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Modelling Receivables and Deferred Revenues to Detect Revenue Management

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  • Jenny Zha Giedt

Abstract

This study develops and calibrates a revenue accruals model. Changes in accounts receivable and deferred revenues are modelled using the respective income statement and cash flow numbers (i.e., revenues and cash flows from sales) that relate directly to the accruals’ origination and reversal. Compared to existing models, the proposed specification explains more variation in the data and, in simulations with seeded revenue manipulation, exhibits greater detection power and less bias. Furthermore, the abnormal revenue estimates are positively associated with cases of revenue misstatements identified by the Securities and Exchange Commission. Results imply that researchers, auditors, and regulators interested to detect earnings management should focus on modelling specific accruals. As a practical matter, to detect revenue management, they should consider broadening their scope to examine not only accounts receivable but also current and long†term deferred revenues.

Suggested Citation

  • Jenny Zha Giedt, 2018. "Modelling Receivables and Deferred Revenues to Detect Revenue Management," Abacus, Accounting Foundation, University of Sydney, vol. 54(2), pages 181-209, June.
    • Handle: RePEc:bla:abacus:v:54:y:2018:i:2:p:181-209
    DOI: 10.1111/abac.12119
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    References listed on IDEAS

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    1. Carol A. Marquardt & Christine I. Wiedman, 2004. "How Are Earnings Managed? An Examination of Specific Accruals," Contemporary Accounting Research, John Wiley & Sons, vol. 21(2), pages 461-491, June.
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    Cited by:

    1. Maureen F. McNichols & Stephen R. Stubben, 2018. "Research Design Issues in Studies Using Discretionary Accruals," Abacus, Accounting Foundation, University of Sydney, vol. 54(2), pages 227-246, June.
    2. A‐Reum Jung & Do‐Jin Jung, 2022. "The effects of size‐dependent policy on the sales distortion reporting: Focusing on the discretionary sales management of Korean SMEs," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 43(2), pages 301-320, March.
    3. Andrzej Piosik, 2021. "Revenue Identification in Attaining Consensus Estimates on Income Predictions: The Function of Ownership Concentration and Managerial Ownership Confirmation from Poland," Sustainability, MDPI, vol. 13(23), pages 1-16, December.
    4. Véronique Darmendrail & Frederic Pourtier, 2021. "La mise en équivalence dans les comptes consolidés permet-elle la manipulation du chiffre d’affaires et du résultat consolidés ? : le cas des groupes cotés en Europe," Post-Print hal-03361453, HAL.
    5. Stewart Jones & Nurul Alam, 2019. "A machine learning analysis of citation impact among selected Pacific Basin journals," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 59(4), pages 2509-2552, December.

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