Data-Driven Investments: Equity
Fall 2025

Instructor

Kerry Back
kerryback@gmail.com
J. Howard Creekmore Professor of Finance and Professor of Economics

Meeting Schedule

McNair 317
MW 2:15-3:45
10/27/2025 – 12/08/2025

Overview

The topic of this course is selecting stocks based on quantitative signals. What we want to do is to predict which stocks will do better than others. In quantitative investing, this is often done using machine learning, which means fitting a model to predict returns from signals. Signals can be technical or fundamental. Even subjective things like the tone of the CEO in an earnings call are quantifiable today. Thus, quantitative investing includes a large part of equity investing.

Many different types of data are used for constructing trading signals. In this course, we will use past returns and data from corporate financial statements. Textual analysis of corporate filings and news reports, social media activity, web traffic, insider trades, short sales, analyst forecasts, satellite imagery, and other types of data are also often used in practice.

Many studies have been published on the effectiveness of different quantitative signals for predicting stock returns. We will discuss those studies throughout the course and use them to guide our construction of signals.

Data and Tools

We will use data from the Rice Business Data Portal, and we will work with it in Python with the help of Anthropic’s Claude Code. We need the Pro account ($20/month). You will be reimbursed for the expense. Instructions on installing and using Claude Code will be provided in the class.

Assignments and Grading

Grades will be based on four group assignments, peer assessments, class participation, and a final group presentation. The group assignments are due on Sundays at 11:59 p.m. (Nov. 9, 16, and 23, and Dec. 7). The assignments are posted on Canvas.

Schedule, Slides, and other Materials

The schedule can be found at the Schedule/Materials link in the navbar at the top of the page. Links to slide decks and other resources are also there.

CQA Competition

Each year, the Chicago Quantitative Alliance (CQA) hosts a competition for universities on quantitative investing - the CQA Challenge. Twice recently (2022 and 2023), a team of JGSB MBA students from this course took first place, earning a prize of $3,000.

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.

The app in Assignment 4 could be used to generate recommendations for weekly trades. Unfortunately, the competition begins very shortly after the class begins, so some stopgap trades will need to be put in place before your app is ready.

Honor Code

The Rice University honor code applies to all work in this course. Use of generative AI is of course permitted.

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.