Each year, the Chicago Quantitative Alliance (CQA) hosts a competition for universities on quantitative investing - the CQA Challenge. In each of the past two years (2021-2022; and 2022-2023), a team of JGSB MBA students won it, finishing first among 30 or so teams. The winning JGSB teams have used machine learning methods developed in this course. The competition is to run a diversified long-short market-neutral portfolio with a quantitative approach. Paper trading is done using the StockTrak platform. Teams are judged on compliance, returns, and a video presentation of their strategy in three stages: teams that perform well on compliance proceed to the second stage, the top ten teams on compliance and portfolio returns proceed to the third stage, and teams in the third stage prepare video presentations.
Honor Code
The Rice University honor code applies to all work in this course. Each student must do his or her own assignments, but it is allowed and in fact encouraged for students to seek advice from each other. Likewise, groups must do their projects, but they can seek advice from students in other groups. Also, searching for advice on the internet is allowed.
Disability Accommodations
Any student with a documented disability requiring accommodations in this course is encouraged to contact me outside of class. All discussions will remain confidential. Any adjustments or accommodations regarding assignments must be made in advance. Students with disabilities should also contact Disability Support Services in the Allen Center.