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Investor relations on the Internet: a survey of the Euronext zone

Author

Listed:
  • J. Geerings
  • L. H. H. Bollen
  • H. F. D. Hassink

Abstract

This study investigates the investor relations activities on the Internet of companies listed on the Euronext stock exchange. For this purpose, the websites of the fifty largest listed companies in each of the countries Belgium, France and the Netherlands were screened for investor relations items. Results obtained by using a three-stage model show that most companies in the Euronext zone are at the second stage of Internet investor relations, i.e. where information available through other sources is combined to better inform investors. In the third stage companies use the full interactive possibilities of the Internet for investor relations purposes. French and Dutch companies use the Internet for investor relations purposes more widely and more intensely than Belgian companies. The study also revealed a size effect; large companies use the Internet for investor relations purposes more extensively than smaller companies. This conclusion holds for each of the three countries. The leading companies in France and the Netherlands are either at the third stage of Internet investor relations or are ready to move to this stage.

Suggested Citation

  • J. Geerings & L. H. H. Bollen & H. F. D. Hassink, 2003. "Investor relations on the Internet: a survey of the Euronext zone," European Accounting Review, Taylor & Francis Journals, vol. 12(3), pages 567-579.
  • Handle: RePEc:taf:euract:v:12:y:2003:i:3:p:567-579
    DOI: 10.1080/0963818032000083531
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    References listed on IDEAS

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    15. Ivica Pervan, 2006. "Voluntary financial reporting on the internet – analysis of the practice of Croatian and Slovene listed joint stock companies," Financial Theory and Practice, Institute of Public Finance, vol. 30(1), pages 1-27.
    16. Borchert, Philipp & Coussement, Kristof & De Caigny, Arno & De Weerdt, Jochen, 2023. "Extending business failure prediction models with textual website content using deep learning," European Journal of Operational Research, Elsevier, vol. 306(1), pages 348-357.
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