Journal Article

Performance of Portfolios Optimized with Estimation Error

Woodgate, Artemiza, Siegel, Andrew F

2007

Overview

We explain the poor out-of-sample performance of mean-variance optimized portfolios, developing theoretical bias adjustments for estimation risk by asymptotically expanding future returns of portfolios formed with estimated weights. We provide closed-form non-Bayesian adjustments of classical estimates of portfolio mean and standard deviation. The adjustments significantly reduce bias in international equity portfolios, increase economic gains, and are robust to sample size and to nonnormality. Dominant terms grow linearly with the number of assets and decline inversely with the number of past time periods. Under suitable conditions, Sharpe-ratio maximizing tangency portfolios become more diversified. Using these approximation methods it may be possible to assess, before investing, the effect of statistical estimation error on portfolio performance.

• Publisher: INFORMS,Institute for Operations Research and the Management Sciences
• Subject: Adjustment / Analysis / Applied sciences / Bayesian analysis / Bias / Business management / capital market line adjustment / Estimation / Estimation bias / estimation error / Exact sciences and technology / Financial portfolios / Financial theory / Investment risk / Investments / Investors / Management / Management science / Methods / Operational research and scientific management / Operational research. Management science / Optimization / Organizational effectiveness / Organizational structure / Portfolio diversification / Portfolio management / Portfolio performance / Portfolio selection / Portfolio theory / Portfolios / Risk aversion / Standard deviation / Statistical discrepancies / Statistical methods / statistical noise correction / Studies / Time periods