The Penal Code (StGB) of the Principality of Liechtenstein, enacted on 24 June 1987, will celebrate its 40th anniversary on 24 June 2027. On this occasion, the Chair of Economic Criminal Law, Compliance, and Digitalization is organizing a commemorative event, the highlight of which will be the presentation of a scholarly commentary on the Liechtenstein StGB. This will be the first comprehensive scientific work on this code in Liechtenstein, providing a central reference both for legal practice and for academic discourse. Due to the limited sources available in Liechtenstein, the commentary is conducted with reference to Austrian literature and case law. Particular attention, however, is given to Liechtenstein-specific features in order to reinforce the national criminal law identity. The commentary systematically covers all sections of the StGB (as well as the Criminal Law Adaptation Act [StRAG] of 20 May 1987) and will be published both as a book and online via the Austrian legal database RDB. The project represents an independent scholarly contribution to the development of criminal law. To date, no scientific commentary on a code exists in Liechtenstein, and criminal law research has been conducted only to a very limited extent compared with other areas of law. The commentary thus assumes a pioneering role.

Introduction and Background
The Federal Reserve (Fed) significantly influences U.S. economic policy. Fed speeches, pivotal for market participants, provide insights into monetary policy, economic forecasts, and regulations. Over time, the Fed has shifted towards transparent communication to enhance policy effectiveness. Analyzing these speeches with machine learning (ML) and natural language processing (NLP) offers valuable insights into their impact on financial stability.
Research Objectives
This research aims to automate Fed speech classification and measure their effects on financial markets. Key questions include:
1. How can ML classify Fed speeches by relevance to financial stability?
2. What are the short-term and long-term market impacts of these speeches?
3. How can we distinguish between anticipated and actual speech effects?
Methodology Data Collection and Preparation
A comprehensive database of Fed speeches will be created, preprocessed, and enriched with metadata like date, speaker, and context.
Text Classification Using Machine Learning
Various ML and NLP techniques, including Bag-of-Words, TF-IDF, Word Embeddings, and Transformer Models (e.g., BERT, GPT-3), will classify the speeches.
Model Training and Evaluation
Using annotated data, models such as SVMs, random forests, and neural networks will be trained and evaluated with accuracy, precision, recall, and F1-score metrics.
Analysis of Impact on Financial Markets
The classified speeches' impact on stock prices, bond yields, and exchange rates will be analyzed using statistical and econometric methods, including event studies and time-series analysis. The goal is to separate anticipated market effects from actual impacts post-speech.
Relevant Literature and Theoretical Foundations
This research builds on the work of Jurafsky and Martin (2020) on NLP, Goldberg (2017) on neural networks, Blinder et al. (2008) on central bank communication, and Hansen and McMahon (2016) on transparency.
Expected Outcomes and Implications
The classification method is expected to differentiate speeches by their relevance to financial stability reliably. Analyzing speech impacts will provide insights into monetary policy communication. This research could enhance Fed communication strategies and contribute to financial stability. It will offer a real-time tool for market participants and policymakers, aiding in risk management and decision-making.
Conclusion
This interdisciplinary approach combines ML with economic analysis to study Fed speeches' impact on financial markets. The anticipated outcomes will advance research and practical applications in financial economics, enhancing central bank communication strategies.

The proposed dissertation is centered on the following overarching research question: To what extent do cryptocurrency prices respond to risks associated with their underlying technological design?

The first paper of this dissertation explores non-standard financial risks that arise in cryptocurrencies due to their technological design by examining how governance disputes are resolved through hard forking in decentralized blockchains. Specifically, the paper provides insights on the immediate effects on returns, trading volume, and volatility of the main blockchain.

The second paper examines spillover risks between the parent blockchain and successful hard forks over an extended time horizon following these governance disputes. In particular, this paper examines whether governance disputes resolved through technological divergence are sufficient to separate networks or whether they continue to influence each other`s price formation.

The third paper examines the market reactions stemming from the two main mechanisms used to implement planned and coordinated protocol upgrades. More precisely, it focuses on protocol upgrades implemented through hard and soft forks. Finally, the fourth paper examines speculative behavior as a central source of risk, given that many cryptocurrencies are not backed by tangible assets due to their technical design. Concretely, this paper proposes a forward-looking sentiment gauge.

Overall, this proposed dissertation provides insights into crypto-specific risks with implications for improving risk management practices, trading strategies, and policy recommendations.