IDEAS home Printed from https://ideas.repec.org/a/taf/rcjaxx/v6y2018i3p231-274.html
   My bibliography  Save this article

Firm site visits and differential information of text in analyst reports

Author

Listed:
  • Yugang Yin
  • Zheng Niu
  • Yahui Liu
  • Shuang Mou

Abstract

We quantify differential information of text in analyst reports by adopting the methodology of cosine similarity and investigate the role of analysts’ firm site visits in the differential information of their reports. We find that the analysts who conduct a visit to a firm site provide more differential information than those who do not, and this process of collecting information by a visit is affected by analyst–firm factors. Specifically, an analyst’s visit and visit frequency of a firm three months before his/her reports are issued are positively associated with the differential information of text in the reports. This effect is more pronounced among reports of worse firm information transparency, more analysts’ local advantage, and stronger analyst–firm relationships. This finding is robust with different measurements of text information, local advantage and analyst–firm relationships. Moreover, we find that the differential information of text in analyst reports through visits to firm sites is more likely to be private information rather than inside information.

Suggested Citation

  • Yugang Yin & Zheng Niu & Yahui Liu & Shuang Mou, 2018. "Firm site visits and differential information of text in analyst reports," China Journal of Accounting Studies, Taylor & Francis Journals, vol. 6(3), pages 231-274, July.
  • Handle: RePEc:taf:rcjaxx:v:6:y:2018:i:3:p:231-274
    DOI: 10.1080/21697213.2018.1543251
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/21697213.2018.1543251
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/21697213.2018.1543251?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. Tam, Lewis H.K. & Tian, Shaohua, 2023. "Language barriers, corporate site visit, and analyst forecast accuracy," The Quarterly Review of Economics and Finance, Elsevier, vol. 91(C), pages 68-83.

    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:rcjaxx:v:6:y:2018:i:3:p:231-274. 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/rcja .

    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.