E-print available at SSRN: https://ssrn.com/abstract=3352667, March 13, 2019.
ISSN/ISBN: Not available at this time. DOI: 10.2139/ssrn.3352667
Abstract: We propose a parsimonious metric – the Adjusted Benford score (AB-score) – to improve the detection of financial misstatements. Based on Benford’s Law, which predicts the leading-digit distribution of naturally occurring numbers, the AB-score estimates a firm-year’s likelihood of financial statement manipulation, compared to its peers and controlling for time-series trends. The AB-score requires less data than the leading accounting-based misstatement metric (the F-score) and can be computed for many more firm-years, including for financial firms. For firm-years with all data available, combining the AB-score and F-score variables into one model yields higher accuracy in predicting misstatements in- and out-of-sample.
Bibtex:
@misc{,
AUTHOR = {Bidisha Chakrabarty and Pamela C. Moulton and Leonid Pugachev and Xu (Frank) Wang},
TITLE = {Catch Me If You Can: Improving the Scope and Accuracy of Fraud Prediction},
HOWPUBLISHED = {\url{https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3352667}},
YEAR = {2019},
DOI = {10.2139/ssrn.3352667},
NOTE = {last accessed Jun 7, 2019},
}
Reference Type: E-Print
Subject Area(s): Accounting