IDEAS home Printed from https://ideas.repec.org/a/eme/parpps/par-04-2016-0044.html
   My bibliography  Save this article

Detecting fraud in Chinese listed company balance sheets

Author

Listed:
  • Yi Wei
  • Jianguo Chen
  • Carolyn Wirth

Abstract

Purpose - This paper aims to investigate the links between accounting values in Chinese listed companies’ balance sheets and the exposure of their fraudulent activities. Design/methodology/approach - Every balance sheet account is proposed to be a potential vehicle to manipulate financial statements. Findings - Other receivables, inventories, prepaid expenses, employee benefits payables and long-term payables are important indicators of fraudulent financial statements. These results confirm that asset account manipulation is frequently carried out and cast doubt on earlier conclusions by researchers that inflation of liabilities is the most common source of financial statement manipulation. Originality/value - Previous practices of solely scaling balance sheet values by assets are revealed to produce spurious relationships, while scaling by both assets and sales effectively detects fraudulent financial statements and provides a useful fraud prediction tool for Chinese auditors, regulators and investors.

Suggested Citation

  • Yi Wei & Jianguo Chen & Carolyn Wirth, 2017. "Detecting fraud in Chinese listed company balance sheets," Pacific Accounting Review, Emerald Group Publishing Limited, vol. 29(3), pages 356-379, August.
    • Handle: RePEc:eme:parpps:par-04-2016-0044
    DOI: 10.1108/PAR-04-2016-0044
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/PAR-04-2016-0044/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://www.emerald.com/insight/content/doi/10.1108/PAR-04-2016-0044/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1108/PAR-04-2016-0044?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. Achakzai, Muhammad Atif Khan & Peng, Juan, 2023. "Detecting financial statement fraud using dynamic ensemble machine learning," International Review of Financial Analysis, Elsevier, vol. 89(C).
    2. Zhou, Ying & Li, Haoran & Xiao, Zhi & Qiu, Jing, 2023. "A user-centered explainable artificial intelligence approach for financial fraud detection," Finance Research Letters, Elsevier, vol. 58(PA).
    3. Maria Tragouda & Michalis Doumpos & Constantin Zopounidis, 2024. "Identification of fraudulent financial statements through a multi‐label classification approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.

    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:eme:parpps:par-04-2016-0044. 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: Emerald Support (email available below). General contact details of provider: .

    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.