Modules SS 2023

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
  • Derivatives Markets and Instruments: Forwards, Futures, Options, Swaps
  • Pricing of Equity, Fixed Income, and Currency Derivatives
  • Hedging Using Derivatives
  • Financial Engineering
  • Blockchain Technologies
  • Bitcoin and Altcoins
  • Tokenization of assets
  • Crypto Wealth Manangement
  • Crowdfunding
  • Robo Advisory
  • Smart Contracts
  • Token Valuation
  • Crypto Exchanges
  • Tokenization of services and other goods
  • Trade Finance with Blockchain
  • InsurTech, PropTech and Social Trading
  • 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
  • This course builds on what you have learnt in Innovative Finance: Data Science and Machine Learning 1.
  • Based on a large real-world dataset, we will host our own Kaggle competition, where groups of students will compete against each other in a machine learning contest using financial data.
  • The challenge will be different each time, so we might forecast stock returns, classify stocks according to how green they are based on tweets and facebook posts or dynamically put together portfolios of cryptocurrencies that are expected to outperform in subsequent periods.
  • The course is structured as a lab, where we tackle all real-world issues related to the current challenge together, but will also run small competitions to get the most out of our data.
  • Grading will NOT be based on placement in the contest but focus on contribution to the final output and team work.
  • Introduction to Private Wealth Management, Estate and Succession planning
  • Structuring and Governance of Wealth and Wealth Management Structures
  • International Wealth Tax Management of UHNWI with Wealth Structures
  • The Liechtenstein Wealth Management Centre and other Wealth Management Hubs: BM, BS, CH, SG
  • International asset protection, family office and next generation issues
  • Onboarding of Private Clients (UHNWI) with Wealth Structures: Input Statements by banks & trustees
  • Case studies on International Wealth Management: Input Statements by banks & trustees
  • CFA level III: Topics in Private Wealth Management
  • Introduction to taxation of individuals and legal entities in selected jurisdictions: FL, AT, CH, DE
  • Application of Double Tax Treaties, Exchange of Information and Mandatory Disclosure Obligations
  • National and international taxation of Wealth Structures incl. their Settlors and Beneficiaries
  • International Tax Planning and Investment Hubs: CH, FL, HK, IRL, LU, SG
  • International Wealth Tax Management of UHNWI with Wealth Structures
  • Onboarding of Private Clients (UHNWI) with Wealth Structures: Input Statements by banks & trustees
  • Case studies on International Tax Planning of Private Clients (UHNWI) with Wealth Structures and Investments in Participations, Private Equity, Financial Instruments, Real Estate and Tangible Assets
  • CFA level III: Topics in Private Wealth Management
  • Introduction to International and European Tax Policy and Tax Standards
  • Principles of national and international taxation of individuals
  • Introduction to taxation of individuals and legal entities in selected jurisdictions: FL, AT, CH, DE
  • Application of International Double Tax Treaties to individuals and legal entities
  • Application of Exchange of Information and Mandatory Disclosure Obligations under EU-DAC 6
  • International Tax Planning, Investment and Wealth Management Hubs: BM, BS, CH, HK, IRL, LU, SG
  • Input Statements by Liechtenstein financial service providers
  • Case studies: International Tax Planning of individuals and legal entities incl. MNE (Apple, Nike)
  • Risk modelling
  • Strategical and tactical asset allocation
  • International diversification
  • Forecasting moments of asset returns
  • Foreign exchange rate risks and management
  • Portfolio management
  • Performance analysis
  • Behavioural finance
  • Dividend policy
  • Company valuation
  • Legal and tax issues in financial decisions
  • Risk modelling
  • Strategical and tactical asset allocation
  • International diversification
  • Forecasting moments of asset returns
  • Foreign exchange rate risks and management
  • Portfolio management
  • Performance analysis
  • Behavioural finance
  • Dividend policy
  • Company valuation
  • Legal and tax issues in financial decisions
  • 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.
  • Identifying, Measuring, and Management of Financial Risks
  • Risk Categories and Associated Models: Market, Credit, Operational and Liquidity Risks
  • Rating Agencies and Credit Ratings
  • Risk modelling
  • Strategical and tactical asset allocation
  • International diversification
  • Forecasting moments of asset returns
  • Foreign exchange rate risks and management
  • Portfolio management
  • Performance analysis
  • Behavioural finance
  • Dividend policy
  • Company valuation
  • Legal and tax issues in financial decisions
  • Current and past developments in sustainable finance
  • ESG data, data providers and materiality
  • Environmental and social impact
  • Channels of action: primary and secondary market, direct investments, real estate, politics
  • Tools of action: positive and negative screening, ESG integration, proxy voting and engagement, green bonds and loans, political influence
  • Regulatory frameworks and initiatives
Extracurriculare Activities comprise of various activities that are not linked to the Curriculum of the MSc in Banking and Financial Management, which are optional and further support the studying of the Master programme.