Determinants of Earnings Manipulation amongst Lusaka Securities Exchange Listed Firms: Application of the Beneish M-Score Model


Authors : Nathan M Mataa; Dr. Taonaziso Chowa

Volume/Issue : Volume 8 - 2023, Issue 3 - March

Google Scholar : https://bit.ly/3TmGbDi

Scribd : https://bit.ly/40Jz8aC

DOI : https://doi.org/10.5281/zenodo.7800755

Corporations have incentive to manipulate their financial performance with reported earnings or net after tax income being a common target resulting in concerns such as misallocation of funds in capital markets and in the extreme, firms concealing possible impending cases of corporate failure. This study was conducted to evaluate prevalence and determinants of earnings management amongst firms listed on the Lusaka Securities Exchange (LuSE) using the Beneish (1999) M-score model and establish factors that may have contributed to the likelihood of such practices amongst the firms for the period 2010-2019. Data for the study was collected from annual reports of 12 out of the 17 non-service-related firms listed on the LuSE over the period 2010-2019. Results of the study were that there was high prevalence of earnings manipulation with 30.27% of the M-Scores calculated in the manipulator range amongst the firms. However, of the variables adopted as possible explanatory factors for the practice, the study found that only the coefficients of “years listed”, and “financial gearing” for the listed firm were statistically significant in the empirical Probit regression models estimated. Based on these findings, the study recommends that LuSE and the Securities and Exchange Commission create guidelines for the use of Generally Accepted Accounting Practices and application of accounting standards that minimize the likelihood of earnings manipulation amongst listed firms. They should also closely monitor IPOs and financial statements of recently listed firms to curtail their tendency to manipulate earnings.

Keywords : Earnings Manipulation, Beneish M-Score Model, Lusaka Securities Exchange, Probit Regression

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