In Defense of Portfolio Optimization: What If We Can Forecast?

Abstract

We challenge academic consensus that estimation error makes mean-variance portfolio strategies inferior to passive equal-weighted approaches. We demonstrate analytically, via simulation and empirically that investors endowed with modest forecasting ability benefit substantially from an MV approach. An investor with some forecasting ability improves expected utility by increasing the number of assets considered. We frame our study realistically using budget constraints, transaction costs and out-of-sample testing for a wide range of investments. We derive practical decision rules to choose between passive and mean variance optimisation results and generate results consistent with much financial market practice and the original Markowitz formulation.