IDEAS home Printed from https://ideas.repec.org/a/eee/riibaf/v70y2024ipbs0275531924001648.html
   My bibliography  Save this article

Big data analytics, firm risk and corporate policies: Evidence from China

Author

Listed:
  • Sun, Pengfei
  • Yuan, Chunhui
  • Li, Xiaolong
  • Di, Jia

Abstract

This study provides the empirical evidence on the impact of big data analytics (BDA) on firm risk. Using a combination of deep learning and text mining, we construct BDA indicators for a sample of Chinese A-share listed companies from 2003 to 2019. We find a significant positive relationship between BDA and firm risk. In addition, we identify several factors that significantly moderate the effect of BDA on firm risk. Examining the channels, we find that BDA affects corporate policy actions that lead to higher firm risk. Further, this study also shows that the improvement in BDA is value-enhancing for the firm and that this improvement is somewhat sustainable. The results of the test for endogeneity and other tests indicate that our causal relationship is robust.

Suggested Citation

  • Sun, Pengfei & Yuan, Chunhui & Li, Xiaolong & Di, Jia, 2024. "Big data analytics, firm risk and corporate policies: Evidence from China," Research in International Business and Finance, Elsevier, vol. 70(PB).
    • Handle: RePEc:eee:riibaf:v:70:y:2024:i:pb:s0275531924001648
    DOI: 10.1016/j.ribaf.2024.102371
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0275531924001648
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ribaf.2024.102371?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.

    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:riibaf:v:70:y:2024:i:pb:s0275531924001648. 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: http://www.elsevier.com/locate/ribaf .

    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.