The Limits of Optimisation: Why Simpler Portfolios Often Win

Mean-variance optimisation is the theoretical cornerstone of modern portfolio construction. In practice, however, estimated parameters are noisy — and that noise can render sophisticated optimisation worse than doing nothing at all. This paper uses a controlled simulation framework to quantify the performance cost of estimation error across five portfolio strategies: the unconstrained plug-in tangency portfolio, a long-only constrained tangency portfolio, the global minimum-variance portfolio, a risk parity allocation, and the naïve equal-weight portfolio. We find that under realistic estimation conditions, the equal-weight and risk parity strategies deliver near-optimal Sharpe ratios, while the unconstrained plug-in portfolio achieves only a fraction of the theoretical optimum. These results carry direct implications for systematic portfolio construction: in regimes of high parameter uncertainty, analytical simplicity is not a concession — it is a competitive advantage.