Detecting probable manipulation of financial statements. Evidence from a selected Zimbabwe Stock Exchange-Listed bank

Purpose: The study used the Beneish M Score to discover probable financial statement manipulation by a selected Zimbabwe Stock Exchange-listed bank. Research methodology: The Beneish M Score eight variable statistical model was applied to secondary data of the selected bank from 2011 to 2018. The model utilizes ratios in distinguishing between manipulators and non-manipulators, with a yardstick measure of -2.22. Results greater than -2.22, classify the organization as a financial statements manipulator with less than -2.22 classify it as a non-manipulator. Results: The M score model detected manipulation for the years 2011 (-0.74), 2013 (-1.84), and 2015 (-2.19), which are greater than the benchmark of -2.22. The years 2012 (-3.17), 2014 (-2.46), 2016 (-3.07), 2017 (-2.80) and 2018 (-2.42) reveal the bank as a non-manipulator as these values are less than -2.22. Limitations: The Beneish M score statistical model was modeled for manufacturing companies. The study sought to test the M Score’s applicability in the banking sector and it was restricted to the selected bank for the years 2011 to 2018. Contribution: The Beneish M score is a valuable model for users of issued annual financial statements to guard against earnings manipulation. Stakeholders rely on audited financial statements, believed to be free from manipulation, yet companies fold up with unqualified audit opinions contained in published financial statements. The study validates the Beneish M score statistical model for detecting manipulation in published annual financial statements in Zimbabwe, where there is limited research on earnings manipulation. Purpose: The study used the Beneish M Score to discover probable financial statement manipulation by a selected Zimbabwe Stock Exchange-listed bank. Research methodology: The Beneish M Score eight variable statistical model was applied to secondary data of the selected bank from 2011 to 2018. The model utilizes ratios in distinguishing between manipulators and non-manipulators, with a yardstick measure of -2.22. Results greater than -2.22, classify the organization as a financial statements manipulator with less than -2.22 classify it as a non-manipulator. Results: The M score model detected manipulation for the years 2011 (-0.74), 2013 (-1.84), and 2015 (-2.19), which are greater than the benchmark of -2.22. The years 2012 (-3.17), 2014 (-2.46), 2016 (-3.07), 2017 (-2.80) and 2018 (-2.42) reveal the bank as a non-manipulator as these values are l