Course Offerings WINTER TERM

 

 

BACHELOR'S, MASTER's AND PHD PROGRAM

 

One Bachelors' module (B.1), one Masters module (M.1) and two PhD modules (PHD.1 and PHD.2) are offered during the winter term. B.1 is a lecture and tutorial oriented module for students who see a gentle, yet, rigorous introduction to the exciting field of Financial Economics. M.1 is a research oriented module for students well equipped with finance and statistics and Python coding experience.

PHD.1 is an advanced lecture and tutorial oriented module for learning advanced modeling concepts and quant tools for research in financial economics, computational risk and asset management and AI-finance. PHD.2 is a research oriented reading group to discuss current developments in the field of deep financial economics.


MODULE (B.1): INTRODUCTION TO FINANCIAL ECONOMICS

Prerequisites: Passing grade in VWL 3 (Introduction to Econometrics)

The module consists of two weekly lectures and respective tutorials: (i) Static Portfolio Choice and Asset Pricing Theory, (ii) Essentials for Financial Machine Learning. There will be a single modul-wide exam at the end of the semester for the 9 ECTS module.

 

                   

MODULE (M.1): ADVANCED MACHINE LEARNING AND DATA SCIENCE

 This module is a coding oriented research project in the interdisciplinary area of AI-Finance.

 

                                                                                                                                     

MODULE (PHD.1): DEEP FINANCE

The module consists of weekly lectures and tutorials for the following courses: (i) Advanced Concepts in Financial Engineering, (ii) Code Review for AI-Finance Applications, (iii) Topics in Deep Learning in Finance


                                                                                                                         

MODULE (PHD.2): Reading Group in Deep Financial Economics

This module is a research oriented discussion group to assess the quality and impact of newly published research paper.

 

The following paragraphs provide further information for all courses, closely related to the information from the "Modulhandbuch".


(B.1.i) Static Portfolio Choice and Asset Pricing Theory (2/2)

This course is an advanced undergraduate introduction to the theory of asset pricing and portfolio choice. This is the foundation for the `investments' branch of finance.  Understanding how assets are priced is also important for issuing entities, like corporations, so asset pricing is also part of the foundation for corporate finance.  Of course, prices are determined by supply and demand.  We take supply (a topic in corporate finance) as given in this course and study demand (portfolio choice).  Mostly, we'll work in the neoclassical framework and encounter numerous Nobel Prize winning concepts on the way.

Modern financial machine learning successes from supervised learning to reinforcement learning exploit asset pricing and portfolio choice theories. This course does provide the necessary basic intuition and concepts for such advanced endavours.

Uncertainty and time are the two key elements of portfolio choice.  This Bachelors course will focus mainly on single-period models, which makes uncertainty (risk) to be the main element of portfolio choice. This suffices for building intuition and understanding.


Student learn about the following concepts:
* Utility function and Risk Aversion
* Portfolio Choice and Stochastic Discount Factor
* Equilibrium and Efficiency
* Arbitrage and Stochastic Discount Factor
* Mean-Variance Analysis
* Linear Factor Models
* Representative Investor

The lectures develop all concepts on the whiteboard. Weekly tutorials solve and discuss exercises.

 


(B.1.ii) Essentials for Financial Machine Learning (2/2)

Once you have a model for financial markets, the next step is to confront the model with data in order to learn the model parameters. Doing so allows you to simulate the financial market, make quantitative predictions on where asset prices are in the future and assess the uncertainty around your model forecasts.

This courses provides an introduction into how to estimate financial models. The introduction is based on theory and algorithms. Students do also obtain access to ipython notebooks to observe different estimation algorithms at work. Students will learn to derive the following learning algorithms and implement these in Python with different financial data sets:

* Linear Factor Models
* Efficient Markets
* Univariate Time Series Modeling
* Simulating Financial Markets
* Estimating linear models with OLS and MLE
* Estimating non-linear models with MLE
* Forecasting stochastic volatility using ARCH(m) and GARCH(m,s) models

Lectures develop all concepts on the whiteboard. Tutorials discuss Python programs for selected problems.


(M.1): Advanced Machine Learning and Data Science

The course "Advanced Machine Learning and Data Science" offers students the opportunity to deep dive into research on current developments in the area of AI-Finance.

Aspects of supervised, unsupervised and reinforcement learning are combined with financial economic modeling to improve our understanding of financial markets and risk management.

Typically, students obtain access to research clusters with sizeable CPU and GPU units and starting code in Python or C++.

The complexity of topics varies to meet the students prior experience and wishes for further development.

Grading of this 9 ECTS module is based on a written scientific report, documented code and an oral presentation at the end of the semester.

Prerequisites: Students must have obtained an A or B in the module "Modeling the Dynamics of Financial Markets" or equivalent MSc courses in the field of Financial Economics and Statistics and Python programming.


(PHD.1.i) Advanced Concepts in Financial Engineering

The course covers advanced research-oriented concepts to equip students to read and understand current research in the field of quantitative financial modeling.

The course aims to strike a balance between foundational mathematical and financial economic concepts as well as new developments.

Prerequisites: Students are enrolled as PhD students at KIT.


(PHD.1.ii) Code Review

This course focuses on reviewing published research code on current topics in AI-Finance.

Prerequisites: Students are enrolled as PhD students at KIT.


(PHD.1.iii) Topics in Deep Learning in Finance

This course provides an in-depth analysis of how deep learning is being applied to tackle traditional unresolved problems in financial economics.

Prerequisites: Students are enrolled as PhD students at KIT.


(PHD.2) Reading Group in Deep Financial Economics

This weekly reading group discusses current research papers in the field of financial economics and deep learning.

Prerequisites: Students are enrolled as PhD students at KIT.