Advanced (tidy) Empirical Finance

During the spring semester 2021 I designed a new course Advanced Empirical Finance: Topics and Data Science for the master students at KU. The course aims at providing a unified coding framework in R to tackle many (probably too many) common issues in empirical finance:

  • Portfolio allocation and backtesting
  • Portfolio sorts and asset pricing tests
  • Machine learning in empirical asset pricing
  • Volatility estimation
  • High frequency trading and econometrics

Along the course I collected my own R-code and curated some solutions for typical problems. I will be working on updating the document further in the future, but everybody is invited to take a look, make use of the code and - even better - to provide feedback on improvements or other interesting features. The full book is available here, comments are very welcome.

Assistant Professor in Finance

I pursue research questions related to market fragmentation, high frequency trading and big data in financial applications.