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*
  • Predicting Equity Risk Premia via Machine Learning this paper
  • Factor Momentum: Does it exist in statistical factors?
  • Uncertainty and Google searches Dzielinski, (2012)

Thesis (Mainly master thesis)

Please be aware, that I will only supervise theses, where the Research Proposal fulfills the following requirements:

  • Either use exactly one of the topics given below, or
  • Suggest a topic that is closely related to one of those given below. If so, the main paper to base your research on must have been published (or accepted for publication) in one of the Top Finance Journals (preferrably A+, but at least A in this ranking).
  • For the Research Pitch/Proposal I suggest to take a very close look at Robert Faff’s Webpage on pitching research, especially on the Finance examples.
  • 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
  • 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
    • Predicting the Equity Premium with Machine Learning and turbulencnce 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
  • Innovative Finance - Uncertainty (Parameter/Financial/Macro-Financial/Portfolio …):

    • NEW: Macro-Financial Uncertainty and the work of Rossi & Sekhposyan, 2017 and Rossi, Sekhposyan & Soupre, 2016. Relate the various indices to each other. Whate 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:

  • Asset Selection:

    • Robust portfolios: Estimation & Empirics
Cryptocurrencies: Many of the topics above allow for an application using cryptocurrencies. I suggest using my modification of the crypto2 package to download related data.

Theses Supervised:

  • In progress:
    • The Price of Critical Mineral Risk: Market Risk Premium for Rare Earth Elements (MSc)
    • Empirical asset pricing via Machine Learning: Evidence from the Cryptocurrency Market (MSc)
    • Inflation-Hedged Portfolios within the European Stock Market (MSc)
    • Parameter Uncertainty and Equity Premium Prediction via Machine Learning Techniques (MSc)
    • Parameter Uncertainty and Portfolio Management (PhD Mentoring)
    • Machine Learning in Financial Economics (PhD Mentoring)
  • 2022:
    • 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)
Sebastian Stöckl
Sebastian Stöckl
Assistant Professor in Financial Economics (tenured)

My research interests include Financial and Economic Uncertainty as well as Empirical Asset Pricing.