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The Difference between Earnings and Operating Cash Flow as an Indicator of Financial Reporting Fraud

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  • THOMAS A. LEE
  • ROBERT W. INGRAM
  • THOMAS P. HOWARD

Abstract

This paper examines the relation between earnings and operating cash flow to derive and test an indicator of financial statement fraud. Accrual measurement concepts indicate that financial statement fraud should be associated with high levels of earnings relative to operating cash flow. We demonstrate that the excess of earnings over operating cash flow is extreme in most fraud cases in years immediately prior to the fraud discovery based on a sample of 56 fraud cases from 1978 to 1991. We compare the distribution of the earnings minus operating cash flow variable for fraud firms with that for a sample of 60,453 firm†years for firms listed on COMPUSTAT. We test a logistic regression model in which the discovery/nondiscovery of fraud is the dependent variable, and earnings minus operating cash flow is the explanatory variable. Other control variables are included in the model based on prior studies. Results are consistent with expectations derived from accrual measurement theory. We then examine the predictive ability of the model using our sample of fraud firms and a sample of nonfraud firms in the same four†digit SIC code industries. Observations for the fraud firms are for the fiscal year prior to the discovery of fraud. Observations for the nonfraud firms are for the same fiscal years as the fraud firms in the same industries. The predictive ability of the model, including the excess of earnings over operating cash flow, is substantially higher than the predictive ability of the model omitting this variable. We conclude that the earnings†operating cash flow relation provides important information for those interested in identifying financial statement fraud, especially when considered in conjunction with other factors associated with fraud risk.

Suggested Citation

  • Thomas A. Lee & Robert W. Ingram & Thomas P. Howard, 1999. "The Difference between Earnings and Operating Cash Flow as an Indicator of Financial Reporting Fraud," Contemporary Accounting Research, John Wiley & Sons, vol. 16(4), pages 749-786, December.
    • Handle: RePEc:wly:coacre:v:16:y:1999:i:4:p:749-786
    DOI: 10.1111/j.1911-3846.1999.tb00603.x
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    1. Joseph F. Brazel & Keith L. Jones & Mark F. Zimbelman, 2009. "Using Nonfinancial Measures to Assess Fraud Risk," Journal of Accounting Research, Wiley Blackwell, vol. 47(5), pages 1135-1166, December.
    2. Ozili, Peterson K, 2018. "Advances and Issues in Fraud Research: A Commentary," MPRA Paper 84879, University Library of Munich, Germany.
    3. Adrian Gepp & Kuldeep Kumar & Sukanto Bhattacharya, 2021. "Lifting the numbers game: identifying key input variables and a best‐performing model to detect financial statement fraud," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(3), pages 4601-4638, September.
    4. Yves Mard, 2005. "Vers Une Information Comptable Plus Transparente : L'Apport Des Recherches Portant Sur La Gestion Des Résultats Comptables," Post-Print halshs-00581229, HAL.
    5. Abdullah Albizri & Deniz Appelbaum & Nicholas Rizzotto, 2019. "Evaluation of financial statements fraud detection research: a multi-disciplinary analysis," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 16(4), pages 206-241, December.
    6. Harris, David G. & Shi, Linna & Xie, Hong, 2018. "Does benchmark-beating detect earnings management? Evidence from accounting irregularities," Advances in accounting, Elsevier, vol. 41(C), pages 25-45.
    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.
    8. Shiyou Li & Emeka Nwaeze & Jennifer Yin, 2016. "Earnings management in the electric utility industry: profit incentives," Review of Quantitative Finance and Accounting, Springer, vol. 46(3), pages 633-660, April.
    9. Bethany Hoogs & Thomas Kiehl & Christina Lacomb & Deniz Senturk, 2007. "A genetic algorithm approach to detecting temporal patterns indicative of financial statement fraud," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 15(1‐2), pages 41-56, January.
    10. Wenxia Ge & G. Whitmore, 2010. "Binary response and logistic regression in recent accounting research publications: a methodological note," Review of Quantitative Finance and Accounting, Springer, vol. 34(1), pages 81-93, January.
    11. Joseph F. Brazel & Lorenzo Lucianetti & Tammie J. Schaefer, 2021. "Reporting Concerns About Earnings Quality: An Examination of Corporate Managers," Journal of Business Ethics, Springer, vol. 171(3), pages 435-457, July.
    12. Yuan, Shuai & Lan, Hao & Seufert, Juergen H., 2020. "FRC intervention, financial reporting quality and due diligence," Research in International Business and Finance, Elsevier, vol. 52(C).
    13. C.S. Agnes Cheng & Simon S.M. Yang, 2003. "The Incremental Information Content of Earnings and Cash Flows from Operations Affected by Their Extremity," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 30(1‐2), pages 73-116, January.
    14. Song Mingzi & Naoto Oshiro & Akinobu Shuto, 2016. "Predicting accounting fraud: Evidence from Japan (Accepted by The Japanese Accounting Review)," CARF F-Series CARF-F-402, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    15. Sunita Goel & Jagdish Gangolly, 2012. "Beyond The Numbers: Mining The Annual Reports For Hidden Cues Indicative Of Financial Statement Fraud," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(2), pages 75-89, April.
    16. Giuseppe Ianniello, 2015. "The effects of board and auditor independence on earnings quality: evidence from Italy," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 19(1), pages 229-253, February.
    17. James S. Ang & Zhiqian Jiang & Chaopeng Wu, 2016. "Good Apples, Bad Apples: Sorting Among Chinese Companies Traded in the U.S," Journal of Business Ethics, Springer, vol. 134(4), pages 611-629, April.
    18. Chantziaras, Antonios & Dedoulis, Emmanouil & Leventis, Stergios, 2020. "The impact of labor unionization on monitoring costs," European Management Journal, Elsevier, vol. 38(2), pages 288-307.

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