Course Catalogue Master Innovative Finance

This course introduces students to the fundamental principles and techniques of financial statement analysis. It aims to equip students with the skills necessary to interpret and analyse financial statements, understand the financial health of a company, and make informed financial decisions. The course covers key topics such as financial reporting standards, income statements, balance sheets, cash flow statements, and financial ratios.

Key topics covered in this course include:

• Introduction to financial statements
• Financial reporting standards
• Income statement
• Balance sheet
• Cash flow statement
• Financial ratios
• Interpretation and analysis

In this module, students will write a thesis on a finance topic of their choice. The module involves identifying and analysing a research problem, structuring it into research questions, selecting and applying appropriate research methods, and writing a comprehensive thesis. Students will also discuss their progress with experts and faculty members and defend their thesis in an oral examination. Both tracks provide research-based knowledge but differ in their focus. The research track emphases the methodology pillar, while the industry track puts more weight on the expert knowledge pillar and the development of business-related personality skills.

The number of characters should be between 90.000-180.000 characters. That relates to the contents in the text section of the work. It does not include the abstract or any lists, illustrations, tables, appendices, etc. These numbers are meant to be benchmark values; deviating from these values is possible after confirming with the supervisor

Key topics covered in this course include: • Identifying a research problem

• Structuring research questions
• Applying research methods
• Writing the thesis
• Discussing progress with experts
• Defending the thesis

• This course introduces students to the essential principles and practices of project management, with a particular focus on the finance sector. Students will learn to manage projects in both business and academic settings, devel-oping skills necessary for successful project completion. The interactive nature of the class will involve case studies, group work, and real-world project simulations to enhance learning.Key topics covered are:Project fundamentalsFinancial project managementInteractive learningCase studiesGroup workReal-world simulationsProject planningRisk managementPerformance measurement

This course is designed to guide MSc in Finance students through the essential steps and processes of creating and developing a start-up in the finance sector, with a special focus on fintech start-ups. Students will learn about idea generation, business model development, financial planning, and market analysis. The course is highly interactive and practical, involving workshops, case studies, and hands-on projects to help students transform their ideas into viable business plans.

Key topics covered in this course include:

• Idea generation
• Business model development
• Financial planning
• Market analysis
• Fintech start-ups
• Workshops
• Case studies
• Hands-on projects

The course is designed to provide students with hands-on experience in managing a wealth management firm through a comprehensive investment management game simulation. The course focuses on strategic and tactical asset allocation, implementing strategies based on financial outlooks and forecasts, evaluating performance (performance attribution), and adapting strategies in the context of existing but uncertain information. Developed through an Erasmus+ project by the University of Liechtenstein and the Free University of Bolzano in collaboration with Cesim, this simulation provides a competitive and realistic environment for students to apply theoretical knowledge to practical scenarios.

Key topics covered in this course include:

• Strategic and Tactical Asset Allocation
• Financial Forecasting and Outlook
• Performance Evaluation and Attribution
• Investment Decision-Making under Uncertainty
• Wealth Management Firm Operations

• 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

• In this module, students will write a thesis on a finance topic of their choice. The module involves identifying and analysing a research problem, structuring it into research questions, selecting and applying appropriate research methods, and writing a comprehensive thesis. Students will also discuss their progress with experts and faculty members and defend their thesis in an oral examination. Both tracks provide research-based knowledge but differ in their focus. The research track emphases the methodology pillar, while the industry track puts more weight on the expert knowledge pillar and the development of business-related personality skills.

The number of characters should be between 150.000-300.000 characters. That relates to the contents in the text section of the work. It does not include the abstract or any lists, illustrations, tables, appendices, etc. These numbers are meant to be benchmark values; deviating from these values is possible after confirming with the supervisor

Key topics covered in this course include:

• Identifying a research problem
• Structuring research questions
• Applying research methods
• Writing the thesis
• Discussing progress with experts
• Defending the thesis

Pension Finance provides an introduction to concepts and methods around saving for and spending in retirement.

The course covers the following topics:

• 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
• Sustainable spending at retirement
• The pension system in Liechtenstein and Switzerland

This course introduces the principles and practices of academic writing in the context of finance research. Students learn to formulate precise research questions, conduct structured literature reviews, and produce a short research paper on a topic drawn from the faculty’s research areas. AI is treated as a declared methodological tool: students learn when and how to use AI for source discovery, comprehension, and writing support — and where its limits lie. Critical thinking runs through the course as a continuous thread, from source criticism to argument construction.

Key topics covered are:

• Principles of academic writing in finance research
• Developing precise research questions
• Literature reviews with AI-assisted source discovery
• Citation standards and source criticism
• Structuring a short research paper
• AI as a declared methodological tool
• Critical thinking and argument construction

The Advanced Investment Strategies course is designed to provide students with in-depth knowledge and practical
skills in advanced portfolio management and investment strategies. The course is structured into two main parts. The first half focuses on advanced portfolio management and optimization techniques such as improved estimates, Black-Litterman and equal risk contribution portfolios. The second half involves student groups presenting and implementing (in R) advanced investment strategies from selected papers, covering various techniques (e.g. statistical arbitrage) and datasets (such as bonds, exchange rates, crypto currencies or derivatives).

Key topics covered in this course include:

• Advanced Portfolio Management Techniques
• Optimization Methods: Improved Estimates, Black-Litterman, Equal Risk Contribution
• Statistical Arbitrage
• Investment Strategies for Bonds
• Exchange Rate Strategies
• Cryptocurrency Strategies
• Derivative-based Strategies
• Implementation of Investment Strategies using R

The course "AI and Machine Learning in Finance" introduces students to the exciting world of artificial intelligence and machine learning, specifically focusing on their application in investment strategies. Building on basic concepts learned in previous courses, this course explores essential methods of supervised and unsupervised learning, teaching students to analyse large datasets for innovative financial insights. The course includes practical R -codes to help students set up their own ML-driven investment strategies.

Key topics covered in this course include:

• Introduction to linear model selection and regularization
• Resampling methods and model assessment
• Basics of tree-based methods
• Fundamentals of neural networks and deep learning
• Essentials of unsupervised learning
• Applying machine learning techniques to factor investing

This course provides a comprehensive understanding of business ethics, focusing on theory, principles, and worldviews. It explores the role of ethical leadership, corporate ethics, and professional conduct standards. The course also covers important industry codes of ethics and standards of professional conduct. Through interactive lectures and case discussions, students will gain insights into the ethical challenges faced by finance professionals. Key topics covered are:

• Theory, principles, and world-views on ethics
• The ethical leader: self-mastery and ethics, mind-sets
• Corporate ethics: shared values, professionalism
• Industry codes of ethics and professional conduct
• Ethical dilemmas
• Professionalism in finance
• Standards of professional conduct
• Learning Outcomes

This course is designed for MSc in Finance students in their third semester to develop a research exposé as afoundation for their master thesis, which they will write in the following semester. Students will engage in developing research questions and hypotheses, designing qualitative and quantitative research, and writing and communicating research proposals.

Key topics covered in this course include:

• Developing research questions and hypotheses
• Designing qualitative and quantitative research
• Writing research proposals
• Communicating research proposals
• Conducting structured literature reviews
• Identifying research gaps

• This course is designed to foster critical thinking skills among first-semester Master in Finance students. The mod-ule consists of two input sessions, where foundational concepts and techniques of critical thinking are introduced, followed by four discussion sessions. In these discussion sessions, students will analyse and debate current topics with a strong emphasis on applying critical thinking principles to evaluate arguments and evidence.Key topics covered are:Introduction to critical thinkingAnalytical techniquesLogical reasoningBias identificationArgument evaluationCurrent topic discussionsInteractive debatesEvidence-based analysisReflective thinking

Crypto Finance covers an introduction to the blockchain and its applications to crypto markets and portfolio man-agement. The course builds on the basic concepts of the blockchain technology and Bitcoin. The course then ex-tends to the development of Altcoins and their features. The course concludes with the integration of crypto as-sets into portfolio management.
Key topics covered are:
· Blockchain Technologies
· Consensus Mechanisms
· Forks
· Bitcoin
· Altcoins
· Token Sales
· Crypto Portfolio Management

• 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 requirementsDatabase system architectureDiagnosing and handling data quality problemsRelational databases (SQL)Hands-on labs with MySQLConcurrency control techniquesNoSQL databases (e.g., MongoDB)Apache Hadoop (HDFS, MapReduce)

This course develops the communication skills required to convey complex financial and business content persuasively across a range of professional situations. The focus is on high-stakes spoken and written communication: students learn to pitch financial ideas, present to decision-makers, navigate business conversations, and adapt their message to different audiences and contexts. Cultural diversity is treated as one key dimension of audience-awareness in international finance, an integrated feature of professional communication practice. AI tools support preparation, rehearsal, and reflection throughout the course.

Key topics covered are:

• Pitching financial ideas — structure, delivery, and impact
• Presentation skills for finance and business contexts
• Persuasion and argumentation in professional settings
• Business conversation formats: interviews, client meetings, negotiations
• Audience analysis and communication adaptation
• Intercultural dimensions of finance communication
• Language development
• Self-awareness and self-reflection

Financial Economics provides an introduction to concepts that are fundamental to the understanding of financial decision making.

Key topics covered are:

• Introduction to financial economics
• Equilibrium and arbitrage
• Valuation
• State prices
• Risk-neutral probabilities
• Expected utility, risk aversion, mean-variance theory
• Optimal portfolios

Financial Engineering provides an introduction to concepts and methods around financial derivatives and their use in the creation of tailor-made payoffs according to client needs. The course covers the following topics:

• Derivatives Markets and Instruments: Forwards, Futures, Options, Swaps
• Pricing of Equity, Fixed Income, and Currency Derivatives
• Hedging Using Derivatives
• Decomposing structured products into their components
• Creating tailor-made payoffs from basic financial instruments

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

Integration and globalization of the modern world require a deep understanding of international financial markets. The course equips students with both analytical and practical skills to comprehend macroeconomic and financial trends in open economies. By combining theory, real-world case studies, and practical tools, we empower students to make informed international financial decisions and effectively manage risks in the competitive global landscape.

Key topics covered in this course include:

• National Income Accounting and the Balance of Payments
• Exchange Rates and the Foreign Exchange Market
• Money, Interest Rates, and Exchange Rates
• Price Levels and the Exchange Rate in the Long Run
• Output and the Exchange Rate in the Short Run
• International Monetary Systems
• Financial Globalization: Opportunity and Crisis
• Theories of Optimum Currency Areas
• Crisis and Growth in Developing Countries

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 download 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