Each notebook on this site starts from an idea in the empirical asset pricing literature and turns it into a portfolio an investor could hold. The notebooks do not claim new results. They show every step: the theory behind the idea, the data, the code, and how the portfolio performs once realistic constraints are imposed. All notebooks draw on the same data sources and report the same performance measures, so their results can be compared directly.
The notebooks in their current form use data from WRDS, which requires a subscription to reproduce, and the public factor returns of Jensen, Kelly and Pedersen. The rendered results are stored with the site, so the pages read the same either way. The notebooks page lists them by topic.
This site is a work in progress: notebooks are added and revised as they are developed.
I use AI openly to build these notebooks, specifically Claude, Anthropic’s AI model, through Washington University’s Claude for Education license. It writes most of the code and drafts much of the text, following my direction. I see my job as finding interesting questions and problems to teach: deciding which ideas from the research are worth testing, what each notebook should ask, and whether the answers make sense. That part is mine. I also review the code, check the results and edit the text. If AI lets me turn more good questions into working notebooks, students are better served. I believe AI tools are extremely useful for creating educational material.