ADVANCED STATISTICAL TECHNIQUES FOR TESTING BENFORD'S LAW

Rabeea Sadaf ()
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Rabeea Sadaf: Karoly Ihrig Doctoral School of Management and Business University of Debrecen, Hungary

Annals of Faculty of Economics, 2017, vol. 1, issue 2, 229-238

Abstract: The frequency of accounting data frauds has been increased in corporate environment. As a result of that, the research on detection of such irregularities in accounting and auditing is gaining researchers’ focus. Bedford’s law has been in the literature for the identification of data manipulation in accounting and auditing field. The application of this law in accounting fraud detection started in 1988 after the work of Carslaw (he observed a greater frequency of zeros and less frequency of nines in the second place in the reported earning numbers). The underlying idea about this technique is based on comparison of certain digit frequency to the expected digit pattern proposed by Bedford’s law. Various goodness-of-fit test are used to analyze the data conformity to Benford’s law based on a null hypothesis of conformity of empirical data to expected data pattern. This study addresses some of the most important goodness-of fit tests that can be used to analyze data pattern and digit behavior. Most importantly chi-square, Kolmogorov-Smirnov test (KS), Euclidean distance, Joenssen’s JP-square, Freedman-Watson u-square, Chebyshev distance, Z-statistics and mean absolute deviation tests are discussed with expression to calculate test statistics. Tests like Chi-Square and KS are also sensitive to size of data set, so a combination of various goodness-of fit test is recommended in literature to make more accurate analysis of data conformity.

Keywords: Benford’s Law; accounting and auditing; Goodness-of-fit; Conformity; Digit pattern (search for similar items in EconPapers)
JEL-codes: C58 M40 M41 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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