IDEAS home Printed from https://ideas.repec.org/a/taf/ufajxx/v72y2016i1p22-35.html
   My bibliography  Save this article

The Misrepresentation of Earnings

Author

Listed:
  • Ilia Dichev
  • John Graham
  • Campbell R. Harvey
  • Shiva Rajgopal

Abstract

The authors conducted a survey of nearly 400 chief financial officers on the definition and drivers of earnings quality, with an emphasis on the prevalence and detection of earnings misrepresentation. The respondents believe that the hallmarks of earnings quality are sustainability, absence of one-time items, and backing by actual cash flows. However, they also believe that in any given period, a remarkable 20% of companies intentionally distort earnings, even while adhering to GAAP. The magnitude of the misrepresentation is large: 10% of reported earnings.The summary was prepared by Marla Howard, CFA, University of Maryland University College.What’s Inside?The authors explore the definition of earnings quality as described by chief financial officers (CFOs), the producers of earnings quality. Earnings quality is characterized by sustainability in profits, the absence of one-time items, and backing by actual cash flows. The CFOs surveyed believe that, in any given period, 20% of public companies and 30% of private companies distort earnings, and the magnitude of the misrepresentation is at least 10% of reported earnings.How Is This Research Useful to Practitioners?With a deeper understanding of the factors that determine earnings quality and awareness of the red flags of misrepresented earnings, financial analysts and investors can temper their reliance on certain financial information that shows signs of potential misrepresentation.The CFOs surveyed identified characteristics of high-quality earnings, such as sustainability and predictability of future earnings, accruals that are reflected as cash flows, consistent reporting choices over time, and avoidance of long-term estimates for which assumptions can be unreliable and subject to interpretation. CFOs believe that earnings quality is determined equally by controllable factors (internal controls and corporate governance) and non-controllable factors (industry membership and macroeconomic conditions). The determinant of earnings quality most agreed on by CFOs is the business model of the company. The authors point out that this link between fundamentals and earnings quality is not appreciated enough.CFOs face internal and external pressures to smooth earnings and meet earnings benchmarks. They also protect their own career and compensation, fearing adverse consequences if earnings targets are not met. The CFOs surveyed believe that 20% of public companies and 30% of private companies use discretion within GAAP (generally accepted accounting principles) to report earnings that misrepresent the economic performance of the company and that the extent of the misrepresentation is around 10% of reported earnings for public companies and even higher for private companies. It is interesting that the CFOs think that one in three cases involves understating reported earnings.Earnings misrepresentation is difficult to detect. The surveyed CFOs presented a list of red flags that includes lack of correlation between earnings and cash flow from operations, deviation from industry or peer norms, consistently meeting or beating earnings targets, large or frequent one-time or special items, and a lot of accruals. The CFOs also provided some specific areas that lend themselves to earnings management, such as acquisition and pension accounting, the use of subsidiaries and off-balance-sheet entities, and tax accruals.The CFOs emphasized the importance of managers, audit teams, assumptions used in estimates, and clear and open disclosures to help avoid earnings misrepresentation.How Did the Authors Conduct This Research?Past research about earnings misrepresentation has been based on published financial information. The authors go straight to the producers of earnings quality for their insights about intentional misrepresentation of earnings. They interviewed 12 CFOs and surveyed 375 CFOs (169 from public companies and 206 from private companies).The CFOs anonymously answered questions about factors that influence earnings quality; the extent, magnitude, and direction of earnings misrepresentation; and the motivations of CFOs to use earnings to misrepresent economic performance. The CFOs also provided lists of potential indicators of earnings misrepresentation for investors and financial analysts to monitor. The authors summarize survey responses and provide excerpts from interviews with CFOs.Although the interviews supported the survey results, the authors did not indicate how they selected the 12 CFOs for personal interviews. Knowing that the 12 CFOs are representative of the CFO population strengthens the ability of the survey results to be generalized.Abstractor’s ViewpointAlthough the survey and interview methodologies provide the inside view from CFOs with firsthand knowledge about managerial intent regarding earnings decisions, CFO judgment about the misrepresentation of reported earnings can be subjective. A reader may wonder whether earnings misrepresentation is more widespread than the CFOs believe or whether they may exaggerate or extrapolate based on isolated instances. Future research seeking viewpoints from auditors, board audit committees, accounting standards setters, and regulators may be helpful.Editor’s note: This article was reviewed and accepted by Executive Editor Robert Litterman.Authors’ note: This article is an augmented version of “Earnings Quality: Evidence from the Field,” published in the Journal of Accounting and Economics, vol. 56, no. 2–3 (Supplement 1, 15 December 2013): 1–33. We have added additional interviews with chief financial officers and present results that are not contained in our earlier work.

Suggested Citation

  • Ilia Dichev & John Graham & Campbell R. Harvey & Shiva Rajgopal, 2016. "The Misrepresentation of Earnings," Financial Analysts Journal, Taylor & Francis Journals, vol. 72(1), pages 22-35, January.
  • Handle: RePEc:taf:ufajxx:v:72:y:2016:i:1:p:22-35
    DOI: 10.2469/faj.v72.n1.4
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.2469/faj.v72.n1.4
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.2469/faj.v72.n1.4?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:ufajxx:v:72:y:2016:i:1:p:22-35. 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/ufaj20 .

    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.