Advanced Empirical Finance

Topics and Data Science

The guiding question of this course: (How) can state-of-the-art methods improve financial decision-making? The lecture covers the theory and the recent papers — but most of your effort should go into actually doing empirical work.

🚀 Start here Get R, Python & Positron ready before the first lecture (Exercise 0). 🗓️ Lecture plan Week-by-week schedule with readings and assignment deadlines. 📊 Slides View and download every lecture deck, embedded on one page. ✏️ Exercises Hands-on problem sets that build the skills the lectures introduce. 📚 More resources Guides on Quarto, Git, Positron AI, and machine-learning workflows.

About the exercises

The exercise sets partially subsume work I created with my colleagues Christoph Scheuch and Patrick Weiss for the books Tidy Finance with R and Tidy Finance with Python. Feedback on every aspect is very welcome — if you spot typos, mistakes, or anything that deserves more attention, contact me or my colleagues at contact@tidy-finance.org.

Try to solve each question on your own before consulting the proposed answers, and discuss issues with your peers. The most direct way to get help is to post your questions on StackExchange or Absalon. As an absolute minimum, make sure you are familiar with Grolemund and Wickham’s R for Data Science before starting.