IDEAS home Printed from https://ideas.repec.org/a/taf/euract/v29y2020i5p877-900.html
   My bibliography  Save this article

Hierarchy and Performance of Analyst Teams

Author

Listed:
  • Wen He
  • Andrew B. Jackson
  • Chao Kevin Li

Abstract

We examine the effect of hierarchy on analyst teams’ performance using a large sample of financial analysts from China. Hierarchy, defined as the disparity in power or status within a group, which we operationalize as the difference in experience between the senior and junior analyst in a team, is an important aspect of team structure that could affect team performance. We find that analyst teams with a hierarchy outperform flat teams (teams without a clear hierarchy). Specifically, hierarchical teams issue forecasts with higher accuracy, less optimism bias, less co-movement with the consensus, and stronger investor reactions. The results remain robust after we control for a number of firm and analyst characteristics and fixed effects. Further analysis shows that working in a hierarchical team helps junior analysts improve their individual forecasts for other firms, and senior analysts also benefit from working with junior analysts in hierarchical teams. Our results provide important insights into understanding the effect of team structure on the performance of analyst teams who issue majority of earnings forecasts.

Suggested Citation

  • Wen He & Andrew B. Jackson & Chao Kevin Li, 2020. "Hierarchy and Performance of Analyst Teams," European Accounting Review, Taylor & Francis Journals, vol. 29(5), pages 877-900, October.
  • Handle: RePEc:taf:euract:v:29:y:2020:i:5:p:877-900
    DOI: 10.1080/09638180.2020.1714460
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/09638180.2020.1714460
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/09638180.2020.1714460?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. Jiang, Shuai & Guo, Yanhong & Zhou, Wenjun & Li, Xianneng, 2023. "Identifying predictors of analyst rating quality: An ensemble feature selection approach," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1853-1873.

    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:29:y:2020:i:5:p:877-900. 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.