IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-0-387-30399-4_3.html
   My bibliography  Save this book chapter

Equilibrium Voluntary Disclosures when Firms Possess Random Multi-Dimensional Private Information

In: Essays in Accounting Theory in Honour of Joel S. Demski

Author

Listed:
  • Ronald A. Dye

    (Northwestern University)

  • Mark Finn

    (Northwestern University)

Abstract

This paper presents an equilibrium model of voluntary disclosures for the seller of an asset who receives a random sample of information of random size about the asset’s value. Even though (a) antifraud rules prevent the seller from making false statements about the value of the items in his random sample, (b) all potential purchasers of the asset know that the seller’s random sample always contains at least one sample element, (c) all potential purchasers of the asset interpret the seller’s disclosure or nondisclosure in the same way, and (d) disclosure of any or all of the seller’s sample information generates no proprietary costs, we show that in equilibrium there is a positive probability that the seller will make no disclosure at all, and that, when the seller makes no disclosure, the nondisclosed information is not the worst possible sample information the seller could have had about the asset’s value. These results are contrasted with the “unravelling” result of [Grossman [1981]], [Grossman and Hart [1980]], and [Milgrom [1981]]. We show that, were potential purchasers of the asset to know the size of the seller’s random sample, “unravelling” (i.e., full disclosure) would occur. We conclude that the randomness of the seller’s sample size is key to determining the seller’s equilibrium voluntary disclosure strategy.

Suggested Citation

  • Ronald A. Dye & Mark Finn, 2007. "Equilibrium Voluntary Disclosures when Firms Possess Random Multi-Dimensional Private Information," Springer Books, in: Rick Antle & Frøystein Gjesdal & Pierre Jinghong Liang (ed.), Essays in Accounting Theory in Honour of Joel S. Demski, chapter 0, pages 53-72, Springer.
  • Handle: RePEc:spr:sprchp:978-0-387-30399-4_3
    DOI: 10.1007/978-0-387-30399-4_3
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. David Butler & Daniel Read, 2021. "Unravelling Theory: Strategic (Non-) Disclosure of Online Ratings," Games, MDPI, vol. 12(4), pages 1-20, September.

    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:spr:sprchp:978-0-387-30399-4_3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.