Data-Driven Finance II
Rice University
Spring 2024


Kerry Back
J. Howard Creekmore Professor of Finance and Professor of Economics

Meeting Schedule

Room 317, McNair Hall
TTh 10:50 – 12:05
1/8/2024 – 2/23/2024

Course Description

The first half of the course will cover options and futures for risk management and investing. We’ll explain what the securities are and how they trade, how they are used in risk management and investments, and how to value them. The second half of the course will be devoted to active portfolio management and specifically factor investing. We will review the historical results of factor investing and study how to optimally combine factors using machine learning techniques. We will analyze the results in the way that endowments and other institutions evaluate investment managers. We’ll do a real-world implementation of our models with paper trading via the python API at Alpaca Brokerage.

Assignments and Grading

Grades will be based on a group project, weekly assignments, and a final exam. The group project will account for 20% of the course grade, the assignments for 40%, and the final exam for 40%. The final exam will cover only options and futures. It will be distributed on Feb 22 and due on Feb 27. The group project is due April 23.

Group project

Groups should consist of two or three students. The project is to implement a 140/40 portfolio strategy at Alpaca brokerage, using their python API for paper trading. The portfolio should be 100% long in SPY and 40% long-short with the longs and shorts based on return predictions from a machine learning model. Each group should submit a pdf describing their investment strategy and an evaluation of their results. The strategy should be implemented by Friday, Feb 23. It should be rebalanced weekly, and results should be reported through Friday, Apr 12. The write-up should include an analysis of the weekly returns relative to the weekly returns of SPY.

Tentative Schedule by Day

  1. Options and their uses
  2. Option spreads
  3. Option valuation
  4. Introduction to futures
  5. Spot-futures parity
  6. Risk-management case
  7. Factor investing
  8. Backtesting
  9. Backtesting machine learning models
  10. Strategy evaluation
  11. Industries and sectors
  12. Alpaca API
  13. Rebalancing

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.

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 or the final exam must be made in advance. Students with disabilities should also contact Disability Support Services in the Allen Center.