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How to better detect cases of financial reporting fraud: some new findings from earnings restatements

In: Research Handbook of Investing in the Triple Bottom Line

Author

Listed:
  • François Aubert
  • Jean-François Gajewski
  • Lamya Kermiche

Abstract

This chapter examines the ability of the managed earnings component metric (MEC) and discretionary accrual-based models to detect financial reporting fraud. By using a comprehensive sample of Accounting and Auditing Enforcement Releases (AAERs) issued by the SEC during the reporting period January 1, 1993 to December 31, 2014, we show that using the innovative managed earnings component metric of a firm as an independent variable (Aubert and Grudnitski, 2012, 2014) – appears to be a more relevant indicator of accounting fraud than cross sectional models based on inspecting abnormal accruals estimated in the literature (Dechow et al., 2011; Hui et al., 2014). Overall, our findings contribute to substantially enhancing prior literature on corporate earnings materially misstated and should be of great interest for regulatory bodies interested in identifying and preventing voluntary financial misreporting such as the US Security and Exchange Commission, which has been continually improving its Accounting Quality Model (AQM or Robocop) fraud detection tool for many years.

Suggested Citation

  • François Aubert & Jean-François Gajewski & Lamya Kermiche, 2018. "How to better detect cases of financial reporting fraud: some new findings from earnings restatements," Chapters, in: Sabri Boubaker & Douglas Cumming & Duc K. Nguyen (ed.), Research Handbook of Investing in the Triple Bottom Line, chapter 2, pages 29-52, Edward Elgar Publishing.
  • Handle: RePEc:elg:eechap:17813_2
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