Abstract
This article shows how sparse solutions can be generated in parametric portfolio selection methods. Sparse mean-variance optimization procedures can be applied after the translation of parametric weight estimates into implied mean return estimates. The results of our empirical analysis suggest that such a translation is potentially helpful for sparse parametric portfolio selection. We however find that l1-penalized portfolio optimization methods have unintended properties and are outperformed by a simple heuristic approach in our data set.
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