TY - JOUR
T1 - Efficient global portfolios
T2 - Big data and investment universes
AU - Guerard, J. B.
AU - Rachev, S. T.
AU - Shao, B. P.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84884917263&partnerID=8YFLogxK
U2 - 10.1147/JRD.2013.2272483
DO - 10.1147/JRD.2013.2272483
M3 - Article
AN - SCOPUS:84884917263
SN - 0018-8646
VL - 57
JO - IBM Journal of Research and Development
JF - IBM Journal of Research and Development
IS - 5
M1 - 6601697
ER -