Search

We propose an optimal combining strategy to mitigate estimation risk for the popular mean-variance portfolio choice problem in the case without a risk-free asset. We find that our strategy performs well in general, and it can be applied to known estimated rules and the resulting new rules outperform the original ones. We further obtain the exact distribution of the out-of-sample returns and explicit expressions of the expected out-of-sample utilities of the combining strategy, providing not only a fast and accurate way of evaluating the performance, but also analytical insights into the portfolio construction. This paper was accepted by Tyler Shumway, finance. [ABSTRACT FROM AUTHOR]

Tills paper explores the impact of adjustments to the inputs on total returns, terminal wealth, and portfolio turnover in an unconstrained monthly mean-variance (MV) asset allocation over time. It is well known that MV allocations are very sensitive to small forecast errors in the means and covariances. This sensitivity is especially pronounced for errors in means. One way to control this sensitivity to forecast errors is to use Stein estimation. We examined three naive applications of Stein estimation for six individual country stock indexes, five country bond indexes and five cash indexes. This study has two major conclusions. First, any of the suggested adjustments to inputs dominate the results of an unadjusted-input MV optimization. Adjusted-input portfolios have higher mean return, less variance and greater terminal wealth than unadjusted-input portfolios. Second, these improvements become even greater with transaction costs. [ABSTRACT FROM AUTHOR]