Inhalte der LV: 

• Introduction to systematic investing
• Overview of the R ecosystem for financial time series analysis, portfolio; optimization, visualization, data acquisition and preprocessing
• Portfolio weighting schemes
• Portfolio optimization under constraints and with different objectives
• Risk-adjusted performance measures
• Introduction to factor models (Value, Momentum, Size, ...)
• Signal generation and ranking
• Backtesting frameworks and pitfalls
• Performance attribution
• Risk metrics and drawdown analysis
• Transaction costs modelling and liquidity constraints
• Strategy automation and reporting in R

By the end of the course, students will: 

• Understand the principles of systematic investing and portfolio construction.
• Learn to implement rule-based investment strategies using R.
• Apply optimization techniques and backtesting frameworks.
• Evaluate performance and risk metrics for systematic portfolios.

Participation is compulsory. Students are not allowed to miss more than one unit.

This course is structured as an interactive in-class seminar, focusing on hand-on implementation of systematic investment strategies. The emphasis is on practical coding in R, enabling students to implement and evaluate their own rule-based strategies using real financial data. 

• Midterm exam (written): 30%
• Endterm exam (written): 50%
• Home assignment: 20% 

Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.