A Further Analysis of Robust Regression Modeling in Global Stocks

In this analysis of the risk and return of stocks in global markets, we build a reasonably large number of stock selection models and create optimized portfolios to outperform a global benchmark. We apply robust regression techniques include variable selection method like LASSO and LAR regression in producing stock selection models and Markowitz-based optimization techniques in portfolio construction in a global stock universe. We apply the Markowitz-Xu (1994) Data Mining Corrections test to a global and Chinese stock universes and report interesting results. We find that (1) robust regression applications are appropriate for modeling stock returns in global markets; (2) weighted latent root regression robust regression techniques work as well as LAR, LASSO, and Sturdy-Regressions in building effective stock selection models; (3) mean-variance techniques continue to produce portfolios capable of generating excess returns above transactions costs; and (4) our models pass data mining tests such that regression models produce statistically significant asset selection for global stocks. Recent Sturdy-Regression modeling techniques offers the greatest potential for further research for statistically based stock selection modeling