Modules SS 2022

This module covers the practical application and implementation of concepts in portfolio management. Using an online tool with real and live market data, student groups manage their own portfolio throughout the semester. This includes the specification of an investment strategy at the beginning of the course, frequent trading and writing a fund brochure and performance report.

Master students have the opportunity to take part in educational journeys to the world’s most important financial centres. Taking place

All aspects that have to be considered for writing a successful exposé and master thesis, like e.g. the fulfillment of formal requirements, the development of a research project and the correct choice of methodology for answering the research question.

> Review of Portfolio Theory and Asset Pricing
> Extensions of the CAPM
> Empirical confirmation/rejection of the CAPM
> Stock Market Anomalies
> Multi-Factor Models
> Investment Strategies
> Performance Evaluation
> Portfolio Execution, Monitoring, Rebalancing and Costs

• The course Innovative Finance: Data Science and Machine Learning 1 will give students the understanding and necessary tools to apply Machine Learning methods to essential research problems in finance.
• Statistical learning (aka Machine Learning or Artificial Intelligence) is the main driver of innovation in the financial industry and can be found almost everywhere: credit decisions, risk management, fraud prevention or (automated) investment processes.
• Therefore, this course will pick up where Quantitative Finance stopped and further explore methods of supervised and unsupervised learning, thereby teaching our computers to learn from the large amounts of data available to us.
• The entire course will be accompanied by (small) real-world-real-data applications making use of Googles’ free and powerful Colab and Kaggle platform.
• For those with a further interest in Innovative Finance: Join Innovative Finance: Data Science and Machine Learning 2 for a real and big-data based machine learning challenge, entirely hosted on www.kaggle.com.

In particular, this course will cover:

• Linear model selection and regularization
• Resampling methods, model assessment and selection
• Tree-based methods
• Neural networks and deep learning
• Unsupervised learning

The content of the course is the analysis of different business models/case constellations at the interface of different competing areas of financial market law, such as banking supervision, asset management and payment services as well as e-money. In addition, reference is made to Liechtenstein's current blockchain regulation.
Furthermore, in the area of asset management, current regulatory developments, such as in the area of sustainable asset management, will be discussed.