Abstract
Portfolio choice by full‐scale optimization applies the empirical return distribution to a parameterized utility function, and the maximum is found through numerical optimization. Using a portfolio choice setting of three UK equity indices we identify several utility functions featuring loss aversion and prospect theory, under which full‐scale optimization is a substantially better approach than the mean–variance approach. As the equity indices have return distributions with small deviations from normality, the findings indicate much broader usefulness of full‐scale optimization than has earlier been shown. The results hold in‐ and out‐of‐sample, and the performance improvements are given in terms of utility as well as certainty equivalents.
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