Organizational Information

Dear student, if you plan to write your thesis at our chair or have been allocated to it, please read the following policies and procedures.

Due to capacity constraints, we can supervise only students allocated through the central systems of our department (master theses) or the faculty (bachelor theses). GSEFM students may be supervised if we can find a suitable supervisor and topic.

After receiving the allocation results, students will receive an email asking about prior experience, academic results, and areas of interest. Ensure timely response, as delays may lead to loss of supervision. Based on the answers, students will be assigned a supervisor, usually a “Wissenschaftlicher Mitarbeiter” or doctoral student of the chair.

We encourage you to review the Guidelines for Writing a Thesis and submit the Bachelor’s Thesis Registration Form. Master students should request the registration form at Prüfungsamt.

Pure literature reviews are not allowed; every thesis must include an original research project with a well-specified research question and quantitative analysis.

Students are rarely allowed to propose their own topics unless they have a detailed plan, including hypotheses, data sets, and a literature review. Thesis topics are typically developed jointly by students and supervisors.

Many students will be asked to use AI as a research tool, particularly on the Analystics platform, applying modern AI methods in Banking and Finance. More information can be found here: Thesis Topics AI.pdf and How ChatGPT Transforms the Careers of Financial Analysts.pdf.

Motivation

“The more I work on the topic, the more I learn—the more I get energized and want to contribute significantly,” wrote a master student working on his thesis at our chair.

Our students, often without prior programming skills, familiarize themselves quickly with the Analystics platform, developing algorithms to predict analyst questions, forecast parts of analyst reports, and much more. Students are encouraged to build their GitHub portfolios, mastering prompt engineering and data processing to gain a competitive advantage in the job market.

In the fast-evolving finance world, Analystics is transforming workflows by developing AI tools that are easy to use for financial tasks. Large Language Models (LLMs), such as ChatGPT, Gemini, and Llama, are crucial in this revolution, analyzing data from earnings calls, news articles, and analyst reports to provide predictive insights.