Decision methods and tools in the context of pension finance

Type and Duration

Coordinator

Main Research

Field of Research

Description

In this project we developed an R-package (available through github at https://github.com/sstoeckl/pensionfinanceLi) to optimize decisions individuals in Liechtenstein's pension system have to take. The package contains several optimizers as well as a documentation (available through 'vignette("model")' once the package is installed). We have started the optimization for a feasible parameter grid to determine which variables are the most relevant drivers of optimal pension decisions. Based on the results we have trained three machine learning models (a hyper parameter-tuned random forest performs best) to allow individuals to receive faster and near-optimal decisions without having to wait for the individual optimization on each run (up to 25 minutes on a regular CPU). Predictions from these models are available to the public at https://apps.resqfin.com/pfli where - based on each persons individual settings.

Project results:

Principal Investigator

Ass.-Prof. Dr. Sebastian Stöckl

Contributor

Prof. Dr. Michael Hanke

Sponsor

• Forschungsförderungsfonds der Universität Liechtenstein