IDEAS home Printed from
   My bibliography  Save this article

Multivariate probit model for a priori assessment of behavioral risks in audit


  • Arzhenovskiy, Sergey

    (Rostov State University of Economics, Rostov-on-Don, Russian Federation)

  • Sinyavskaya, Tatiana

    (Rostov State University of Economics, Rostov-on-Don, Russian Federation)

  • Bakhteev, Andrey

    (Rostov State University of Economics, Rostov-on-Don, Russian Federation)


The paper presents an original approach to assessing behavioral risks during audit procedures based on a multivariate probit model. Dependent variables in the model were binary behavioral characteristics of individual responsible for financial statement: tolerance to legislation violations, pathological monetary type, propensity to increased risk, belief in impunity, and illiteracy in accounting legislation. It is found that the same factors tend to increase the chances of having one and reduce the chances of having another characteristic, which does not allow us to formulate the “highest risk” profile. The results can be used by auditors in the procedure of assessing the risks of falsification of financial statement.

Suggested Citation

  • Arzhenovskiy, Sergey & Sinyavskaya, Tatiana & Bakhteev, Andrey, 2020. "Multivariate probit model for a priori assessment of behavioral risks in audit," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 60, pages 102-114.
  • Handle: RePEc:ris:apltrx:0409

    Download full text from publisher

    File URL:
    File Function: Full text
    Download Restriction: no

    References listed on IDEAS

    1. Clinton Free, 2015. "Looking through the fraud triangle: a review and call for new directions," Meditari Accountancy Research, Emerald Group Publishing, vol. 23(2), pages 175-196, August.
    2. Jones, Jj, 1991. "Earnings Management During Import Relief Investigations," Journal of Accounting Research, Wiley Blackwell, vol. 29(2), pages 193-228.
    3. Young, Gary & Valdez, Emiliano A. & Kohn, Robert, 2009. "Multivariate probit models for conditional claim-types," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 214-228, April.
    4. Patricia M. Dechow & Weili Ge & Chad R. Larson & Richard G. Sloan, 2011. "Predicting Material Accounting Misstatements," Contemporary Accounting Research, John Wiley & Sons, vol. 28(1), pages 17-82, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shivaram Rajgopal & Suraj Srinivasan & Xin Zheng, 2021. "Measuring audit quality," Review of Accounting Studies, Springer, vol. 26(2), pages 559-619, June.
    2. Filip, Andrei & Huang, Zhongwei & Lui, Daphne, 2020. "Cross-listing and corporate malfeasance: Evidence from P-chip firms," Journal of Corporate Finance, Elsevier, vol. 63(C).
    3. Theoharry Grammatikos & Nikolaos I. Papanikolaou, 2021. "Applying Benford’s Law to Detect Accounting Data Manipulation in the Banking Industry," Journal of Financial Services Research, Springer;Western Finance Association, vol. 59(1), pages 115-142, April.
    4. David F. Larcker & Anastasia A. Zakolyukina, 2012. "Detecting Deceptive Discussions in Conference Calls," Journal of Accounting Research, Wiley Blackwell, vol. 50(2), pages 495-540, May.
    5. Patricia M. Dechow & Amy P. Hutton & Jung Hoon Kim & Richard G. Sloan, 2012. "Detecting Earnings Management: A New Approach," Journal of Accounting Research, Wiley Blackwell, vol. 50(2), pages 275-334, May.
    6. Brandon Gipper & Luzi Hail & Christian Leuz, 2017. "On the Economics of Audit Partner Tenure and Rotation: Evidence from PCAOB Data," NBER Working Papers 24018, National Bureau of Economic Research, Inc.
    7. Minh, Man Duc Binh & Van Cuong, Dinh & Linh, Nguyen Thi Linh & Ho, Manh-Toan, 2019. "Xây dựng mô hình phát hiện gian lận trong báo cáo tài chính của các công ty tại Việt Nam," OSF Preprints kecmv, Center for Open Science.
    8. Andrew B. Jackson, 2018. "Discretionary Accruals: Earnings Management ... or Not?," Abacus, Accounting Foundation, University of Sydney, vol. 54(2), pages 136-153, June.
    9. Peng, Qiyuan & Yin, Sirui, 2021. "Does the executive labor market discipline? Labor market incentives and earnings management," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 62-86.
    10. Callen, Jeffrey L. & Fang, Xiaohua & Zhang, Wenjun, 2020. "Protection of proprietary information and financial reporting opacity: Evidence from a natural experiment," Journal of Corporate Finance, Elsevier, vol. 64(C).
    11. Beck, Matthew J. & Gunn, Joshua L. & Hallman, Nicholas, 2019. "The geographic decentralization of audit firms and audit quality," Journal of Accounting and Economics, Elsevier, vol. 68(1).
    12. Stephen Terry & Anastasia Zakolyukina & Toni Whited, 2018. "Information Distortion, R&D, and Growth," 2018 Meeting Papers 217, Society for Economic Dynamics.
    13. Tri Tri Nguyen & Chau Minh Duong & Sunitha Narendran, 2021. "CEO profile and earnings quality," Review of Quantitative Finance and Accounting, Springer, vol. 56(3), pages 987-1025, April.
    14. Stephen J. Terry & Toni M. Whited & Anastasia A. Zakolyukina, 2020. "Information versus Investment," Working Papers 2020-110, Becker Friedman Institute for Research In Economics.
    15. Tahir, Muhammad & Ibrahim, Salma & Nurullah, Mohamed, 2019. "Getting compensation right - The choice of performance measures in CEO bonus contracts and earnings management," The British Accounting Review, Elsevier, vol. 51(2), pages 148-169.
    16. Jeff L. McMullin & Bryce Schonberger, 2020. "Entropy-balanced accruals," Review of Accounting Studies, Springer, vol. 25(1), pages 84-119, March.
    17. Dain C. Donelson & Christopher G. Yust, 2014. "Litigation Risk and Agency Costs: Evidence from Nevada Corporate Law," Journal of Law and Economics, University of Chicago Press, vol. 57(3), pages 747-780.
    18. Andrey Vladimirovich Bakhteev & Sergey Valentinovich Arzhenovskiy & Natalya Nikolayevna Khakhonova & Yelena Vyacheslavovna Kuznetsova, 2017. "Use of Regression Models when Performing Fraud Risk Assessment Procedures in the Audit Process," European Research Studies Journal, European Research Studies Journal, vol. 0(3B), pages 22-33.
    19. Berrill, Jenny & Campa, Domenico & O'Hagan-Luff, Martha, 2021. "Firm diversification and earnings management strategies: European evidence," International Review of Financial Analysis, Elsevier, vol. 78(C).
    20. Bradley, Daniel & Gokkaya, Sinan & Liu, Xi & Xie, Fei, 2017. "Are all analysts created equal? Industry expertise and monitoring effectiveness of financial analysts," Journal of Accounting and Economics, Elsevier, vol. 63(2), pages 179-206.

    More about this item


    multivariate probit; endogeneity; behavioral characteristics; risk of financial statement falsification;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • M42 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Auditing


    Access and download statistics


    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:ris:apltrx:0409. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: .

    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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Anatoly Peresetsky (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 hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.