IDEAS home Printed from https://ideas.repec.org/a/taf/defpea/v30y2019i7p877-889.html
   My bibliography  Save this article

Does the Efficient Market Hypothesis Fit Military Enterprises in China?

Author

Listed:
  • Kai-Hua Wang
  • Chi-Wei Su
  • Ran Tao
  • Hsu-Ling Chang

Abstract

This paper investigates whether the efficient market hypothesis (EMH) fits the Chinese military market using the Sequential Panel Selection Method (SPSM) and the Panel KSS unit root test with a Fourier function. We obtain evidence for structural shifts and non-linearity in the stock prices of the military industry in the Chinese stock market. Because sharp shifts and structural breaks are taken into account, the unit root hypothesis for most listed companies is rejected. Our result suggests that the Chinese military market is inefficient because of such factors as defense reforms, friction in the stock market, and irrational investors. We provide investment implications to enable future stock price movements to be predicted based on past behavior and enable trading strategies to be developed to earn abnormal returns. Meanwhile, Chinese defense enterprises should continue to implement industrial reforms, change their bureaucratic culture, and develop a market-oriented workforce.

Suggested Citation

  • Kai-Hua Wang & Chi-Wei Su & Ran Tao & Hsu-Ling Chang, 2019. "Does the Efficient Market Hypothesis Fit Military Enterprises in China?," Defence and Peace Economics, Taylor & Francis Journals, vol. 30(7), pages 877-889, November.
    • Handle: RePEc:taf:defpea:v:30:y:2019:i:7:p:877-889
    DOI: 10.1080/10242694.2018.1425118
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/10242694.2018.1425118
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/10242694.2018.1425118?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhou, Zhongbao & Gao, Meng & Xiao, Helu & Wang, Rui & Liu, Wenbin, 2021. "Big data and portfolio optimization: A novel approach integrating DEA with multiple data sources," Omega, Elsevier, vol. 104(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:defpea:v:30:y:2019:i:7:p:877-889. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/GDPE20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.