IDEAS home Printed from https://ideas.repec.org/a/eee/jebusi/v127y2023ics0148619523000292.html
   My bibliography  Save this article

Board independence and analysts' forecast accuracy: R&D perspective

Author

Listed:
  • Rahman, Anisur
  • Talukdar, Bakhtear
  • Fan, Zaifeng Steve

Abstract

Research and development (R&D) activities are essential for firm growth and profitability. However, R&D activities also exacerbate information complexity in the financial markets. Therefore, the accuracy of earnings forecasts suffers when R&D expenses are high. This study aims to examine whether board independence can mitigate the adverse effect of high R&D expenditure on analysts' forecasts. Using a sample of 11,645 annual observations from 1997 to 2016, we find that board independence improves analysts' forecast accuracy for R&D-intensive firms. The improvement is more pronounced in firms with low analyst coverage and powerful CEOs. These results are robust with an alternative measure of information asymmetry, a dynamic generalized method of moments (GMM) model and a quasi-natural experiment based on the Sarbanes-Oxley Act of 2002 to address endogeneity concerns.

Suggested Citation

  • Rahman, Anisur & Talukdar, Bakhtear & Fan, Zaifeng Steve, 2023. "Board independence and analysts' forecast accuracy: R&D perspective," Journal of Economics and Business, Elsevier, vol. 127(C).
    • Handle: RePEc:eee:jebusi:v:127:y:2023:i:c:s0148619523000292
    DOI: 10.1016/j.jeconbus.2023.106136
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0148619523000292
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jeconbus.2023.106136?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.

    More about this item

    Keywords

    Board Independence; Information complexity; Analyst forecast; R&D;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G34 - Financial Economics - - Corporate Finance and Governance - - - Mergers; Acquisitions; Restructuring; Corporate Governance
    • G38 - Financial Economics - - Corporate Finance and Governance - - - Government Policy and Regulation

    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:eee:jebusi:v:127:y:2023:i:c:s0148619523000292. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-economics-and-business .

    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.