Quantitative Finance

Neoclassical theories form the intellectual backbone of modern finance, guiding the decisi-ons of investors and firms alike. Yet whether these theories hold up in reality is ultimately an empirical question. In this course, we explore six cornerstone results of financial theory and uncover the fundamental properties of stock, bond, and options markets. Each of these six parts is paired with the study of an empirical method designed to test the validity of the respective theory against real-world data.

The lecture series is complemented by a Python tutorial. In three hackathons, students will design and implement empirical tests, working in teams to analyze large datasets and present their findings in concise presentations. Along the way, they will not only deepen their understanding of finance but also gain hands-on skills in data science and collaborative programming.

After an introduction to the foundations of finance, we begin with equity markets, focu-sing on the Capital Asset Pricing Model and predictors of the cross-section of expected stock returns. We then turn to bond markets, asking whether firms’ ecological footprints are reflected in the prices of their corporate bonds. Next, we examine corporate capital structure, specifically the choice between debt and equity financing. Following this, we explore option markets and the methods used to compute prices of derivatives. Finally, we ask whether machine learning techniques can help us improve the quality of earnings forecasts of professional analysts.

Quantitative Finance in the Winter Semester 2025/2026:

The course takes place during the first half of the winter semester, beginning on October 28 and ending in December. The language of instruction will be English. There will be two lectures per week on Tuesdays at 9:45 a.m. and 11:30 a.m. in Building 09.21 (Blücherstraße 17), Room 125. Additionally, there will be three hackathons, which will take place on Mondays between 2 and 6 p.m. A detailed schedule can be found below.

Course materials and getting in touch:

All course materials can be found on the ILIAS page of the course. Announcements related to the course will be published exclusively on ILIAS. If you have any questions, please feel free to email Fanchen or Julian, or use the forum on ILIAS.

Hackathons and grading:

There will be three hackathons in which students work in groups on data science problems in the field of finance. Some of the problems will replicate the studies discussed in the lectures, while others will address related questions using the same methods. In the middle of each hackathon, each group will share their results with another group and receive peer feedback. At the end of each hackathon, students will present their findings in short presentations.

In each hackathon, students can earn up to 25 points, depending on the quality of their code, empirical analysis, and presentations. The two best hackathon performances count towards the final grade, with a maximum of 50 points.

An additional 25 points can be earned by submitting a short report on one of the six major topics of the course. The report topics will be assigned via ILIAS at the end of the course, and reports must be submitted by January 25, 2026.