I am a Senior Quantitative Researcher in the Investment Strategy Department at Sal. Oppenheim, one of Germany’s leading quantitative and research-driven investment managers, and a PhD Student at the Chair of Financial Econometrics at Ludwig Maximilian University (LMU) Munich. In September 2015 I have won the Best Paper Award at the Annual Meeting of the German Finance Association for my research on cross-sectional asset pricing using machine learning algorithms.
Research and Teaching Fields
My research focuses on asset pricing, asset allocation, forecasting, data science and household finance. I am interested in understanding and analyzing the drivers of asset prices. The academic evidence points to the fact that expected returns and risks are time-varying and partly predictable. A natural way is then to develop appropriate forecasting models to utilize this predictability. The growing availability of new data helps to tackle those challenges. In recent works I use machine learning algorithms to extract new information from big data. And last, understanding household decision making is key for developing proper investment advice.
1. Strategical Asset Allocation
2. Tactical Asset Allocation
3. Risk Allocation (Target VaR, CPPI, …)
4. Tactical Factor Allocation (Value, Size, Carry, …)
5. Annual Investment Outlook (Scenario Analysis)
6. Equity Stock Selection
1. Long-Term Expected Returns (5-10 Years)
2. Short-Term Expected Returns (1 Month)
3. Risk Simulation (1 Day – 10 Years)
4. Portfolio construction (Mean-CVaR, Equal Risk Contribution, …)