Date of Conferral
This study investigated earnings management in European firms. The private investors became victims of manipulated earnings where few laws offered regulatory oversight. The study forensically examined the attributes of earnings management identified using a discretionary accrual model published in Jones' work and Schippers' work. The firms' managers should fulfil agency theory when they made reporting decisions, and they should act in the investors' best interests to fulfil stewardship theory. The managers failed as they seemed to favor insiders when they reported manipulated earnings to outsiders like small investors even though the managers published financial reports conforming to the International Financial Reporting Standards. The investors depended on the decision usefulness of the reports. The study used the data of 432 listed firms in 11 code law nations. The paired t test identified significant differences between reported and economic earnings to find earnings management attributes and between economic and restated earnings to find earnings management cases. The research found that managers seemed to manipulate discretionary accruals to misstate earnings and reduce the decision usefulness of reporting. The data came from published financial reports and databases. The firms represented 11 nations and 9 industries that excluded banking and insurance. Almost 17% of nations and industry segments reflected earnings management attributes. About 29% of firms restated at least one annual earnings, and 84% of the restatements appeared to offset manipulation. The research results should prompt social change for small investors where regulators would redress the manipulation using stronger investor protection laws to improve the reported earnings quality and its decision usefulness.
Garner, Jef Lee, "Forensic Detection for Earnings Management in Selected Code Law Nations of Europe" (2018). Walden Dissertations and Doctoral Studies. 5863.
Accounting Commons, Finance and Financial Management Commons, Statistics and Probability Commons