Modules WS 2024/2025

Data Management covers the modern data-management cycle, from the collection of data from diverse sources to the preparation of data for data-driven applications. Students learn how to handle various data formats, how to assess and improve data quality, and how to store and process data using SQL, NoSQL, and Hadoop technologies. The course covers eight primary topics:

  • Modern data-management requirements
  • Database system architecture
  • Diagnosing and handling data quality problems
  • Relational databases (SQL)
  • Hands-on labs with MySQL
  • Concurrency control techniques
  • NoSQL databases (e.g., MongoDB)
  • Apache Hadoop (HDFS, MapReduce)
  • Overview on different Forms and Asset Classes of Alternative Investments
  • Chances and Risks of Alternative Investments
  • Alternative Investments in a Portfolio Context
  • Regulation of Alternative Investments
  • Socially Responsible Investments and Impact
  • Alternative Investments and Corporate Governance
  • Cost of capital and capital budgeting
  • Discounted cash flow valuation and financial multiples
  • Payout policy
  • Equity and debt financing
  • Applications of option pricing theory
  • Corporate control and recapitalizations
  • Enterprise Risk Management
  • Role and Responsibility of Owners
  • Practice of Right of Control for Various Actors
  • Board structures and diversity
  • Theory, Principles, and World-Views
  • The Ethical Leader: Self-Mastery and Ethics, Mind-Sets
  • Corporate Ethics: Shared Values, Professionalism (as part of Standards of Professional Conduct)
  • Developing research questions and hypothesis
  • Designing qualitative and quantitative research
  • Writing and communicating research proposals
  • Theory, Principles, and World-Views on Ethics
  • The Ethical Leader: Self-Mastery and Ethics, Mind-Sets
  • Corporate Ethics: Shared Values, Professionalism (as part of Standards of Professional Conduct)
Paper-based preparation of topics, strategy implementation and testing, presentation and discussion
  • Investment Strategies by Asset Class: Equity, Fixed Income, Derivatives Strategies
  • Investment Strategies for Different Economic Environments
  • Asset Management Practice
  • Identification of a research problem and development of a research questionThematically formulating a problem and developing a solution through application of - scientific methods Independence in handling a research problem determined in the course of an assessment.Discussion with the advisor about methodological and content issues in solving a research - topic.Completion of a comprehensive assignment where the students deal with a theoretical or - practice-oriented problem in their field of specialisation by drawing on scientific work methods. Completion of presentation documentation on a research problem within their specialised - field. Defense of the elaborated research topic and in-depth discussion with the examination board.
  • Modelling the Human Life Cycle
  • Models of Human Mortality
  • Valuation Models of Deterministic Interest
  • Models of Risky Financial Investments
  • Models of Pension Life Annuities
  • Models of Life Insurance
  • Models of DB vs. DC Pensions
  • Sustainable Spending at Retirement
  • The Liechtenstein Pension System
  • Systematically identify and exploit opportunities.
  • Market-Pull, Technology-Push and Blue Ocean.
  • Opportunity Recognition as a process.
  • Systematization of business models and components.
  • Analysis and evaluation of business models.
  • Application of big data algorithms to identify new markets and technologies.
  • This course aims to optimize English language and general communication skills while raising self-awareness to enhance competence. Students will engage in focused information gathering, discussions, and practice. The course covers academic writing skills, negotiations, techniques of persuasion, presentation skills, and decision making. Emphasis is placed on self-reflection and teamwork in a shared learning environment.Key topics covered are:Academic writingPresentation skillsNegotiation techniquesPersuasion and argumentationDecision makingMicro office skillsLanguage developmentSelf-awareness and self-reflection
tba
The course is an introduction to the field of Finance, reiterating the most important concepts from a bachelor's degree with a focus on Finance. It builds on the time value of money principle and applies it to the valuation of bonds, interest rates, and capital budgeting. The course also highlights some of the most significant markets for financial instruments. The main goal is to establish a strong foundation for understanding the key concepts of Finance.
Key topics covered are:
  • Introduction to financial markets
  • Interest rates and bond prices
  • Structure of interest rates
  • Market efficiency
  • Funds markets
  • Money markets
  • Bond markets
  • This course provides a comprehensive introduction to the application of econometric techniques in finance. Stu-dents will delve into both univariate and multivariate time-series analysis, gaining insights into key concepts of modern econometrics. Overall, students will be equipped with the necessary skills to analyse and interpret com-plex financial time-series data effectively. This course combines theoretical knowledge with practical application using R. Key topics covered are:Stationarity, differencing, and co-integrationHeteroskedasticity and volatility clusteringSelf-dependence and endogeneityVector auto regressions.Time series modelling, including ARMA and GARCHImplement and empirically test the above mentioned in R
The purpose of this course is to familiarize students with the statistical methods and tools necessary not only for producing high quality research output in finance, but also necessary to understand and apply the quantitative tools that are at the core of a modern and innovative financial business. In this context, students will recapture common statistical concepts such as regression analysis and hypothesis testing within financial data as well as the basics of linear algebra. Simultaneously, students will learn how to use R, a statistical software that has become standard in research and industry. A third competence gained during the course is about how to find and down-load data from professional (Refinitiv) and open-source data providers.
Key topics covered are:
  • Statistical programming in R (tidy data handling, programming)
  • Basics of linear algebra (vectors, matrices, systems of linear equations)
  • Descriptive statistics for uni- and multivariate analysis
  • Time series analysis
  • Hypothesis testing
  • Regression analysis: uni-/ multivariate, (non-)linear