A further analysis of robust regression modeling and data mining corrections testing 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, LAR regression, and LASSO regression modeling to estimate stock selection models. Markowitz-based optimization techniques is used in portfolio construction within a global stock universe. We apply the Markowitz–Xu data mining corrections test to a global stock universe. 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 and LASSO-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 several data mining tests such that regression models produce statistically significant asset selection for global stocks. Recent Sturdy-Regression modeling technique may offer the greatest potential for further research for statistically based stock selection modeling.