IDEAS home Printed from https://ideas.repec.org/a/taf/oaefxx/v9y2021i1p1889756.html
   My bibliography  Save this article

Predictability of financial statements fraud-risk using Benford’s Law

Author

Listed:
  • Mehdi Rad
  • Ali Amiri
  • Mohammad Hussein Ranjbar
  • Hojatollah Salari
  • David McMillan

Abstract

The main objective of this research is to investigate the Predictability of Financial Statements Fraud-Risk Using Benford’s Law on the Tehran Stock Exchange. Therefore, based on financial fraud detection criteria, a sample of 50 companies was extracted that 25 companies had fraud-risk in financial statements (experimental group) and 25 did not have fraud-risk (control group). Next, the frequency distribution of the first left digit of the numbers in the financial statements as well as the financial ratios of both groups was extracted, and their conformity with Benford’s distribution was evaluated through the chi-square test to test the research hypotheses. The comparison between the mentioned frequency distribution and Benford’s distribution showed a significant difference. The result indicates that Benford’s law cannot predict the financial statements fraud-risk of companies, in other words, Benford’s law cannot separate companies with fraud-risk from those without fraud-risk in financial statements.

Suggested Citation

  • Mehdi Rad & Ali Amiri & Mohammad Hussein Ranjbar & Hojatollah Salari & David McMillan, 2021. "Predictability of financial statements fraud-risk using Benford’s Law," Cogent Economics & Finance, Taylor & Francis Journals, vol. 9(1), pages 1889756-188, January.
    • Handle: RePEc:taf:oaefxx:v:9:y:2021:i:1:p:1889756
    DOI: 10.1080/23322039.2021.1889756
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/23322039.2021.1889756
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/23322039.2021.1889756?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

    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:oaefxx:v:9:y:2021:i:1:p:1889756. 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/OAEF20 .

    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.