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Can Accrual-based Metrics Indicate Material Accounting Misstatements? Evidence on Audit Adjustments

Author

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  • Slavko ?odan

    (Faculty of Economics, Business and Tourism, University of Split)

Abstract

This paper analyses the usefulness of various accrual-based measures in detecting material accounting misstatements. Predicting financial misstatements has been of great interest not only to researchers but also to investors, auditors, financial analysts and regulators. However, since pre-audit financial statements are usually not available, prior research is mainly based only on samples of Securities and Exchange Commission (SEC) enforcement actions. We exploit almost unique institutional framework in which Croatian listed companies are required to publish both pre-audit and audited annual financial statements. Therefore, we are able to investigate which pre-audit financial indicators are significantly related to accounting misstatements detected by independent statutory audit, i.e. audit adjustments. Using a sample of 437 pairs of pre-audit and audited financial statements during 2009-2018, we find that financial statements with high value of absolute residuals from the Dechow-Dichev accrual model modified by McNichols (2002) are more likely to contain material misstatements of net income, working capital or fixed assets.Funding: This work has been partially supported by Croatian Science Foundation under the project IP-2020-02-9372 ?Disentangling Financial Reporting Quality?.

Suggested Citation

  • Slavko ?odan, 0000. "Can Accrual-based Metrics Indicate Material Accounting Misstatements? Evidence on Audit Adjustments," Proceedings of Economics and Finance Conferences 14416287, International Institute of Social and Economic Sciences.
    • Handle: RePEc:sek:iefpro:14416287
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    References listed on IDEAS

    as
    1. Patricia M. Dechow & Richard G. Sloan & Amy P. Sweeney, 1996. "Causes and Consequences of Earnings Manipulation: An Analysis of Firms Subject to Enforcement Actions by the SEC," Contemporary Accounting Research, John Wiley & Sons, vol. 13(1), pages 1-36, March.
    2. Feng Xu & Zinan Zhu, 2014. "A Bayesian approach for predicting material accounting misstatements," Asia-Pacific Journal of Accounting & Economics, Taylor & Francis Journals, vol. 21(4), pages 349-367, December.
    3. 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.
    4. Jeremy Bertomeu & Edwige Cheynel & Eric Floyd & Wenqiang Pan, 2021. "Using machine learning to detect misstatements," Review of Accounting Studies, Springer, vol. 26(2), pages 468-519, June.
    5. Messod D. Beneish, 1999. "The Detection of Earnings Manipulation," Financial Analysts Journal, Taylor & Francis Journals, vol. 55(5), pages 24-36, September.
    6. Beneish, Messod D., 1997. "Detecting GAAP violation: implications for assessing earnings management among firms with extreme financial performance," Journal of Accounting and Public Policy, Elsevier, vol. 16(3), pages 271-309.
    7. Mingzi Song & Naoto Oshiro & Akinobu Shuto, 2016. "Predicting Accounting Fraud: Evidence from Japan," The Japanese Accounting Review, Research Institute for Economics & Business Administration, Kobe University, vol. 6, pages 17-63, December.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Audit adjustments; Pre-audit financial statements; Earnings quality; Accounting misstatements;
    All these keywords.

    JEL classification:

    • M41 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Accounting
    • M42 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - Auditing

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