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  • 标题:Detection of Anomalies in Accounting Data Using Benford’s Law: Evidence from India
  • 本地全文:下载
  • 作者:Ramesh Chandra Das ; Chandra Sekhar Mishra ; Prabina Rajib
  • 期刊名称:Journal of Social Science Studies
  • 电子版ISSN:2329-9150
  • 出版年度:2017
  • 卷号:4
  • 期号:1
  • 页码:123
  • DOI:10.5296/jsss.v4i1.9873
  • 语种:English
  • 出版社:Macrothink Institute
  • 摘要:This study uses the financial accounting data to examine if they depart from Benford’s Law. Using large sample of Indian public listed companies, the study conducts an analysis of the “first digit analysis”, “second digit analysis”, and “first two digit analysis “of test variables such as total assets, receivables, fixed assets, property, plant and equipment, inventory, current assets, current liabilities, sales, selling and distribution expenses, cost of goods sold, cash, EBIT, direct tax, indirect tax. The initial results find that most of the variables have significant deviation from Benford’s Law distribution. Further analyses indicate that business group firms indulge more data anomalies than standalone firms and small size firms have more data anomalies than large size firms in Indian context.
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