A further analysis of robust regression modeling and data mining corrections testing in global stocks

John B. Guerard (), Ganlin Xu and Harry Markowitz
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John B. Guerard: McKinley Capital Management, LLC
Ganlin Xu: McKinley Capital Management, LLC

Annals of Operations Research, 2021, vol. 303, issue 1, No 9, 175-195

Abstract: Abstract 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.

Keywords: Portfolio optimization; Data mining; Testing; I/B/E/S forecasts (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10479-020-03521-y

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