EARNINGS MANAGEMENT AND ECONOMIC VALUE ADDED IN CHINA, AFRICAN AND LATINAMERICAN MARKETS: A STUDY OF LOGISTICS MODEL, SUPPORT VECTOR MACHINES AND ROUGH SET THEORY

Abstract

Purpose- This study to predict the association between earnings management and EVA, evaluated it for accuracy.

Methodology- This study through logistic regression model (excluding OLS regression model), Support Vector Machines and Rough Set Theory

Findings- Empirical results show that RST model exhibited the highest accuracy in China and Africa nations. SVM model exhibited the highest accuracy in Latin-America nations.

Conclusion- Our results provide critical implications for managers, researchers, investors, and regulators. Managers should analyze whether EVA motivates managers to engage in earnings management behavior. For researchers, we adopted logit, SVM and RST model to predict effect of earnings management on economic value added; For investors, they can analyze the true value of enterprises, regardless of whether enterprises have adopted earnings management. Regulators (e.g., governments) should establish stricter security measures and laws or rules for listed firms to prevent earnings management following a financial tsunami and to encourage them to report their “real” true value.

Keywords

• Earnings management
• economic value added
• logistics model
• support vector machines
• rough set theory

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