Potential Thesis Topics

General Area of Thesis Supervision:

Machine Learning in Finance, Cryptocurrencies, Political Finance, Empirical Finance, Quantitative Finance, Risk Management, Parameter and Model Uncertainty, Forecasting

Open Topics:

Seminars (e.g. Seminar in finance)

• Ambiguity/Parameter Uncertainty and Real Option Portfolios (Literature Review)
• 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)?

Thesis (Mainly master thesis)

• Political Finance:

  • The cost of populism (relate to corruption and economic freedom indices)
  • Election Portfolios: Drivers of Betting Quotes and Stock Returns
  • New: Estimate Political Risk Premia for US stocks based on various political exposure indices (e.g. betting odd betas and CEO political contributions)
  • New: Drivers of CEO political contributions: Are companies driving their CEOs or is it the other way round?
  • New: New measures of populism and their implications for country risk (estimated from options)

• Machine Learning in Finance:

  • Multivariate Predictability in Assets and Factors. Apply Machine Learning techniques to exploit linear and nonlinear predictability.
  • Predicting the Equity Premium with Machine Learning and turbulence measures rather than anomaly returns (see this paper)
  • Predicting Returns for Optimal portfolios: Forecasting returns vs Forecasting Weights.
  • What drives government bond returns? A forecasting exercise using macroeconomic data and (Bayesian) machine learning.
  • What drives dividend payout policies? A forecasting exercise using fundamental data and (Bayesian) machine learning.

• Cryptocurrencies:

  • Asset Pricing Factors: Non-Standard Errors and a live Shiny App for my crypto2-dataset.

• 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.

• Asset Pricing:

  • Sectorial Risks in Asset Pricing Factors. Exploit drivers and analyze best ways to neutralize sector risk.

• Asset Selection/Investment:

  • Predicting stock returns and the Sharpe ratio gap: How to adapt prediction models?
  • A review and implementation exercise regarding the growth-optimal portfolios