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Data Mining Corrections Testing in Chinese Stocks

Author

Listed:
  • John B. Guerard, Jr.

    (McKinley Capital Management, LLC, Anchorage, Alaska 99503)

  • Robert A. Gillam

    (McKinley Capital Management, LLC, Anchorage, Alaska 99503)

  • Harry Markowitz

    (Harry Markowitz & Company and McKinley Capital Management, San Diego, California 92109)

  • 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)

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.

Suggested Citation

  • John B. Guerard, Jr. & Robert A. Gillam & Harry Markowitz & Ganlin Xu & Shijie Deng & Ziwei (Elaine) Wang, 2018. "Data Mining Corrections Testing in Chinese Stocks," Interfaces, INFORMS, vol. 48(2), pages 108-120, April.
    • Handle: RePEc:inm:orinte:v:48:y:2018:i:2:p:108-120
    DOI: 10.1287/inte.2017.0908
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    Cited by:

    1. John B. Guerard & Ganlin Xu & Harry Markowitz, 2021. "A further analysis of robust regression modeling and data mining corrections testing in global stocks," Annals of Operations Research, Springer, vol. 303(1), pages 175-195, August.

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