Does the Efficient Market Hypothesis Fit Military Enterprises in China?

Kai-Hua Wang, Chi-Wei Su, Ran Tao and Hsu-Ling Chang

Defence and Peace Economics, 2019, vol. 30, issue 7, 877-889

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.

Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1080/10242694.2018.1425118 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:defpea:v:30:y:2019:i:7:p:877-889

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/GDPE20

DOI: 10.1080/10242694.2018.1425118

Access Statistics for this article

Defence and Peace Economics is currently edited by Professor Keith Hartley

More articles in Defence and Peace Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().