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Industry classification and the efficiency of intra-industry information transfers

Author

Listed:
  • Dennis Y. Chung
  • Karel Hrazdil
  • Kim Trottier

Abstract

Purpose - – Motivated by recent studies that demonstrate the superiority of the Global Industry Classification System (GICS) relative to the Standard Industry Classification (SIC) system in capital market research, the authors revisit the stock market anomaly documented by Thomas and Zhang (TZ) (“Overreaction to intra-industry information transfers?”Journal of Accounting Research, Vol. 46, pp. 909-940) and analyze whether the anomaly based on SIC remains evident when intra-industry information transfers are based on the GICS. The paper aims to discuss these issues. Design/methodology/approach - – The authors first replicate TZ and test whether stock prices of late announcers in response to earnings reported by early announcers in the same SIC industry are significantly related to subsequent price responses of late announcers to their own earnings reports. In the multivariate setting, the authors then evaluate whether the magnitude and significance of the overreaction anomaly changes under the more comprehensive GICS and across different time periods. Findings - – The authors first confirm the over-reaction anomaly based on SIC as documented by TZ. Utilizing a larger sample of firms based on the GICS and extending TZ for a new time period, the authors then demonstrate that the overreaction anomaly disappears during recent years, a period that is characterized by markedly higher trading activity. Research limitations/implications - – The findings provide new insights and contributions to the debate on whether or not market significantly misprices information transfers. Originality/value - – The authors are first to utilize the GICS in evaluating intra-industry information transfers.

Suggested Citation

  • Dennis Y. Chung & Karel Hrazdil & Kim Trottier, 2014. "Industry classification and the efficiency of intra-industry information transfers," American Journal of Business, Emerald Group Publishing Limited, vol. 29(1), pages 95-111, April.
  • Handle: RePEc:eme:ajbpps:v:29:y:2014:i:1:p:95-111
    DOI: 10.1108/AJB-03-2013-0014
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    Citations

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    Cited by:

    1. Zura Kakushadze & Willie Yu, 2017. "Open Source Fundamental Industry Classification," Papers 1706.04210, arXiv.org, revised Dec 2017.
    2. Cox, Raymond A.K. & Dayanandan, Ajit & Donker, Han, 2016. "The Ricochet Effect of Bad News," The International Journal of Accounting, Elsevier, vol. 51(3), pages 385-401.

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