Mean-ETL Optimization of a Global Portfolio

Abstract

In this article, the authors examine the mean-expected tail loss (ETL) portfolio optimization of global portfolios using a global expected return (GLER) model, which is based on fundamental data of companies. Empirically, they show that for the 2003–2011 period, mean-ETL portfolios based on a GLER model could generate statistically significant active returns. Also, they show that the GLER model active return dominates the United States expected returns model active returns for the same periods. The global markets offer greater opportunities than the U.S. markets.