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.
• This terms' Python problem sets include: (i) strategic asset allocation with numerical optimization, (ii) extracting predictive features for equity return predictions from option markets, (iii) finding market-neutral (long-short) portfolio strategies with high and significant alphas, (iv) learning classical machine learning models and evaluate their statistical and financial performance. We likely extend the problem sets by two applications: assessing the real-time intraday news impact on asset prices and robust reinforcement learning strategies for optimal asset allocation.
• Lecture: Monday: 8:00am - 11:15am, Engler-Bunte Hörsaal, R40.50
• Tutorial (Python "Focus Sessions"): Monday: 11:30am - 1pm, Egon-Eiermann Hörsaal.
• First meet-up is on April 28th (due to Ostermontag on April 21st).

The topics are structured as follows:

    1.  MBA Intuition for Core Finance Concepts

• Case Study: Theory of valuation 
• Case Study: Theory of optimal asset allocation
• Theory for determining the cost of capital, 
• Running predictive regressions and linear factor model regressions

    2.  Python Fundamentals; applied to Mean-Variance Portfolio Choice and Factor Regressions

• Regression analyses for linear factor models
• Constrained optimization for portfolio construction and likelihood-based learning
• Introduction to key libraries (e.g., NumPy, Pandas, ...)

    3.  A Bird's Eye View onto Tradeable Financial Data

• Machine Learning: Why Finance Data is Different
• A trader's view on data: spot vs futures markets; trading rates vs cash-flows vs volatility
• Arbitrage in spot-, futures-, derivatives markets

   4. Extracting Predictive Features for Stocks from Stock Options

• ECB case study on COVID-19

  5. Risk-Neutral PDFs: A microscopic view into the future

• Introduction to the Chair's big data database

  6. Artificial Neural Nets (ANN)

• Concept, training, validation, testing
• Application: learning the model that we used to generate prices of calls and puts
• Application: predicting next day's option prices
• Python: tensorflow, keras, sklearn

  7.  What Traders Know about the Black-Scholes-Merton Option Pricing Model

• Replication, martingale pricing
• Reasons for vol skew
• The Greeks
• P&L of option trading

   8. Risk Management

• Scenario analysis, Value-at-Risk
• Time-series risk forecasting using GARCH and Stochastic Volatility Models
• Python: MLE for GARCH
• Python:; Kalman Filter MLE for Stochastic Volatility Models

   9.  Machine Learning Portfolios

• Discussion of the tapastry of machine learning modeling approaches in literature
• Critical assessment of their empirical performance
• From kitchen-sink predictive models for stocks and options to constrained deep learning approaches to end-to-end model-based reinforcement learning approaches

Dedicated Tutorials ("Focus Sessions"), offer insights into the workings of Python for big datasets. Topics include

(i) numerical optimization: constrained mean-variance efficient portfolios, empirical likelihood functions,

(ii) empirical asset pricing: constructing predictive features, efficient portfolio sorts for finding high alpha portfolio strategies,

(iii) supervised learning: predicting stock returns with machine learning methods, learning option pricing models,

(iv) intraday price variations: on the importance of economic events,

(v) model-based reinforcement learning: end-to-end robust portfolio decision making systems.

MODULE (MSc): ADVANCED MACHINE LEARNING AND DATA SCIENCE

9 ECTS Research Project

This research-oriented module offers highly motivated Master students the opportunity to work in small interdisciplinary teams (2–6 students) on applied machine learning and data science problems using real-world financial data. Students from industrial engineering, computer science, mathematics, and related fields are welcome—team composition may intentionally combine complementary skill sets.

Topics

Projects cover modern machine learning techniques with direct application to financial market data. Examples include:

• End-to-End Model-Based Reinforcement Learning

• Deep Supervised Learning and Forecasting

• Natural Language Processing applied to financial communication and market sentiment

All topics are connected to ongoing research projects at the Chair of Financial Economics and Risk Management and align with the broader KABFI Ph.D. research environment.

️ Organization & Timeline

• Kick-Off Session (Virtual):
  Tuesday, 22.04.2025, 17:30–20:00
  The kick-off takes place online via Microsoft Teams. You will receive an overview of all available topics, details on the project structure, and have the opportunity to ask questions.
  Click here to join the Kick-Off Session

• Topic Selection Deadline:
  Thursday, 24.04.2025, 20:00
  Please submit your Priority A and Priority B topic preferences within 48 hours after the kick-off.

• Early Start Option:
  If you wish to start early, you may register immediately via:
  Form 1 or Form 2

• Contact:
  Prof. Dr. Maxim Ulrich – maxim.ulrich∂kit.edu

Student Perspective

„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.“
— KIT Computer Science Student (name withheld)

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