Less is More: Ranking Information, Estimation Errors and Optimal Portfolios

back to overview


Salcher, L., & Stöckl, S. (2022). Less is More: Ranking Information, Estimation Errors and Optimal Portfolios. Presented at the European Conference on Stochastic Optimization and Computational Management Science, Venice, Italy.

Publication type

Presentation at Scholarly Conference


Despite its significance for Finance as an academic field, mean-variance optimization has yet to be broadly accepted as an investment opportunity in practice due to the crippling effects estimation errors have on the out-of-sample performance of such portfolios. In this paper we offer a novel approach that aims at resolving this issue. More precisely, we propose optimizing portfolios based on forecasted ranking information instead of historical data. The main idea behind this approach is that reducing the informational content of input parameters eliminates outliers caused by estimation errors which in turn means mean-variance optimization suggests less extreme weights resulting in an overall better diversified and less concentrated portfolio. Our results confirm that our approach has a higher risk-adjusted performance compared to the plug-in mean-variance approach and also outperforms naively diversified portfolios. Furthermore, our approach is more effective when estimation errors are expected to be larger.


Organizational Units

  • Chair in Finance