Performance of Portfolios Optimized with Estimation Error
解释了均值方差优化投资组合在样本外表现不佳的原因，通过渐近展开为估计风险提供了理论偏差调整，并给出了非贝叶斯调整公式，显著减少了国际股票组合的偏差并提高了经济收益。
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.