Populism and Financial Markets: Forward-Looking Insights from Option-Implied Measures

November 2025 until October 2026 FFF-Funding Project

Project Description

This project investigates how populism influences financial markets by leveraging option-implied information and improved measures of political risk. Populist movements have re-shaped the political landscape across Europe and increasingly affect investor behaviour, risk perceptions, and asset pricing. Traditional studies focus mainly on volatility around elections and populist outcomes: volatility typically rises around electoral events (Kelly, Pástor, & Veronesi, 2016), while populist victories themselves shift market perceptions of risk (Stöckl & Rode, 2021; Hartwell, 2021). Yet such approaches capture only part of the market response.
We extend this analysis by incorporating forward-looking measures such as option-implied expected returns and higher-order risk-neutral moments, which provide a richer picture of tail risks, asymmetries, and extreme events. In addition, we apply a machine learning-based populism index that distinguishes between ideational and rhetorical dimensions. Together, these innovations provide a more comprehensive framework for measuring political risk, advancing academic research while offering practical insights for investors and policymakers.

Relevance to Liechtenstein

Liechtenstein is an internationally significant financial center with a strong presence of investment funds, banks, and institutional investors actively operating in global markets. This project is particularly relevant for Liechtenstein as it provides new insights into how populist developments affect financial markets, supporting local investors, fund managers, and policymakers. By leveraging option-implied information and a machine learning-based populism index, risks can be identified in advance, enabling informed decision-making. Furthermore, the project methodology can be integrated into courses and seminars at the University of Liechtenstein, giving students hands-on experience with modern approaches to political risk assessment.

Keywords

Financial management Risk management Innovation Society Financial Markets

Participating Institutions

Financial Economics / Project Lead

Project Participants

Assoz. Prof. Dr. Sebastian Stöckl

- Principal Investigator

Associate Professor - Financial Economics Academic Director - Liechtenstein Undergraduate and Graduate School Official Representative AACSB - University of Liechtenstein

Principal Investigator

Prof. Dr. Michael Hanke

- Professor

Professor - Finance Dean - Liechtenstein Business School

Professor

Momentum Horizons

November 2025 until August 2026 FFF-Funding Project

Project Description

Momentum is one of the most pervasive anomalies in financial markets, yet its underlying mechanisms remain debated. Building on recent advances in the literature, this project seeks to disentangle the relative contributions of underreaction and overreaction across different momentum formation horizons. By incorporating technical trading indicators as proxies for shortterm overreaction and by constructing orthogonalized momentum measures, the project aims to provide a new framework for understanding why momentum persists as an enduring return pattern. Expected outcomes include academic publications as well as replicable tools for momentum decomposition.

Relevance to Liechtenstein

The project is highly relevant for Liechtenstein as a financial centre with a strong asset management industry. Momentum strategies are widely used by investment funds, but their profitability is sensitive to market conditions and investor behaviour. By clarifying the drivers of momentum and developing robust indicators, this project contributes to the design of more reliable trading strategies and portfolio management tools. The availability of a replicable decomposition framework also strengthens the connection between academic research and practice in Liechtenstein, providing local institutions with innovative analytical methods.

Keywords

Financial Markets Finance

Participating Institutions

Finance / Project Lead

Project Participants

Dr. rer. oec. Merlin Bartel

- Principal Investigator

Postdoc - Finance

Principal Investigator

Macro Scope: Factor Selection in Dynamic Term Structure Models via Bayesian Methods

November 2025 until October 2026 FFF-Funding Project

Project Description

The proposed project will develop new techniques to improve the forecasting of bond returns and yield curve dynamics by systematically identifying the most relevant macroeconomic factors that affect interest rates. Traditional dynamic term structure models (DTSMs) often assume that the information in the current yield curve fully captures all drivers of future interest rates. However, recent research reveals that various macroeconomic variables, such as measures of inflation and real activity, can possess additional predictive power beyond what yields alone reflect. At the same time, other studies show that including too many factors can lead to unstable forecasts and poor investment outcomes out-of-sample. This project addresses these conflicting findings by developing a Bayesian learning framework that dynamically selects the most important factors for forecasting in real time, while ignoring redundant predictors.
Using modern Bayesian methods, particularly Sequential Monte Carlo and stochastic variable search algorithms, we will let the data inform which macroeconomic variables truly matter for predicting excess bond returns. Our approach updates investors' beliefs as new data arrive, avoiding the pitfalls of overfitting and accounting for model uncertainty in a principled way. We will evaluate whether this data driven macro factor selection leads to improved predictive accuracy and higher economic value for bond investors. By possibly reducing vast macro universe to a few key predictive factors, this project has the potential to enhance both the understanding of what drives bond risk premia and the practical management of interest rate risk.

Relevance to Liechtenstein

The project creates value for Liechtenstein by strengthening both the research environment of the University of Liechtenstein's Financial Economics focus area and the analytical capabilities of the country's financial industry. The project is embedded in an active academic setting where research activities, discussions, and regular scholarly exchanges allow results to be shared with colleagues and practitioners based in Liechtenstein. This ensures that insights generated by the project contribute to the broader academic dialogue and support evidence-based decision-making in the local financial ecosystem. For Liechtenstein's internationally oriented financial institutions, the project offers concrete practical benefits. By identifying a small set of macroeconomic factors that are most relevant for predicting yield curve dynamics and bond risk premia, the outcomes support more robust forecasting and improved management of interest rate risk. The statistical methodology developed in the project reduces model uncertainty, enabling banks, asset managers, insurers, and pension providers in Liechtenstein to strengthen their fixed-income analytics and portfolio decisions. The project also contributes to knowledge transfer and capacity building within Liechtenstein. Its findings will be incorporated into courses and research-related activities at the University of Liechtenstein, ensuring that students and future professionals in the financial sector are trained in state-of-the-art empirical methods.

Keywords

Financial management Innovation Macroeconomics Technology and Innovation Management Financial Instruments

Participating Institutions

Financial Economics / Project Lead

Project Participants

Dr. Tomasz Dubiel-Teleszynski

- Principal Investigator

Postdoc - Financial Economics

Principal Investigator

Extreme Analyst Forecasts

November 2025 until October 2026 FFF-Funding Project

Project Description

This project at the University of Liechtenstein's Chair of Sustainable Finance and Investments examines the predictive power of extreme analyst target prices for subsequent factor adjusted stock returns. We focus on the ratio of consensus target price to current price (TP/P) and deliberately study the tails of the distribution-exceptionally high or low TP/P values. Such extremes may arise from market (over )reactions, firm specific events, or behavioral biases.
We will systematically identify extremes, characterize their drivers, and evaluate their forecasting ability relative to middle range observations. The methodology combines portfolio sorts, empirical asset pricing regressions, and flexible non linear estimators. Findings will inform investors, asset managers, and regulators about the reliability of analyst signals and assess whether extreme targets can underpin robust, transaction cost aware investment rules.

Relevance to Liechtenstein

For Liechtenstein's private banking and asset management ecosystem, assessing the reliability of analyst signals is highly practical. The project clarifies whether, and under which conditions, extreme target prices provide actionable input for mandates and products-accounting for realistic trading frictions and risk constraints. It supports improvements in rule based investment processes, advisory use of research signals, and risk management (e.g., mitigating exuberance during stress periods). It also enhances evidence based education at the University of Liechtenstein and fosters knowledge transfer with local market participants.

Keywords

Sustainable Investments Sustainable finance Finance Sustainability

Participating Institutions

Sustainable Finance and Investments / Project Lead

Project Participants

Dr. Othar Kordsachia

- Principal Investigator

Representative of the professorship - Sustainable Finance and Investments

Principal Investigator

Privacy-aware Scheduling

November 2025 until December 2026 FFF-Funding Project

Project Description

Privacy is an important human right that is constantly challenged by modern information technology. Several regulatory frameworks, such as the GDRP from the European Union, compel organizations to protect the privacy of individuals. Many organizations have protocols and technologies in place to protect obviously sensitive data such as medical records. However, some business activities indirectly reveal private information, e.g., a typical example from everyday life is that many people might suspect that a woman might is pregnant if she is not drinking alcohol at a party. Understanding how organizations indirectly reveal private information is for upmost importance and should be further investigated.
In previous work, I was shown that private information can be revealed from published schedules as they are commonly used for example in healthcare settings. One common goal of privacy research is to quantify how much information is leaked in a specific setup. Previous work by Fahrenkrog-Petersen et al. quantified privacy loss for schedules under the assumption that a public schedule is optimal and correct. However, small variations of the schedule made it impossible to quantify the privacy loss, since the inference attack was based in inverse optimization and required an optimal schedule. Therefore, making a potential anonymization of the data trivial.
In this project, we aim to further improve the quantification of privacy losses of published schedules. For this purpose, we want to develop more realistic inference attacks that allow for a more accurate calculation of the privacy loss. Such results can be used to further develop novel privacy protection techniques and to generalize the existing privacy loss to a wider range of scheduling problems.

Relevance to Liechtenstein

The research project holds relevance for Liechtenstein and the Alpine Rhine Valley region, particularly because of the potential of a publication in a highly ranked journal, such a publication would support the strategic positioning of the Liechtenstein as a high-tech region. Further, Liechtenstein has strict privacy laws itself and understanding how modern technology impact the privacy of individuals is of importance for the country. If the project leads to an additional SNSF grant this would further strengthens Liechtenstein`s visibility and positioning as a high-tech region and also strengthen the financial basis of the University of Liechtenstein.

Keywords

Information technology Innovation Digitalization

Participating Institutions

Information Systems and Process Science / Project Lead

Project Participants

Ass.-Prof. Dr. Stephan Fahrenkrog-Petersen

- Principal Investigator

Assistant Professor (with Tenure-Track) - Information Systems and Process Science

Principal Investigator

The DAO and the New Organizational Governance Model for the Digital Age

since September 2022 PhD-Thesis

Project Description

This dissertation explores the potential of Decentralized Autonomous Organizations (DAOs) as an alternative governance model in the digital age. The research aims to identify the opportunities and challenges DAOs present for socio-technical systems, business processes, and governance mechanisms. Using a Design Science Research methodology, artifacts are developed to enhance transparency, accountability, and participation in organizations. A particular focus is on the development of a Community-Driven Carbon Credit model in Brazil, ensuring ecological integrity and social inclusion.

Participating Institutions

Liechtenstein Business School / Doctoral instiution

Project Participants

Pablo Marcelo Coirolo MBA

- PhD-Student

PhD-Student

Prof. Dr. Jan vom Brocke

- Supervisor

Visiting Professor - Information Systems and Process Science

Supervisor

Prof. Dr. Axel Winkelmann

- Co-Supervisor

Co-Supervisor

Assessing Liechtenstein's Building Construction Practices (2014-2024) - Challenges, Gaps and Strategies for Net-Zero 2040

since September 2025 Preproposal PhD-Thesis

Project Description

This research project examines current construction practices in Liechtenstein and their compatibility with the net-zero targets for 2040. The study aims to identify and address the challenges within the construction sector that hinder the country's net-zero targets. It will assess current construction practices in all eleven municipalities and examine how these are influenced by financial frameworks and spatial planning strategies in order to determine their compatibility with the net-zero targets for 2040. Using a mixed-methods approach, the study will assess the effective impact of construction practices and propose appropriate tools for integrating LCA methodologies and circular economy strategies. The study is intended to provide important insights for the implementation of climate protection targets in Liechtenstein.

Participating Institutions

Urban Design and Spatial Development / Doctoral instiution

Project Participants

Vishruti Shastri

- PhD-Student

PhD-Student

Prof. Dipl. Arch. ETH Michael Wagner

- Supervisor

Dean - Liechtenstein School of Architecture Professor - Urban Design and Spatial Development Member of the Rectorate - Rectorate Academic Director MSc AR - Liechtenstein School of Architecture

Supervisor

‹Darf› Ruggell

KTT

February 2025 until September 2025 (Completed)

Ruggell is a small municipality in the Principality of Liechtenstein that, like many other small agricultural communities, originally developed along a village street, with the church and the mayor's ... More

Design Thinking for AI

October 2025 until September 2027 FFF-Funding Project

Project Description

Objectives: What do you want to achieve by implementing the project?
This project aims to provide an innovative, modular teaching programme on “Design Thinking for AI” (DT4AI) for European University educators. The programme empowers educators to deliver student-centred, problem-based courses that cultivate competencies in AI-driven problem solving, rapid prototyping and effective teamwork. By integrating this programme into existing curricula, we address critical gaps in AI education and promote structured approaches to solving organisational challenges.

Implementation: What activities are you going to implement?
With the DT4AI programme, we will develop comprehensive teaching materials—including lecture slides, notes, a handbook on best practices and course integration, and domain-specific case studies across manufacturing, finance and healthcare. Additional activities include creating video-based training modules and an accessible knowledge base. These resources will equip educators to teach DT4AI in a practical, structured way, helping learners to apply DT and AI skills to real-world challenges.

Results: What project results and other outcomes do you expect your project to have?
By providing comprehensive teaching materials, digital modules and domain-specific case studies, this project will empower European educators to integrate "DT4AI" into their curricula. Consequently, students will develop essential skills in problem solving, user research and prototyping. The project fosters academia-industry collaboration, bridges educational gaps and equips institutions with scalable tools to address real-world challenges through structured, innovative approaches.

Participating Institutions

Information Systems and Digital Innovation / Project Lead

Project Participants

Assoz. Prof. Dr. Benjamin van Giffen

- Project Manager

Associate Professor - Information Systems and Digital Innovation

Project Manager

International Tax Framework for Investment, Wealth and Philanthropy Hubs

October 2025 until December 2026 FFF-Funding Project

Project Description

This research project analyses international tax framework for Investment, wealth, and philanthropy hubs, particularly in small, highly developed, high-income jurisdictions. The aim is to identify success factors for competitive tax design in line with international Standards such as OECD-BEPS, the EU Code of Conduct, and GloBE. The project focuses on legal and tax conditions for operating Companies, foundations, trusts, family Offices, and charitable entities. Liechtenstein is studied in international comparison to derive actionable recommendations for strengthening its positioning as a competitive hub.

Relevance to Liechtenstein

Liechtenstein plays a central role in this project, as it shares many characteristics with the international investment, wealth, and philanthropy hubs under examination. As a small and highly developed jurisdiction with a strong financial sector and well-established foundation and trust law, Liechtenstein actively maintains its attractiveness while meeting evolving international tax and transparency expectations. Increasing cross-border mobility of assets and expanded transparency obligations have a direct impact on Liechtenstein and require continuous refinement of its legal and tax framework.
By analysing comparable jurisdictions, the project identifies how states of similar size and economic orientation adapt their tax and legal systems to remain both internationally compliant and economically competitive. This comparative perspective offers Liechtenstein valuable insights into successful models and potential development paths. The project thus supports Liechtenstein's strategic positioning by outlining evidence-based options for shaping investment, wealth, and philanthropic structures in a forward-looking, internationally aligned, and economically attractive way.

Scientific, Economic and Societal Impact

The project provides direct practical value for policymakers, public authorities, advisors, financial intermediaries, family offices, and philanthropic organisations. Primarily, this project analyses different legal forms and the tax treatment of investment and wealth structures, such as foundations, trusts, and charitable entities, in selected jurisdictions. Key areas include the newly established Global Minimum Tax, substance requirements, transparency obligations, tax qualification and attribution of wealth and income, as well as the design of incentives for wealth structuring and philanthropic engagement.
For public authorities, the findings support evidence-based decision-making in legislative and administrative processes. For practitioners, the project offers actionable guidelines for developing wealth and philanthropy solutions that meet increasing international expectations while remaining efficient, flexible, and sustainable. Philanthropic organisations benefit from insights into the tax treatment of cross-border giving and the mechanisms that can facilitate or hinder international charitable activities. Overall, the project contributes to the development of practical, forward-looking solutions tailored to the complexities of the international regulatory and tax environment.

Participating Institutions

Business Management Taxation and Tax Law / Project Lead

Project Participants

Prof. Dr. Martin Wenz

- Project Manager

Professor - Business Management Taxation and Tax Law Academic Director - Liechtenstein Executive School

Project Manager

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