Data Mining Corrections Testing in Chinese Stocks

John B. Guerard, Jr. (), Robert A. Gillam (), Harry Markowitz, Ganlin Xu (), Shijie Deng () and Ziwei (Elaine) Wang ()
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John B. Guerard, Jr.: McKinley Capital Management, LLC, Anchorage, Alaska 99503
Robert A. Gillam: McKinley Capital Management, LLC, Anchorage, Alaska 99503
Ganlin Xu: GuidedChoice.com, Inc., and McKinley Capital Management, San Diego, California 92122
Shijie Deng: School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332
Ziwei (Elaine) Wang: McKinley Capital Management, LLC, Anchorage, Alaska 99503

Interfaces, 2018, vol. 48, issue 2, 108-120

Abstract: In this analysis of the risk and return of stocks in global and Chinese markets, we build a reasonably large number of models for stock selection and create optimized portfolios to outperform a global benchmark. We apply robust regression techniques in producing stock-selection models and Markowitz-based optimization techniques in portfolio construction in a global stock universe and two Chinese stock universes. We report the results of applying a data mining corrections test to the global and Chinese stock universes. We find that (1) robust regression applications are appropriate for modeling stock returns in global and Chinese stock markets; (2) mean-variance techniques continue to produce portfolios capable of generating returns that exceed transactions costs; and (3) our global portfolio selection models pass data mining tests, such that the models produce statistically significant asset selection for global and MSCI-China universes, but not for China A-shares.

Keywords: global portfolio selection; investment; optimization (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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