Gen AI and Quantitative Investments
led by Kerry Back
Week 1
August 25–27
TUE AUG 25
Getting Started
Course mechanics. Intro to quant investing. Intro to gen AI. Our AI Lab (Claude Code/GLM 4.7 + Python in a container).
THU AUG 27
Median of Peer Multiples
Introduction to the Nasdaq Data Link data. Finding industry/size peers and valuing by multiples. Analyzing model/market differences.
·Files
Week 2
September 1–3
TUE SEP 1
Trading on Predictions
Turning a valuation signal into a portfolio. Ranking stocks, going long and short, and backtesting what the strategy would have earned.
Slides·Files
THU SEP 3
Linear Regression for Multiples
Predicting a multiple from a company's fundamentals. Reading the regression output, holding out a test sample, and the transformations that decide the answer.
·Files
Week 3
September 8–10
TUE SEP 8
Ridge, Lasso, and Trees
Penalized regression, regression trees, and gradient boosting, each with a slider on the slide. Overfitting, and choosing a tuning parameter by cross-validation.
THU SEP 10
Build a Stock Recommender App
Wrapping the fitted models in a web app. Pick a stock, predict its multiple from its fundamentals, and see how far the market price sits from the model's.
·Files
Week 4
September 15–17
TUE SEP 15
Predicting Returns with Machine Learning
Predicting returns directly rather than by way of valuation. Building features, fitting the model, and backtesting the result.
THU SEP 17
Analyzing Earnings Calls
Where transcripts come from and what they cost. Can a model read a call and tell you to buy, and how fast would you have to act? Four architectures, and what we already know.
Week 5
September 22–24
TUE SEP 22
Trading on Earnings Calls
Turning what the model reads in a transcript into a signal, and backtesting it.
THU SEP 24
Short Sales and Insider Trading
Short interest and Form 4 insider filings as signals.
Week 6
September 29 – October 1