Explaining the Beneish model and providing a comprehensive model of fraudulent financial reporting(FFR)

Abstract

In this study, the aim is to provide a comprehensive model for the prediction, prevention and detection of financial reporting fraud using the modified benchmarking model. To achieve the research goal, the necessary data were collected for 161 companies listed on the Tehran Stock Exchange during a 10-year period (2009-2018). The results of estimating the research model have been examined by the binomial logit method. The results of testing the hypotheses of this study indicate that Beneish model is successful in separating companies involved in fraudulent financial reporting and healthy companies, based on McFadden's detection coefficient, with 73% confidence, and among the independent variables, day’s sales in receivable index (DSRI), gross margin index (GMI), asset quality index (AQI), sales growth index (SGI), depreciation index (DEPI) and total accrual to total assets index (TATAI), have a direct and significant effect on fraudulent financial reporting, but sales, general, and administrative expenses index (SGAI) and leverage index (LEVI) have had a significant inverse effect on fraudulent financial reporting (FFR).