IDEAS home Printed from https://ideas.repec.org/a/eee/reveco/v103y2025ics1059056025005970.html
   My bibliography  Save this article

Digital tax administration, investor risk perception, and stock return volatility

Author

Listed:
  • Tu, Wenjun
  • Du, Anna Min
  • Saddler, Sarah Borthwick

Abstract

This study investigates the impact of digital tax administration on stock return volatility amid heightened market uncertainty. Leveraging the staggered rollout of China's Golden Tax Phase III from 2013 to 2016 as a quasi-natural experiment, we employ a Difference-in-Differences approach on a sample of Chinese listed firms over the period 2010 to 2022. We find that the implementation of digital tax administration significantly alleviates stock return volatility. This stabilizing effect is primarily attributed to reduced investors' perceived risks, which arise from enhanced information transparency, mitigated agency conflicts, and alleviated financial constraints. Drawing upon agency theory and institutional theory, we find that this stabilizing effect is more salient in regions with weaker initial institutional environments (i.e., low tax enforcement efforts, weak legal institutions, and low social trust). Our findings indicate that digital tax administration, as a potent external governance mechanism, can compensate for existing institutional deficiencies. Our study provides crucial insights into the role of digital tax administration in promoting market stability and enhancing corporate governance across diverse regional contexts. Our study is important for both policymakers and firm managers, indicating that leveraging digital tax administration can enhance stock market stability by improving regulatory effectiveness and guiding firms' strategic resource allocation in volatile environments.

Suggested Citation

  • Tu, Wenjun & Du, Anna Min & Saddler, Sarah Borthwick, 2025. "Digital tax administration, investor risk perception, and stock return volatility," International Review of Economics & Finance, Elsevier, vol. 103(C).
  • Handle: RePEc:eee:reveco:v:103:y:2025:i:c:s1059056025005970
    DOI: 10.1016/j.iref.2025.104434
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1059056025005970
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.iref.2025.104434?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

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:reveco:v:103:y:2025:i:c:s1059056025005970. 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/inca/620165 .

    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.