IDEAS home Printed from https://ideas.repec.org/a/wly/ijfiec/v31y2026i3p3360-3383.html

Investor Site Visits, Discussion Contents, and Analyst Forecasts: A Machine Learning Approach

Author

Listed:
  • Jinyu Liang
  • Xiaogang Bi

Abstract

We use machine learning to perform a textual analysis of 423,361 Q&As (questions and answers) involved in investor site‐visit reports, in order to explore the impact of information conveyed from them on analysts' forecast errors and revisions. We find that more performance‐ and operations‐related Q&A content discussed during site visits significantly reduces analysts' forecast errors and makes them have a lower degree of revisions. Furthermore, these relations are more pronounced when there are more institutional participants in the site visit. The results remain consistent after addressing endogeneity issues and using alternative calculations for an abnormally larger number of Q&As. Our paper supports the information digesting channel hypothesis of institutional investors and finds that questions raised by them are beneficial to analysts, no matter whether they participate in site visits, due to the timely and accurately conveying information to outside investors.

Suggested Citation

  • Jinyu Liang & Xiaogang Bi, 2026. "Investor Site Visits, Discussion Contents, and Analyst Forecasts: A Machine Learning Approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 31(3), pages 3360-3383, July.
    • Handle: RePEc:wly:ijfiec:v:31:y:2026:i:3:p:3360-3383
    DOI: 10.1002/ijfe.70094
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/ijfe.70094
    Download Restriction: no
    File URL: https://libkey.io/10.1002/ijfe.70094?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
    ---><---

    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:wly:ijfiec:v:31:y:2026:i:3:p:3360-3383. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1076-9307/ .

    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.