How good is the out-of-sample performance of optimized portfolios : an empirical comparison of optimal versus naive diversification

Abstract

Preceding research is inconclusive on the empirical performance of optimized portfolios. In this thesis I evaluate the out-of-sample performance of a minimum-variance portfolio, a meanvariance portfolio, an equally-weighted portfolio, and a value-weighted market portfolio across eight U.S. datasets and seven different out-of-sample time periods. This is done in order to determine if any of the asset-allocation strategies deliver statistically significantly, higher Sharpe ratios compared to the other implemented portfolio models. Although the minimumvariance portfolio is persistent in delivering the highest out-of-sample Sharpe ratio, I find that none of the optimized or equally-weighted portfolios consistently deliver statistically distinguishable Sharpe ratios from each other. The value-weighted market portfolio is found to frequently be statistically suboptimal when compared to the other asset-allocation strategies, suggesting that this strategy should generally be avoided in face of the others. By comparing the in-sample and out-of-sample Sharpe ratio of the mean-variance portfolio, I find that there is estimation error affecting the performance.