IDEAS home Printed from https://ideas.repec.org/a/taf/euract/v20y2011i3p485-519.html
   My bibliography  Save this article

Artificial Intelligence Measurement of Disclosure (AIMD)

Author

Listed:
  • Michael Grüning

Abstract

Empirical research on voluntary disclosure lacks an appropriate measurement technique for quantifying the intensity of a firm's disclosure. In this paper, I introduce artificial intelligence measurement of disclosure (AIMD), a computerised technique for measuring disclosure using artificial intelligence, which derives disclosure proxies from English-language annual reports for 10 different information dimensions without human involvement. Criterion validity tests indicate that, controlling for a robust set of covariates and multiple statistical techniques, AIMD is negatively associated with information asymmetry as proxied by spreads and PIN. Furthermore, AIMD has construct validity when compared to the AIMR disclosure rating, Standard & Poor's Transparency and Disclosure Rating, several proprietary manual disclosure scorings and companies’ own assessment of their level of disclosure as indicated by a survey. I also demonstrate the applicability of AIMD as a cost-effective technique for measuring disclosure using a sample of 127,895 firm-year observations of companies regulated by the SEC.

Suggested Citation

  • Michael Grüning, 2011. "Artificial Intelligence Measurement of Disclosure (AIMD)," European Accounting Review, Taylor & Francis Journals, vol. 20(3), pages 485-519, July.
    • Handle: RePEc:taf:euract:v:20:y:2011:i:3:p:485-519
    DOI: 10.1080/09638180.2011.585792
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/09638180.2011.585792
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/09638180.2011.585792?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. Elshandidy, Tamer & Fraser, Ian & Hussainey, Khaled, 2013. "Aggregated, voluntary, and mandatory risk disclosure incentives: Evidence from UK FTSE all-share companies," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 320-333.

    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:euract:v:20:y:2011:i:3:p:485-519. 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/REAR20 .

    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.