BACHELOR'S, MASTER'S and PhD Level

One Bachelors' module (Bsc), one Masters' module (Msc) and one PhD module (PhD) are offered during the summer term.

MODULE (Bsc): FINANCIAL DATA SCIENCE

- 9 ECTS (one full module); Vertiefungsstudium

- Grading is based on Python problem sets. No "classical" sit-in exam; rather a real-world environment of solving real-world problems in a small team. The problem sets start very simple and get more advanced, requiring a skillful combination of lecture material, which ranges from financial-, analytical-, statistical-, to data engineering concepts. ... Get together with your 2-3 best friends and join the ride.

The topics are structured as follows:

    1.  Introduction to Finance

        - Valuation concepts and portfolio theory

        - Risk premia and the CAPM

        - Case studies showcasing practical applications of financial theories

    2.  Python Fundamentals and Data Handling

        - Effective data management and cleaning

        - Regression analyses and constrained optimization

        - Introduction to key libraries (e.g., NumPy, Pandas, PyTorch)

      3.  Machine Learning in Finance

        - Linear vs. nonlinear prediction models

        - Neural networks, random forests, and other ML methods

        - Feature selection and out-of-sample performance evaluation

    4.  Options and Volatility Analysis

        - Construction and analysis of implied volatility surfaces

        - ML-based option pricing and risk premia models

        - Systematic vs. idiosyncratic risks

    5.  Advanced ML Applications

        - Deep learning for equity and option return forecasting

        - Statistical arbitrage and end-to-end portfolio optimization

        - Model-based reinforcement learning for portfolio construction

    6.  Practical Case Studies and Examples

        - Utilizing modern ML libraries (PyTorch, TensorFlow)

        - Real-world datasets and empirical financial analysis

        - Discussion of computational challenges (e.g., Big Data, computational intensity)

MODULE (Msc): ADVANCED MACHINE LEARNING AND DATA SCIENCE

- Topics: End-to-End model-based reinforcement learning, deep supervised learning, natural language processing: with applications to financial market data

- Kick-off: Discussion and Allocation to topics takes place first week of the lecture period. For a later start contact us.

- contact: maxim ulrich ∂does-not-exist.kit edu

- A recent course review of a KIT computer science student: "Der Kurs <Advanced Machine Learning and Data Science> hat mir eine gute Möglichkeit geboten, auch ohne einen Finance-Hintergrund mein Wissen aus der Informatik erfolgreich anzuwenden. Zwar bekommt man jederzeit Hilfe bei Fragen, doch es war wirklich notwendig, sich intensiv mit dem Thema auseinanderzusetzen und eigene Ideen einzubringen, um den Kurs sehr erfolgreich abzuschließen. Besonders positiv fand ich, dass das Projektthema in Absprache mit den Veranstaltern nach meinen eigenen Fähigkeiten und Interessen gestaltet wurde, was mir viel Mitspracherecht und Flexibilität gab.

Insgesamt hat mir der Kurs als Informatikstudent die Chance geboten, mich auf das Thema Data Science im Finanzbereich vorzubereiten und wertvolle, berufsqualifizierende Erfahrungen zu sammeln." (Name of student is kept private).

MODULE (PhD): DEEP FINANCIAL ECONOMICS

- This module is a research oriented discussion group to assess the quality and impact of newly published research papers. Roughly 3-6 papers are presented per week and discussed.

- Researchers from other research groups and departments are very welcome to participate

- contact: maxim ulrich ∂does-not-exist.kit edu