Academic Thesis Work at the Intersection of Machine Learning and Financial Economics

We supervise Bachelor, Master and PhD theses that sit at the intersection of machine learning, data science, and financial economics. Our focus is on applying and developing modern computational methods—ranging from supervised learning to reinforcement learning—in the context of high-dimensional, high-frequency financial data.

Thesis projects often use proprietary or industry-grade datasets and aim to contribute to both practical insight and academic publication. We are particularly interested in research that combines algorithmic sophistication with economic interpretability.

The best theses are eligible for a tax-free award of up to €3,000, generously funded by our industry sponsor association, the Forschungsgesellschaft Geld-Banken-Bausparkassen-Versicherungen am KIT e.V.

Recent examples demonstrate the impact and diversity of our thesis supervision:

  • A thesis developed in collaboration with a major European exchange built a stress-testing tool for all traded products—directly integrated into the exchange’s risk architecture.

  • Another project, combining option data with nonparametric estimation techniques, was published in The Review of Derivatives Research and led to a data science position at a tier-1 consulting firm in Zurich.

  • A Master thesis exploring machine learning and asset pricing served as the foundation for a student’s successful interviews and job offer at Morgan Stanley in New York. He later shared:
    “Your classes truly prepared me for the interviews, and our project gave me a lot to discuss… Topics like factor models, stochastic discount factor, and machine learning gave me the confidence to speak about them.”

  • A KABFI graduate joined Barclays in London as a quantitative researcher. After multiple rounds of interviews, he reported:
    “The interviews were like your lecture notes… The ability to connect mathematical models to economic intuition made the difference.”

Students are well-prepared for thesis work through our BSc and MSc modules—Financial Data Science and Advanced Machine Learning & Data Science—which emphasize hands-on coding, statistical modeling, and interpretability of algorithmic output.

We welcome students from finance, economics, computer science, or related fields who want to work on technically rigorous, high-impact questions in financial data science.

Push the frontier with us—at the interface of machine learning and market design.

Contact:
maxim.ulrich∂kit.edu