A Further Analysis of Robust Regression Modeling in Global Stocks

50 Pages Posted: 20 Jun 2018

See all articles by John Guerard

John Guerard

McKinley Capital Management, LLC

Ganlin Xu


Harry Markowitz

University of California at San Diego

Date Written: June 4, 2018


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.

Suggested Citation

Guerard, John and Xu, Ganlin and Markowitz, Harry, A Further Analysis of Robust Regression Modeling in Global Stocks (June 4, 2018). Available at SSRN: https://ssrn.com/abstract=3190716 or http://dx.doi.org/10.2139/ssrn.3190716

John Guerard (Contact Author)

McKinley Capital Management, LLC ( email )

3301 C St # 500
Anchorage, AK 99503
United States

Ganlin Xu

Guidedchoice.com ( email )

8910 University Center Lane
Suite 400
San Diego, CA
United States

Harry Markowitz

University of California at San Diego ( email )

9500 Gilman Drive
La Jolla, CA 92093-0508
United States
(858) 534-3383 (Phone)

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