Decision methods and tools in the context of pension finance

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Type and Duration

FFF-Förderprojekt, September 2019 until August 2020 (finished)


Chair in Finance

Main Research

Wealth Management

Field of Research

International financial management


In this project we developed an R-package (available through github at 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 where - based on each persons individual settings.

Project results:


  • Forschungsförderungsfonds der Universität Liechtenstein