  We revisit Markowitz's mean-variance portfolio selection model by considering
a distributionally robust version, where the region of distributional
uncertainty is around the empirical measure and the discrepancy between
probability measures is dictated by the so-called Wasserstein distance. We
reduce this problem into an empirical variance minimization problem with an
additional regularization term. Moreover, we extend recent inference
methodology in order to select the size of the distributional uncertainty as
well as the associated robust target return rate in a data-driven way.
