Thesis Topics

General Area of Thesis Supervision:

Empirical Finance, Quantitative Finance, Risk Management, Parameter and Model Uncertainty, Forecasting

Open Topics:

Seminars (e.g. Seminar in finance)

• COVID-19 and impacts on financial markets (moments/cross-sectional moments)
• Ambiguity/Parameter Uncertainty and Real Option Portfolios (Literature Review)
• Predicting Equity Risk Premia via Machine Learning this paper
• Factor Momentum: Does it exist in statistical factors?
• Uncertainty and Google searches Dzielinski, (2012)
• Predicting the equity premium with ambiguity, which measure performs best?
• Predicting stock returns with ambiguity vs measuring the current level of ambiguity from the markets
• Predicting the equity premium with cross-sectional moments using simple ML rather than OLS and auto-encoders rather than principal components.
• Turbulence before and after elections. Is there a relationship to the measure of political uncertainty of Kelly et al. (2016)?
• Uncertainty and Volatility. Conduct a literature review.

Thesis (Mainly master thesis)

• Political Finance:

  • Relationship between Populism and Financial Markets (Data available)
  • The cost of populism (relate to corruption and economic freedom indices)
  • Election Portfolios: Drivers of Betting Quotes and Stock Returns
  • Based on data from this (conditionally accepted) AER paper see how financial markets did perform during this time

• Innovative Finance - Machine Learning in Finance:

  • Multivariate Predictability in Assets and Factors. Apply Machine Learning techniques to exploit linear and nonlinear predictability.
  • Machine Learning in Asset Management. Clustering/Shrinking, Factor Models and the Covariance Matrix
  • Optimal Portfolio Building using Machine Learning Techniques (investigate possible modifications of the loss function)
  • Predicting the Equity Premium with Machine Learning and turbulence measures rather than anomaly returns (see this paper)

• Innovative Finance - Cryptocurrencies:

  • The predictability of cryptocurrency returns and the transfer/reference currency
  • Delisting Bias in aggregate CC returns
  • Machine learning and crypto currencies
  • Efficient portfolio formation for crypto currencies
  • Factor investing in crypto currencies
  • A cryptocurrency turbulence index

• Innovative Finance - Uncertainty (Parameter/Financial/Macro-Financial/Portfolio …):

  • NEW: History-aware ambiguity measures: is there something turbulence can learn from probability uncertainty and vice-versa?
  • NEW: Macro-Financial Uncertainty and the work of Rossi & Sekhposyan, 2017 and Rossi, Sekhposyan & Soupre, 2016. Relate the various indices to each other. What measures what? How can we find out what the best measure of uncertainty is? (see also Bekaert et. al, 2022)
  • The term structure of uncertainty: Uncertainty (Unusualness/Turbulence) in good times, uncertainty in bad times and uncertainty about bad times. Investigate the term structure of uncertainty and its implications on (e.g.) asset prices.
  • Within- and cross-country uncertainty (data available). What drives (international) asset prices?
  • Higher (Co)Moment Uncertainty and Stock Returns: The case of the Covid-19 crash (also relate to coskewness risk)
  • Follow Redl (2018) and link elections to macro/financial uncertainty
  • Closely related: Check how much macroeconomic uncertainty/parameter uncertainty relate to the results of Pflueger, Siriwarda & Sunderam (2018): A Measure of Risk Appetite for the Macroeconomy
  • Some more uncertainty: Replicate this paper using our Macro-Uncertainty Index
  • Financial Turbulence and the Estimation of Tail Risk

• Asset Pricing:

  • What is the optimal number of portfolios to build for long-short factor premia? Is building portfolios the right way? A good starting point is this paper (ONLY FOR TOP STUDENTS)
  • What is the optimal number of partitions (portfolios) for the calculation of cross-sectional predictors (e.g. cross-sectional volatility)? Which cross-section delivers the best predictor of the equity premium? (ONLY FOR TOP STUDENTS, I CAN PROVIDE DATA AND PARTS OF THE CODES). See also Walter et. al, 2022, Non-Standard Errors in Portfolio Sorts
  • All about factor momentum and expected returns: Hanlin Yang (2020): Decomposing Factor Momentum. See also Eshani and Linainmaa (2019): The Inevitability of Momentum and Eshani and Linainmaa (2019): Factor Momentum and the Momentum Factor) Gupta and Kelly (2019): Factor Momentum everywhere
  • Factor Momentum: Factor Uncertainty and Factor Momentum Performance.

• Asset Selection:

  • Robust portfolios: Estimation & Empirics

• Pension Finance

  • Dollar-Cost Averaging vs Lump-Sum Investments in the context of market predictability (market timing) and life-cycle investments.

Theses Supervised:

• In progress:
  • Performancevergleich und -entwicklung von aktiv und passiv gemanagten Schweizer Aktienfonds im Zeitraum von 2008 bis 2019 (BSc)
  • Parameter Uncertainty and Equity Premium Prediction via Machine Learning Techniques (MSc)
  • Factor Momentum Performance in Characteristics based Portfolios (MSc)
  • Measurement and comparability of impact investing in asset management (MSc)
  • Predicting stock returns in the presence of breaks by following the approach from Smith and Timmermann (2021): Evidence from the European market (MSc)
  • Parameter Uncertainty and Portfolio Management (PhD Mentoring)
  • Machine Learning in Financial Economics (PhD Mentoring)
• 2022:
  • The Risk Premium of Critical Raw Materials - A Signal for Priority Needs in Realising the European Green Deal (MSc, A. Caroline White)
  • Empirical asset pricing via Machine Learning: Evidence from the Cryptocurrency Market (MSc, A. Stefan Macanovic)
  • Inflation-Hedged Portfolios within the European Stock Market (MSc, A. Fabian Köffel)
  • Sub-portfolio Optimization (BSc, A. Jasminko Kulenovic)
  • Stock Market Prediction With Long Short-Term Memory Recurrent Neural Networks (BSc, A. Elizabeth Sanyal)
  • Robust Portfolio Optimization with Deep learning - Using past Forecast Errors to Improve Return Predictions (MSc, A. Markus Wabnig, best Master in Finance 2022)
  • The EUR/CHF Exchange Rate and Euro Area Stress (MSc, A. Nicolas Tschütscher, best Master Thesis in Finance 2022)
  • Incorporating ESG Score Changes in Portfolio Management via Deep Learning (MSc, A. Lukas Müller)
  • Neural Network for KPI based Time Series Sales Forecasting (MSc, A. Niklas Leibinger)
  • Optimal Portfolio Building using Deep Learning Techniques (MSc, A. Michael Metz)
  • Economic Uncertainty Premia in U.S. Stock Markets during the COVID-19 Pandemic (MSc, A. Leo Pitscheneder)
• 2021:
  • Cryptocurrency: Delisting Bias in the coinmarketcap database (BSc, A. Fabian Köffel)
  • Multivariate Factor Forecasting and Smart Beta Investments (MSc, A. Dominik Brändle)
  • Evaluating Dollar-Cost Averaging under the Aumann-Serrano Framework (MSc, A. Jonas Sterk)
  • Impact of ESG exclusion on firms’ cost of capital (MSc, A. Fabian Müller)
  • Sales Forecasting with Machine Learning (MSc, A. Johannes Gassner)
  • Investors’ herding in the German equity options market: Evidence from the COVID-19 crisis (MSc, A. Dmytro Livshyts)
• 2020:
  • “Long/Short” Momentum-Strategie am Kryptowährungsmarkt (BSc, A. Timothy Rist, best Bachelor Thesis in Business Administration 2020)
  • Momentum meets Uncertainty (MSc, A. 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, A. Emanuel Broger)
  • Künstliche Intelligenz und Anwendungsmöglichkeiten in der Vermögensberatung (MBA, A. Lukas Schäper)
  • Stock Price Prediction for Portfolio Management Using Recurrent Neural Networks and Machine Learning (EMBA, A. Jensen Chang)
• 2019:
  • Cross-Sectional Volatility and the Prediction of Factor Premia (MSc, A. Maibach, Runner-Up Finance Award)
  • Multi-Factor Timing (MSc, B. Jäger, Winner of Finance Award, best Master Thesis in Business Science)
  • Stock Age as Proxy for Uncertainty of Parameters (MSc, S. Sturzenegger)
  • The Influence of News Coverage on Stock Returns – Evidence from European Markets (MSc, A. Person)
  • Portfolio Optimization in a Cryptocurrency Environment: An Omega Optimization (BSc, D. Brändle, Runner-Up Finance Award)
• 2018:
  • Liquid betting against beta revisited: Evidence from all over the world (MSc, L. Salcher)
  • From IPO to Obsolete: Stock Age related investment strategies (MSc, P. Thoma)
  • Eurex index dividend futures hedging (MSc, A. Spiegel)
  • Predicting Equity Bear and Bull Markets: International Evidence (MSc, L. Liepert)
  • The North Korea threat and its effect on global stock markets: The case of South Korea, Japan and the USA (BSc, M. Wabnig)
• 2017:
  • Liquidity and the Polish Stock Market: Empirical Tests of Asset Pricing Models and Inclusion of Liquidity Factors (MSc, P. Ruzicka)
  • Dynamic Asset Allocation Strategies and An Optimisation Framework: How Optimal is Optimised? (MSc, F. 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, P. Heise)
  • Using momentum to improve low-volatility strategies: Evidence from the US stock market (BSc, M. Amann)
• 2016:
  • Terrorism and its effect on financial markets (MSc, S. Geiger)
  • Prognose von Aktienrenditen - Eine empirische Forschung über die Vorhersagbarkeit der zukünftigen Renditen des Swiss Performance Index anhand von Renditen - Dispersionen (BSc, B. Jäger)
  • Betting Against Beta (MSc, C. Lamprecht)
  • Cross-Sectional and Option-Implied (Higher) Moments and the Predictability of Historical Volatility: US Study (MSc, O. 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)