Advanced Quantitative Finance

Neoclassical theories form the intellectual backbone of modern finance, guiding the decisions 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 exploring the capital structure choice of public companies. We then study factor models and the foundations of asset pricing theory, including intertemporal portfolio choice and the stochastic discount factor. In the later part of the course, we study American options and credit risk. In terms of methods, we cover the bootstrap, panel and cross-sectional regressions, instrumental variable regressions and the generalized method of moments, and, in the later part of the course, focus on machine learning techniques, such as neural networks and random forests.

 

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.

 

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.