Less Is More: Ranking Information, Estimation Errors and Optimal Portfolios
We offer a novel approach that aims at mitigating the crippling effects that parameter uncertainty and estimation errors have on the out-of-sample perforance of mean-variance optimized portfolios. We argue that investors should not rely on exact forecasts when optimizing portfolios but instead base their optimizations on ranking or grouping information and thereby implicitly reduce the informational content of their parameter inputs.
2024-01-01
