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Are there Bubbles in the Defence Sector of China’s Stock Market (2005–2016)? New Evidence from Sequential ADF Tests

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  • Ying Zhang
  • Jiaxin Xu
  • Ling Zhai

Abstract

During the past decade, China’s defence industry has experienced significant reforms, with increasing numbers of defence companies being listed on the stock market. Meanwhile, China’s stock market has suffered from dramatic fluctuations. This article, therefore, attempts to break new ground on an empirical research agenda to detect bubbles in the defence sector of China’s stock market and how these bubbles can be impacted by the entire stock market and defence industry. To explain this issue, sequential unit root tests are introduced to investigate the existence of bubbles in the defence sector using monthly frequency data from China’s stock market from January 2005 to June 2016. The empirical results show strong evidence of explosive behaviours in the defence sector during the past decade. Moreover, the number of bubbles has changed greatly when accounting for the entire stock market. The empirical results indicate that bubbles in the middle of 2006 and at the end of 2014 were driven by the defence industry because of the sharp increase of military expenditure and the release of innovative defence reforms. Finally, we suggest that the government could control the annual budget increase at a steady pace and implement defence reforms by carrying out experimental units gradually.

Suggested Citation

  • Ying Zhang & Jiaxin Xu & Ling Zhai, 2020. "Are there Bubbles in the Defence Sector of China’s Stock Market (2005–2016)? New Evidence from Sequential ADF Tests," Defence and Peace Economics, Taylor & Francis Journals, vol. 31(1), pages 105-119, January.
  • Handle: RePEc:taf:defpea:v:31:y:2020:i:1:p:105-119
    DOI: 10.1080/10242694.2018.1428857
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    Cited by:

    1. Chiwei SU & Kaihua WANG, 2022. "Does Optimal Capital Structure Exist in Chinese Military Enterprises? Evidence from Industrial Heterogeneity," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 128-149, December.

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