Financial statement errors: evidence
from the distributional properties of financial statement
numbers
Dan Amiram
1
•
Zahn Bozanic
2
•
Ethan Rouen
1
Published online: 2 August 2015
Ó Springer Science+Business Media New York 2015
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.
& Zahn Bozanic
bozanic.1@fisher.osu.edu
Dan Amiram
da2477@columbia.edu
Ethan Rouen
ethanrouen@columbia.edu
1
Columbia Business School, Columbia University, New York, NY, USA
2
Fisher College of Business, The Ohio State University, Columbus, OH, USA
123
Rev Account Stud (2015) 20:1540–1593
DOI 10.1007/s11142-015-9333-z
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