Mean-Variance Versus Full-Scale Optimization: In and Out of Sample

Abstract

We present a recent innovation to portfolio construction called full-scale optimization. In contrast to mean-variance analysis, which assumes that returns are normally distributed or that investors have quadratic utility, full-scale optimization identifies the optimal portfolio given any set of return distributions and any description of investor preferences. It therefore yields the truly optimal portfolio in sample, whereas mean-variance analysis provides an approximation to the in-sample truth. Both approaches, however, suffer from estimation error. We employ a bootstrapping procedure to compare the estimation error of full-scale optimization to the combined approximation and estimation error of mean-variance analysis. We find that, to a significant degree, the in-sample superiority of full-scale optimization prevails out-of-sample.

Keywords: Mean-Variance Analysis, Full-Scale Optimization, Portfolio Formation