• 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)

Interactive lecture

After successful completion of this module, students

• are familiar with the basic economic and legal principles in national and international taxation and can apply them;
• understand the impact of international taxation in an Integrated and Globalised world on politics, consumers and investors;
• have at their disposal comprehensive knowledge of national, international and European taxation regarding the development, investment and succession of assets and also with regard to asset investments and structures;
• understand the institutional aspects of international tax standards and the respective multinational institutions, like the BEPS-Inclusive Framework and the Global Forum on Exchange of Tax Information;
• are familiar with tax planning schemes used by multinational entities.

see lecture(s) within the module

• Introduction to International Tax Policy and Tax Standards
• Economic and Legal Principles of National and International Taxation
• International Taxation in an Integrated and globalised World
• International and European Tax Framework
• International Tax Competition and Tax Cooperation
• International Tax Compliance and Tax Law
• OECD-Tax Agenda: New World Tax Order (Pillar one and two)
• EU-Tax Agenda: Business Taxation for the 21st Century
• International re-allocation of taxing rights: Determination of connecting factors
• Global tax initiatives and actions: OECD, EU, US, UK, RCEP, IMF, UN, and NGOs.

Interactive Lecture

Students

• are familiar with the basic economic and legal principles in national and international taxation and can apply them,
• understand the implications of both European and International Tax principles,
• understand the impact of international taxation in an Integrated and globalised world on politics, consumers and investors,
• understand the implications of international tax competition and international tax cooperation in financial markets and global business,
• are familiar with ongoing initiatives on taxing digital companies (determining taxing rights) and global minimum taxation,
• acknowledge the impact that ongoing international tax initiatives have on individuals and legal entities,
• understand the implications of international tax compliance and international tax law in financial markets and global business,
• are familiar with current reforms on international tax environment.

• De Mooij, R., Klemm, A., & Perry, V. (2021). Corporate Income Taxes Under Pressure: Why reform is needed and how could it be designed. IMF
• Devereux, M., Auerbach, A., Keen, M., Oosterhuis, P., Schön, W., & Vella, J. (2021). Taxing profits in a global economy. Oxford.
• Haslehner, W., & Lamensch, M. (2021). Taxation and Value Creation. EATLP International Tax Series
• Oats, L., & Mulligan, E. (2019). Principles of international taxation (Seventh edition). London: Bloomsbury Professional.
• Perdelwitz, A., & Turina, A. (2021). Global Minimum Taxation? An Analysis of the Global Anti-Base Erosion Initiative. IBFD
• Pistone, P., Roeleveld, J. J., Hattingh, J., Nogueira, J. F. P., & West, C. (Eds.) (2019). Fundamentals of taxation: An introduction to tax policy, tax law, and tax administration. Amsterdam: IBFD.

• 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

Interactive lecture with input statements from local banks, trustees and asset managers

After successful completion of this module, students

• are familiar with tax features of several selected jurisdictions regarding individuals and legal entities;
• are familiar with the characteristics of the various legal entities and financial instruments as well as the national and international links to taxation of asset investments and structures;
• understand the legal basis of asset investments and structures, including tax directives and tax agreements;
• are familiar with practical implications of onboarding private clients with wealth structures
• are familiar with the most common wealth structures used on international tax planning for private clients

see course(s) within the module

• 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

Interactive lecture

After successful completion of this module, students

• are familiar with the basic economic and legal principles in international private wealth management and can apply them;
• are familiar with the characteristics of the various legal entities and financial instruments as well as the national and international links to taxation of asset investments and structures;
• understand the goals connected with international and intertemporal tax management for cross-border asset investments and structures for natural persons as well as the particular instruments employed for achieving goals within the framework of tax management;
• understand the framework of structuring wealth management;
• understand the implications of onboarding private clients;
• are familiar with tax planning matters considering various wealth structures.

see course(s) within the module

• 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.

• Lectures are interactive “labs” devoted to hands-on programming.
• Moodle is used throughout the course to disseminate course material and for information and discussion.

After successful completion of the course:

• Students understand the practical problems when applying statistical methods to real world financial data.
• Students are familiar with the necessary tools to tackle real-world problems based on large (and possibly unstructured) datasets.
• Students can apply the relevant methods to solve real-world problems with the tools available to them.
• Students are able to effectively communicate the results from their projects to a wider audience.

see lecture(s) within the module

Data and Application Security provides an introduction to cyber security and covers topics related to information and communication security. This is one of the core subject areas of the degree programme, and the course provides a foundation for choosing further electives in the area of cybersecurity. The course covers the following topics:

• Security goals and design principles
• Economic aspects of security and risk analysis
• Basics of cryptography
• Authentication and access control
• Key instruments of network security
• Key instruments of web security
• Software security, vulnerabilities, and attacks
• Email and mobile device security

• The module involves interactive lectures to integrate theoretical knowledge with practical design and analysis skills.
• The module involves practical exercises in which students investigate security problems and find appropriate countermeasures.
• Lab exercises are used to support the acquisition of practical skills.
• Theoretical material is demonstrated with relevant practical tools.

After successful completion of the course, students will

Professional competence
• understand the main security objectives and design principles
• understand basic theoretical concepts in the above mentioned security fields
• understand elementary attacks against security instruments
• be able to find solutions for basic security vulnerabilities

Methodological competence
• be able to administer basic security instruments
• be able to implement simple programs related to the security instruments

Social competence
• be able to organise learning materials and work in groups
• be able to divide problems into meaningfully tasks, work on them and help each other within the group

Personal competence
• be able to address new challenges and independently identify viable solutions
• be able to think “out of the box” and apply knowledge in an unusual context

Technological competence
• be familiar with programming in Python and its security related libraries
• be familiar with remote access tools such as VPN and SSH

• Students are provided with the lecture slides and supplementary material (e.g., selected journal articles).

Exercise: Assignments
Lecture: Written exam

• 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

• Lectures are interactive
• Moodle is used throughout the course to disseminate course material and for information and discussion.

After successful completion of the course:

• Students understand and can explain the concepts of supervised and unsupervised learning.
• Students are familiar with a variety of topics in finance where machine learning methods can be successfully applied.
• Students are able to apply the most important concepts covered in the course to real datasets in R, making use of powerful online platforms.
• Students are able to effectively communicate about machine learning and artificial intelligence in finance.
• Students are able to critically evaluate situations where machine learning could successfully be applied.

see lecture(s) within the module

• Smart Contracts
• Token Valuation
• Crypto Exchanges
• Tokenization of services and other goods
• Trade Finance with Blockchain
• InsurTech, PropTech and Social Trading

Interactive seminar with guest lecturers.

After successful completion of the course, students…

• Know what smart contracts are and have a basic knowledge of how to code a simple ERC20 Token
• Understand the methods of token valuation and can apply it to simple examples
• Understand how crypto exchanges work and can evaluate them in terms of business model and risk
• Can describe what alternative types of assets can be tokenized and how this is done
• Have basic knowledge of the changes happening in trade finance with respect to Blockchain application
• Know the status quo of developments in the fields of InsurTech, PropTech and Social Trading

Selected chapters from books and current research articles will be provided.

• Blockchain Technologies
• Bitcoin and Altcoins
• Tokenization of assets
• Crypto Wealth Manangement
• Crowdfunding
• Robo Advisory

Interactive seminar with guest lecturers.

After successful completion of the course, students…

• know the basic functions of a Blockchain and can explain the most common consensus mechanisms
• can distinguish between different types of cryptocurrencies and can explain their respective field of application
• understand the principles of tokenization and the important factors in token offerings
• know how crypto assets can be integrated within a portfolio
• can distinguish between different types of crowdsourcing and know when to use which
• understand the basic principles of robo advisory

Selected chapters from books and current research articles will be provided.

• Derivatives Markets and Instruments: Forwards, Futures, Options, Swaps
• Pricing of Equity, Fixed Income, and Currency Derivatives
• Hedging Using Derivatives
• Financial Engineering

Lecture

Students …

• know how derivatives and derivatives markets work,
• apply standard models to price financial derivatives,
• use Greek variables in risk management and financial engineering,
• devise and/or analyze derivatives strategies for speculation, hedging and arbitrage,
• combine basic instruments to achieve desired payoff structures/decompose payoff structures into their basic components.

Hull, J.C. (2018). Options, Futures, and Other Derivatives. Pearson
Jorion, P. (2009) Financial Risk Manager Handbook. Wiley

See lecture(s) within the module