Niklas Gärtner
Senior Analyst, Index Research & Design
Key takeaways:
- In this paper we introduce a robust framework for identifying historically relevant macro environments based on similarity and significance
- We apply this framework to macroeconomic data to assess alignment with today’s conditions
- We leverage macro relevance to dynamically rotate equity factors, enhancing performance over static allocation strategies
Points of differentiation:
- Transparent and intuitive framework: The methodology is designed to be straightforward and fully transparent, avoiding opaque or overly complex models. It relies on clearly defined inputs and logic
- Controlled model complexity: With a minimal number of tuneable parameters, the framework limits discretionary flexibility and reduces the risk of overfitting
- Broad applicability across asset classes: The approach is broadly applicable to many asset classes, with flexibility to choose investment universe and input data
What does our research mean for investors?
- Enables investors to build context-aware strategies by using historically similar and meaningful environments to forecast returns
- By dynamically adjusting exposures in response to evolving macroeconomic conditions, the approach helps improve drawdowns and returns
- The framework offers a practical and adaptable foundation for building systematic strategies that can be tailored for many use cases
