We are offering several research-oriented risk, investment and FinTech courses with a KIT specific information technology blend. Our teaching staff (mainly the professor + PhD students) aims to closely monitor the progress of every student, both during lectures and during projects. During project, seminar, bachelor and master thesis work, students are invited to learn more about the CRAM's topical research projects and to contribute with their own independent research work.
Executive Summary for Module "Empirical Finance"
The aim of this course is to introduce the student to empirical data work in financial economics and investments. Students will learn and implement modern portfolio theory and the most important concepts to estimate expected returns and volatility. At the core of this lecture is the work on modern portfolio theory of Markowitz. Students will learn how to allocate investment opportunities to an optimal portfolio under investment constraints. To obtain the necessary inputs to this framework, students will revisit statistical concepts such as linear regression and maximum likelihood estimation to estimate expected returns and volatilities with econometric time series models.
Executive Summary for Module "Data Science for Finance"
The aim of this course is to master real-world challenges of computational risk and asset management and provide students with a skill set to incorporate different portfolio objectives into the investment process. It enables students to solve such challenges independently in Python. Students will build up on the statistics and finance knowledge from their Bachelors program to learn about how to automatize modern quantitative portfolio strategies. Students learn about advanced topics which are relevant for a realistic, real-world asset and risk management process.
Executive Summary for Module "FinTech Innovations"
This project invites students to either pursue their own FinTech innovation project or to contribute to the Chair's ongoing innovation projects. Students will learn to connect innovative financial research with modern information technology to build a prototype that solves some daunting tasks for professional end-users in the field of modern asset and risk management. The course is targeted to students with strong knowledge in the field of computational risk and asset management and strong programming skills. It offers students the opportunity to develop an algorithmic solution and hence ample their programming experience and their understanding of financial economics or asset and risk management.
In order to take the course "Engineering FinTech Solutions", students must have completed the module "Data Science for Finance" with a grade of 1.3 or better.
Suggested Sequence of Courses
Bachelor-, Master-, and PhD Thesis
We invite students to work on exciting "Computational Risk and Asset Management" research problems, to engineer topical FinTech solutions, or to combine one with the other.
For example: Imagine a KIT industrial engineering student with no particular interest in finance but with an interest in technology. Why not be creative and combine the passion for informatics and/or social media and/or data science or technologies at large with some finance problem to engineer a FinTech solution that helps society? McKinsey&Company's FinTech report (March 2016).
Whatever your topical interest or passion: Get in touch! Contact