Bonds Between Earnings Management and Corporate Financial Stability in the Context of the Competitive Ability of Enterprises

The financial health of enterprises and their continued profitability and competitiveness in the market are influenced considerably by the level of earnings achieved. Enterprises are forced to report the best possible results to demonstrate financial strength and competitiveness and to provide a good accounting for investors and creditors. Thus, the main objective of the study is to investigate whether there is any mutual dependence between corporate financial stability and earnings management. To measure these categories, Altman’s Z score was used to determine the financial health of enterprises, and the Beneish M-score and modified Jones model were applied to detect earnings manipulation. Using the chi-square test, the results revealed a statistically significant dependence between financial distress and earnings manipulation. Then, a multivariate statistical technique of correspondence analysis was applied to the categorical data to find categories of factors that are mutually correspondent. Based on a dataset of 11,105 enterprises operating in the Visegrad countries, the results found that enterprises that are threatened by bankruptcy or located in the gray zone tend to manipulate their earnings to maintain credibility, creditworthiness, and competitiveness. Because the financial health of an enterprise provides a potential incentive for earnings manipulation, state authorities, regulators, and policy-makers may benefit from the findings of the study.

The Impact of Earnings Management on Dividend Policy: Empirical Analysis of Kse-100 Index Firms

For a number of purposes management of firms indulges in earnings manipulations. Moreover, to attract investors firms distribute dividend regularly, however sometimes to do so management can manipulate earnings information. in turn, such activities negatively affect the performance of firms in long run. Hence, in current paperinvestigated earnings manipulation and dividend policies of a sample of 76KSE-100 indexnon-financial listed firms ofPakistan stock exchange during2010-2016.Data are secondary in nature and collected from annual reportsof firms.For measurement of earnings manipulation used discretionary accruals of management activities andmodified cross sectional Jones model (1995) is used.Moreover, used random effect panel data techniquefor analysis. The final results revealed that earningsmanagement and dividend payout ratio as proxy of dividend policy are negatively and insignificantly associated. Therefore, concluded that if management involves in manipulation practices then they are unable to pay their obligations as dividend. Moreover, if the governance system is strong then management cannot manipulate true information because according to governance system management should comply and explain the dividend payment procedures.

Prediction of Earnings Manipulation on Malaysian Listed Firms: A Comparison between Linear and Tree-based Machine Learning

Predicting the earning manipulation is an inseparable part of financial-economic analysis, helping shareholders, investors, creditors and outsiders acquire high quality of firm’s financial information. Thus, the aim of the paper is to compare the earnings manipulation prediction models developed by using two types of machine learning algorithms; linear and tree categories. The linear based machine learning are Logistic Regression and Generalized Linear Model while the tree based are Decision Tree and Random Forest. All of the algorithms were tested on dataset of earnings manipulation among 1874 firm-year observations of firms listed on Bursa Malaysia . The results indicate that the performances of the two kinds of machine learning is not extremely different except with the Decision Tree. Furthermore, the most outperformed algorithm has been presented by the linear based machine learning, which produced the best accuracy in the shortest total time completion. All the models present better ability in detecting the false cases of earnings manipulation rather than the true cases mainly from the tree based machine learning. Keywords-- Earnings Manipulation, Earnings Management, Machine Learning, Malaysia

Can Board Gender Diversity Better Control Earnings Manipulation: Evidence from Banking Industry

The purpose of this paper is to examine the contribution of the board's gender diversity compared to its other characteristics in limitation earnings manipulation in the banks. The empirical study carried out on Tunisian banks over a period extending from 2001 to 2019, using the Panel-Corrected Standard Errors, allowed us to show that board gender diversity, turns out in this study of a considerable contribution to the board of directors composition since it has moderated accounting manipulation to avoid losses. As for the board independence, it has reduced earnings manipulation measured by the abnormal provisions. However, it turns out that board size and board duality does not have a significant effect on earnings manipulation.

Detecting the probability of financial fraud due to earnings manipulation in companies listed in Athens Stock Exchange Market

Purpose The aim of this paper is to detect whether there are companies listed in the general index of Athens Stock Exchange Market that possibly conduct earnings manipulation during 2017–2018. Design/methodology/approach The paper is based upon the Beneish model (M-score), which consists of eight variables to examine the probability of financial statement fraud related to earnings manipulation for 40 companies listed in the Athens Stock Exchange Market. Any company with an M-score −2.22 or above is likely to be a manipulator whereas any company that scores −2.22 or less is unlikely to conduct earnings manipulation. Findings After calculating the M-score for each company, it was found that 33 (out of 40) companies had M-score values lower than −2.22. Therefore, 82.5% of the sample is considered rather unlikely to conduct earnings manipulation whereas 17.5% of the companies listed in the general index of Athens Stock Exchange Market is likely to manipulate its earnings. Research limitations/implications In this paper, all institutions related to financial services were left out of the sample because of the fact that M-score cannot provide reliable results when applied on similar companies. Originality/value Beneish model offers a probability of financial fraud and can be therefore used as a supplementary test for auditors, fraud examiners or even national regulators such as the Hellenic Accounting and Auditing Standards Oversight Board or the Hellenic Capital Market Commission. The results of this paper can contribute to the literature concerning financial fraud in Greece during 2017–2018 because no relevant recent researches have been published yet.