back to search

Campus Challenge: Successful Investing with AI and Large Language Models

MGTHN0122Specialization in Management6 ECTSEnglishwinter semesterProfessur für Finance (Prof. Müller) (TUM Campus Heilbronn)
AI-edited module sheet. Based on the TUMonline module description, edited for readability.Original in TUMonline

What it is about

You develop in a group a systematic equity investment strategy that uses AI techniques and/or Large Language Models (LLMs) or advanced chatbots. In the end you can justify, implement, evaluate, and scientifically document and present an empirically grounded strategy.

What you will be able to do

  • Motivate, create and implement an investment strategy
  • Work with Language Models and advanced chatbots
  • Conduct an empirical team project
  • Systematically and structurally answer research questions
  • Independently write a scientific report

What the module consists of

  • GruppenprojektCentral role: development, implementation and ongoing supervision of the investment strategy
  • PräsentationPresentation of results and answering questions (approx. 20 minutes per group)

Teaching method

  • Regelmäßige TeammeetingsCoordination and status updates on the portfolio
  • Gruppenbasierte Betreuung und PräsentationenEnables collaborative work and the formal presentation of results
  • Collaborative Implementation (Theory, Tools, Prompt Engineering, Data Preparation, Analysis)Practice-oriented development and documentation of the strategy
  • Unabhängige LiteraturrechercheTheoretical grounding of the strategy based on scientific articles
  • Gemeinsames Verfassen eines wissenschaftlichen BerichtsTraining in scientific argumentation and reporting

Dates

SeminarCampus Challenge: Successful Investing with AI and Large Language Models (MGTHN0122)3 groups to choose from

  • AFri09:30–17:00Online: Videokonferenzonce on 05.02.
  • BFri15:30–18:00Online: Videokonferenzonce on 16.10.
  • CFri16:00–18:00Online: Videokonferenz
    13× · 23.10.–29.01.
    • 23.10.
    • 30.10.
    • 06.11.
    • 13.11.
    • 20.11.
    • 27.11.
    • 04.12.
    • 11.12.
    • 18.12.
    • 08.01.
    • 15.01.
    • 22.01.
    • 29.01.

From the current semester, not binding. You attend one of several groups; the timetable automatically suggests the one with the fewest clashes.

Module ratings

No ratings for this module yet.

Rate this module

Only fill in the categories you can judge – for each one, either stars and text together or nothing at all.

Lecture
Tutorial
Exam

Reviews are automatically checked before they are published.

Official page in TUMonline · Details are not binding.