I am a Quantitative Researcher in the Investment Management Industry since 2007 and I am about to finish my PhD in Financial Econometrics at the Department of Statistics at LMU Munich. I have a profound knowledge of Financial Economics and Advanced Statistical Methods. My three recent academic papers focus on Financial Applications of Machine Learning, Textual Analysis and Deep Learning respectively. As an Investment Manager I gained profound knowledge over a broad range of topics: stock selection, factor investing, tactical and strategical asset allocation and risk management. In 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. I have experience with a wide range of programming languages. Presently I work with R, SQL and Git.
Research and Teaching Fields
My research focuses on Asset Pricing, Asset Allocation, Forecasting and Advanced Statistical Methods (Machine Learning, Textual Analysis and Deep Learning). 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 and easier use of advanced statistical methods help to tackle those challenges. In recent works I use machine learning algorithms to extract new information from big data.
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, …)