The purpose of this paper is to introduce analyst estimates and option pricing-based variables in modeling material accounting misstatements.
The paper uses a logistic regression model to analyze a comprehensive sample of AAER and non-AAER firms listed in the USA.
By applying a cross-sectional, sequence of time-series logistic regression models, the authors find better identifiers of ex ante risk of fraud than prediction models based on an inspection of abnormal accruals. These identifiers include the managed earnings (ME) component of a firm and the change in a firm’s option contracts’ implied volatility (IV) prior to an earnings announcement.
The empirical findings contribute to an understanding of earnings manipulation (fraud) and should be of value to auditors and regulatory bodies interested in identifying financial statement fraud, particularly the Securities and Exchange Commission, which has been improving its accounting quality model (AQM or Robocop) fraud detection tool for many years. The results contribute substantially to enhancing the current accounting literature by introducing two non-accrual-based measures that significantly enhance the predictive power of an accrual-based accounting misstatement prediction model.
This paper radically departs from relying on the assumption that the clearest and easiest pathway to detect fraud reporting ex ante is through an examination of accruals. Instead, the authors use a richer source of information about the possibility of a firm’s misstatement of its financial accounting numbers, namely, analyst estimates of ex post earnings and the IV from exchange-traded option contracts.
Aubert, F., Wang, J. and Grudnitski, G. (2019), "Convergence consensus analyst earnings estimates and option pricing in modeling material accounting misstatements", Review of Accounting and Finance, Vol. 18 No. 1, pp. 134-156. https://doi.org/10.1108/RAF-05-2017-0101Download as .RIS
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