Current Master Topics
The Chair of Entrepreneurial Finance offers two structured thesis tracks designed to help students pursue quantitatively rigorous research on timely and relevant topics in private markets. Because high-quality empirical work in private equity is often constrained by limited access to reliable data, our thesis structure reflects these practical realities. One track builds on our proprietary deal-level dataset, while the other leverages semi-public sources to explore broader market questions.
ALTQNT Private Equity Flagship Track
The ALTQNT track is the chair’s flagship thesis program, built around our long-term effort to develop one of Europe’s most detailed proprietary datasets on private markets. Students join a cohort that first works together to transform GP reports into structured, analysis-ready data — the foundation for later individual thesis projects.
Click Here for more Information on the Data Phase
Intake: 8 per cohort
After completing the data phase together with their cohort, students move on to the research phase, where they can either define their individual research question in consultation with the supervisors or choose from a selection of predefined topics:
| ID | Topic | Supervisor | Link |
| MA1 | Redefining Risk-Adjusted Returns of PE Funds through Advanced Analytics | Dr. Sara Boni & Dr. Max Knicker | more |
| MA2 | Nowcasting Net Asset Values: The Case of Private Equity | Dr. Sara Boni | more |
| MA3 | Strategic Investing in Illiquid Assets: A Modern Portfolio Approach | Dr. Max Knicker | more |
| MA4 | Mapping the DNA of Private Equity Cash Flows: A Quantitative Framework for Modeling, Classification, and Simulation | Dr. Max Knicker | more |
| MA5 | Winners Keep Winning? Performance Persistence and Forecasting in Private Equity | Philipp Bockshecker, M.Sc. | more |
| MA6 | Beyond Time Diversification: Clustering Private Equity Vintages for Resilient Portfolio Design | Dr. Sara Boni & Dr. Max Knicker | more |
Empirical Finance Research Track
This track covers a range of thesis projects that draw on semi-public or student-assembled datasets such as PitchBook, Crunchbase, or Orbis. It’s designed for students who want to explore contemporary questions in venture capital, growth finance, and private equity without relying on proprietary data. Each topic offers a defined starting point, academic supervision, and the flexibility to tailor the analysis to individual interests.