DIVERSIFICATION STRATEGIES: DO LIMITED DATA CONSTRAIN INVESTORS?

Authors


  • This article has benefited from helpful comments from the editor, Jeffrey Mercer; associate editor, Ray DeGennaro; and an anonymous referee. We are grateful to Drew Winters, finance area seminar participants at Texas Tech University, and conference participants at the February 2012 Midwest Finance Association annual meeting for valuable suggestions. All remaining errors are ours.

Abstract

We demonstrate that the mean–variance optimal portfolio does not outperform (out of sample) the naive 1/N diversification strategy even if securities are grouped into indexes or broad asset classes. This finding is due to insufficient data on past returns, which limit investors' ability to accurately estimate the means and covariance structure of securities. The resulting high estimation errors eliminate the benefits of using the means and covariance matrix as compared to the naive strategy in portfolio optimization. Using value-weighted indexes, characteristic-sorted portfolios, or portfolios defined by principal components as underlying assets in mean–variance optimization does not help. At the same time, increasing data frequency or adding data on past earnings may in some cases make mean–variance optimization useful.

Ancillary