Efficient global portfolios: Big data and investment universes

Abstract

In this analysis of the risk and return of stocks in the United States and global markets, we apply several portfolio construction and optimization techniques to U.S. and global stock universes. We find that (1) mean-variance techniques continue to produce portfolios capable of generating excess returns above transaction costs and statistically significant asset selection, (2) optimization techniques minimizing expected tail loss are statistically significant in portfolio construction, and (3) global markets offer the potential for greater returns relative to risk than domestic markets. In this experiment, mean-variance, enhanced-index-tracking techniques, and mean-expected tail-loss methodologies are examined. Global equity data and the vast quantity (and quality) of the data relative to U.S. equity modeling have been discussed in the literature. We estimate expected return models in the U.S. and global equity markets using a given stock-selection model and generate statistically significant active returns from various portfolio construction techniques.

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