Past & Ongoing Thesis Supervision

University of Liechtenstein

PhD Theses

• In Progress:

  • Parameter Uncertainty and Portfolio Management (PhD, Lukas Salcher )
  • Machine Learning in Bank Treasury (PhD, Michael Nigsch)
  • Innovative AI Models for Advanced Risk Management in Financial Institutions (PhD, Simon Kühne)

• 2025:

  • Machine Learning in Financial Economics: An Investment Perspective (PhD, Merlin Bartel, Summa Cum Laude)

MSc Theses

• In Progress:

• 2025:

  • Optimizing S&P 500-Tilted Portfolios with Direct Weight Prediction Neural Networks: Information Ratio Performance under Realistic Constraints and Network Depth (MSc, Andreas Pischetsrieder)
  • Leveraging neural networks for direct portfolio weight optimization: A cryptocurrency-focused approach to practical asset allocation constraints (MSc, Eric Schumann)
  • Portfolio Construction with Intertemporal Capital Asset Pricing Model (MSc, Mykola Subtelnyi)

• 2024:

  • Explaining Factor Momentum: The Impact of Shared Stocks in Long and Short Factor Portfolios (MSc, Oliver Nägele, best Master in Finance 2024)
  • Modern Portfolio Optimization: Clustering, Machine Learning, and Higher-Order Moments (MSc, Matěj Ingršt)
  • The Effects of Geopolitical Conflicts on Financial Markets - Examining the Effect on the Performance of US Defense Stocks (MSc, Pascal Herrmann)

• 2023:

  • Parameter Uncertainty and Equity Premium Prediction via Machine Learning Techniques (MSc, Moritz Graf)
  • Factor Momentum Performance in Multivariate Characteristic Based Portfolios (MSc, Paul Burkart)
  • Measurement and Comparability of Impact Investing in Asset Management (MSc, Susanne Schneider)
  • Predicting stock returns in the presence of breaks by following the approach from Smith and Timmermann (2021): Evidence from the European market (MSc)

• 2022:

  • The Risk Premium of Critical Raw Materials - A Signal for Priority Needs in Realising the European Green Deal (MSc, Caroline White)
  • Empirical asset pricing via Machine Learning: Evidence from the Cryptocurrency Market (MSc, Stefan Macanovic)
  • Inflation-Hedged Portfolios within the European Stock Market (MSc, Fabian Köffel)
  • Robust Portfolio Optimization with Deep learning - Using past Forecast Errors to Improve Return Predictions (MSc, Markus Wabnig, best Master in Finance 2022)
  • The EUR/CHF Exchange Rate and Euro Area Stress (MSc, Nicolas Tschütscher, best Master Thesis in Finance 2022)
  • Incorporating ESG Score Changes in Portfolio Management via Deep Learning (MSc, Lukas Müller)
  • Neural Network for KPI based Time Series Sales Forecasting (MSc, Niklas Leibinger)
  • Optimal Portfolio Building using Deep Learning Techniques (MSc, Michael Metz)
  • Economic Uncertainty Premia in U.S. Stock Markets during the COVID-19 Pandemic (MSc, Leo Pitscheneder)

• 2021:

  • Multivariate Factor Forecasting and Smart Beta Investments (MSc, Dominik Brändle)
  • Evaluating Dollar-Cost Averaging under the Aumann-Serrano Framework (MSc, Jonas Sterk)
  • Impact of ESG exclusion on firms’ cost of capital (MSc, Fabian Müller)
  • Sales Forecasting with Machine Learning (MSc, Johannes Gassner)
  • Investors’ herding in the German equity options market: Evidence from the COVID-19 crisis (MSc, Dmytro Livshyts)

• 2020:

  • Momentum meets Uncertainty (MSc, Dominik Kaiser, best Master Thesis in Finance 2020)
  • Changes in Investor Attention and the Cross-Section of Stock Returns: Evidence from Thomson Reuters and Google Trends (MSc, Emanuel Broger)

• 2019:

  • Cross-Sectional Volatility and the Prediction of Factor Premia (MSc, Artur Maibach, Runner-Up Finance Award)
  • Multi-Factor Timing (MSc, Bruno Jäger, Winner of Finance Award, best Master Thesis in Business Science)
  • Stock Age as Proxy for Uncertainty of Parameters (MSc, Sven Sturzenegger)
  • The Influence of News Coverage on Stock Returns – Evidence from European Markets (MSc, Alexander Person)

• 2018:

  • Liquid betting against beta revisited: Evidence from all over the world (MSc, Lukas Salcher)
  • From IPO to Obsolete: Stock Age related investment strategies (MSc, P. Thoma)
  • Eurex index dividend futures hedging (MSc, Adrian Spiegel)
  • Predicting Equity Bear and Bull Markets: International Evidence (MSc, Luca Liepert)

• 2017:

  • Liquidity and the Polish Stock Market: Empirical Tests of Asset Pricing Models and Inclusion of Liquidity Factors (MSc, Pavol Ruzicka)
  • Dynamic Asset Allocation Strategies and An Optimisation Framework: How Optimal is Optimised (MSc, Frank Balz)
  • Prediction of the Monthly Sovereign Yield Spread Changes in EMU Countries from 2000 to 2016 using the Illiquidity Measure ‘Noise’ in Bond Prices (MSc, Philippe Heise)

• 2016:

  • Terrorism and its effect on financial markets (MSc, Stefanie Geiger)
  • Betting Against Beta (MSc, Claudio Lamprecht)
  • Cross-Sectional and Option-Implied (Higher) Moments and the Predictability of Historical Volatility: US Study (MSc, Ognjen Vukovic)

• 2015:

  • The Relationship between Commodities and the Stock Market - Empirical Evidence for the Eurozone (MSc, P. Kain)
  • The Effectiveness of Constant and Time-Varying Futures Optimal Hedge Ratios - Empirical Evidence from the European Stock Market (MSc, E. Panagakou)

BSc Theses

• 2024:
  • Auswirkungen von Indexänderungen auf Einzelaktien (BSc, Kenny Seifert, Runner-Up best Bachelor Thesis in Financial Services 2024)
• 2023:
  • Performancevergleich und -entwicklung von aktiv und passiv gemanagten Schweizer Aktienfonds im Zeitraum von 1989 bis 2022 (BSc, Jessica Albrecht)
• 2022:
  • Sub-portfolio Optimization (BSc, Jasminko Kulenovic)
  • Stock Market Prediction With Long Short-Term Memory Recurrent Neural Networks (BSc, Elizabet Sanyal)
• 2021:
  • Cryptocurrency: Delisting Bias in the coinmarketcap database (BSc, Fabian Köffel)
• 2020:
  • “Long/Short” Momentum-Strategie am Kryptowährungsmarkt (BSc, Timothy Rist, best Bachelor Thesis in Business Administration 2020)
• 2019:
  • Portfolio Optimization in a Cryptocurrency Environment: An Omega Optimization (BSc, Dominik Brändle, Runner-Up Finance Award)
• 2018:
  • The North Korea threat and its effect on global stock markets: The case of South Korea, Japan and the USA (BSc, Markus Wabnig)
• 2017:
  • Using momentum to improve low-volatility strategies: Evidence from the US stock market (BSc, M. Amann)
• 2016:
  • Prognose von Aktienrenditen - Eine empirische Forschung über die Vorhersagbarkeit der zukünftigen Renditen des Swiss Performance Index anhand von Renditen-Dispersionen (BSc, Bruno Jäger)

Other Theses

• 2020:
  • Künstliche Intelligenz und Anwendungsmöglichkeiten in der Vermögensberatung (MBA, Lukas Schäper)
  • Stock Price Prediction for Portfolio Management Using Recurrent Neural Networks and Machine Learning (EMBA, Jensen Chang)