Application Of Beneish M-Score Model In Detecting Probable Earnings Manipulation In Malaysian Public Listed Companies
(1) Accounting Research Institute, Universiti Teknologi MARA, Malaysia
(2) Accounting Research Institute, Universiti Teknologi MARA, Malaysia
(3) Universiti Teknologi MARA Selangor, Malaysia
(*) Corresponding Author
Abstract
Earnings manipulation involves alteration, adjustment, exploitation, and even timing of transactions in financial statements with the intention to report a sound economic performance in a company. This act could jeopardize the company’s financial performance in the long run and also make its stakeholders to suffer significant losses. Therefore, a tool to detect or predict earnings manipulation would be helpful to the stakeholders, practitioners, regulators, academicians, and professionals in the accounting field. Besides, an early detection tool is needed to alarm the enforcement agencies to make further investigations or legal actions. Therefore, the aim of this study is to analyse the Beneish M-Score models and its eight accounting variables to detect the likelihood to engage in earnings manipulation in the case of Malaysian PLCs (Public Listed Companies). The financial data of 80 of PLCs from 2015 to 2017 were gathered. This study applied Beneish Model as a detection tool for earnings manipulation and anomalies of red flags and to classify the companies into two groups, which are manipulators and non-likely manipulators. The Independent T-tests were analysed to identify dominating ratios. The results of this study found that M-Score and its three indexes were significantly different for manipulators and non-likely manipulators, which are Sales Growth Index (SGI), Total Accruals to Total Assets (TATA) and Days' Sales Receivable index (DSRI). The percentage of manipulators had slightly decreased in 2016 and gradually increased in 2017. Hence, the inflation or overestimation of sales and revenues, as well as accruals, could signals earnings manipulation.
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DOI: http://dx.doi.org/10.33019/ijbe.v5i1.301
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