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.
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.
THU SEP 3
Linear Regression for Multiples
Predicting a multiple from a company's fundamentals. Regression, fitted values, and a backtest of the signal.
Week 3
September 8–10
TUE SEP 8
Introduction to Machine Learning
Tree-based models. Training and test samples, overfitting, and cross-validation.
THU SEP 10
Predicting Multiples with Machine Learning
Swapping the regression for a machine learning model, and backtesting the trading signal.
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
Short Sales and Insider Trading
Short interest, short volume, and Form 4 filings. Turning data into signals and backtesting.
Week 5
September 22–24
TUE SEP 22
Text Analysis and Sentiment
Working with a corpus of financial news. Measuring sentiment in text.
Week 6
September 29 – October 1
TUE SEP 29
Risk Models
Factor models of how stocks move together. Market, industry and style factors. Covariance shrinkage.
THU OCT 1
Portfolio Risk
Putting the risk model to work: measuring a portfolio's factor exposures and risks, and building one that takes only the bets you meant to take.