Topics for BSc and MSc Thesis and Research Modules

Work on real research problems in financial machine learning and data-driven asset management.

Are you interested in working with real data, advanced models, and contributing to active research? We invite motivated and quantitatively inclined Bachelor and Master students to participate in current projects led by our professor and Ph.D. researchers through KIT research modules, Bachelor theses, or Master theses.

Many topics are directly embedded in the ongoing work of the KABFI Ph.D. training group, giving students the opportunity to contribute to projects that are both academically rigorous and highly relevant to data-intensive careers.

Two Research Groups, One Integrated Ecosystem

Open topics are organized around the focus areas of our two research groups. Students can choose between these tracks depending on their interests and skills:

AI-Finance Group

Focus: Machine learning, AI algorithms, financial data science
Example areas:

• Reinforcement Learning: Portfolio optimization, market simulation

• Supervised Learning: Return prediction, option pricing, alpha generation

• Natural Language Processing: Central bank communication, sentiment extraction, financial news analytics

• ...

These projects are ideal for students interested in computer science, machine learning, and large-scale data analysis.

C-RAM Group (Computational Risk and Asset Management)

Focus: Risk and return modeling, empirical asset pricing, derivatives, and macro-finance
Example topics:

• Volatility and tail risk modeling using high-frequency option data

• Risk premia estimation from survey and derivative markets

• Macro-finance modeling of asset markets

• Textual analysis of monetary policy communication

• ....

Projects in this track combine financial theory with statistical modeling and computational tools for applied risk and asset management research. Please reach out directly to maxim ulrich ∂does-not-exist.kit edu if you are interested on C-RAM topics.

How to Get Involved

1. Browse the topic areas linked above (Reinforcement Learning, Supervised Learning, NLP).

2. Identify a KABFI Ph.D. researcher working on a topic of interest. If in doubt, reach out to maxim ulrich ∂does-not-exist.kit edu.

3. Reach out to discuss supervision and scope.

4. Follow the step-by-step process outlined in the flowchart provided at the bottom of the page.

All projects are supervised by Prof. Dr. Maxim Ulrich and at least one Ph.D. researcher of the KABFI PhD program and includes structured guidance in data handling, modeling, and academic writing.

Where This Can Lead

Many former students have transitioned from their research module or thesis work into:

• Co-authored academic publications. For instance: https://link.springer.com/article/10.1007/s11147-023-09195-5

• Fast-track Ph.D. positions within the KABFI program

• Data science roles in top-tier firms in Zurich, London, Frankfurt, and New York

As one former student, now at Morgan Stanley in New York, reflected:
“Your classes truly prepared me for the interviews, and our project provided me with a lot to discuss. Topics ranging from factor models to stochastic discount factor and machine learning were part of the conversations, and the way you taught us about these subjects gave me the confidence to speak about them.”

Questions?

For general guidance or to discuss which path best suits your background, please contact:
Prof. Dr. Maxim Ulrich
maxim.ulrich∂kit.edu