Optimizing the Performance of Sample Mean-Variance Efficient Portfolios

Abstract

We propose a comprehensive empirical strategy for optimizing the out-of-sample performance of sample mean-variance efficient portfolios. After constructing a sample objective function that accounts for the impact of estimation risk, specification errors, and transaction costs on portfolio performance, we maximize the function with respect to a set of tuning parameters to obtain plug-in estimates of the optimal portfolio weights. The methodology offers considerable flexibility in specifying objectives, constraints, and modeling techniques. Moreover, the resulting portfolios have well-behaved weights, reasonable turnover, and substantially higher Sharpe ratios and certainty-equivalent returns than benchmarks such as the 1/N portfolio and S&P 500 index.

Keywords: active management, conditioning information, estimation risk, mean-variance optimization, portfolio choice, turnover

JEL Classification: G11, G12, C11