Financial statement errors: evidence from the distributional properties of financial statement numbers

Financial statement errors: evidence from the distributional properties of financial statement... Motivated by methods used to evaluate the quality of data, we create a novel firm-year measure to estimate the level of error in financial statements. The measure, which has several conceptual and statistical advantages over available alternatives, assesses the extent to which features of the distribution of a firm’s financial statement numbers diverge from a theoretical distribution posited by Benford’s Law. After providing intuition for the theory underlying the measure, we use numerical methods to demonstrate that certain error types in financial statement numbers increase the deviation from the theoretical distribution. We corroborate the numerical analysis with simulation analysis that reveals that the introduction of errors to reported revenue also increases the deviation. We then provide empirical evidence that the measure captures financial statement data quality. We first show the measure’s association with commonly used measures of accruals-based earnings management and earnings manipulation. Next, we demonstrate that (1) restated financial statements more closely conform to Benford’s Law than the misstated versions in the same firm-year and (2) as divergence from Benford’s Law increases, earnings persistence decreases. Finally, we show that our measure predicts material misstatements as identified by SEC Accounting and Auditing Enforcement Releases and can be used as a leading indicator to identify misstatements. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Review of Accounting Studies Springer Journals

Financial statement errors: evidence from the distributional properties of financial statement numbers

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Publisher
Springer US
Copyright
Copyright © 2015 by Springer Science+Business Media New York
Subject
Business and Management; Accounting/Auditing; Corporate Finance; Public Finance & Economics
ISSN
1380-6653
eISSN
1573-7136
D.O.I.
10.1007/s11142-015-9333-z
Publisher site
See Article on Publisher Site

Abstract

Motivated by methods used to evaluate the quality of data, we create a novel firm-year measure to estimate the level of error in financial statements. The measure, which has several conceptual and statistical advantages over available alternatives, assesses the extent to which features of the distribution of a firm’s financial statement numbers diverge from a theoretical distribution posited by Benford’s Law. After providing intuition for the theory underlying the measure, we use numerical methods to demonstrate that certain error types in financial statement numbers increase the deviation from the theoretical distribution. We corroborate the numerical analysis with simulation analysis that reveals that the introduction of errors to reported revenue also increases the deviation. We then provide empirical evidence that the measure captures financial statement data quality. We first show the measure’s association with commonly used measures of accruals-based earnings management and earnings manipulation. Next, we demonstrate that (1) restated financial statements more closely conform to Benford’s Law than the misstated versions in the same firm-year and (2) as divergence from Benford’s Law increases, earnings persistence decreases. Finally, we show that our measure predicts material misstatements as identified by SEC Accounting and Auditing Enforcement Releases and can be used as a leading indicator to identify misstatements.

Journal

Review of Accounting StudiesSpringer Journals

Published: Aug 2, 2015

References

  • Predicting material accounting misstatements
    Dechow, P; Ge, W; Larson, C; Sloan, R
  • Understanding earnings quality: A review of the proxies, their determinants and their consequences
    Dechow, P; Ge, W; Schrand, C
  • The market pricing of accruals quality
    Francis, J; LaFond, R; Olsson, P; Schipper, K
  • Performance matched discretionary accrual measures
    Kothari, SP; Leone, A; Wasley, C
  • Annual report readability, current earnings, and earnings persistence
    Li, F

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