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Earnings management visualization and prediction using machine learning methods

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  • Veganzones, David
  • Séverin, Eric

Abstract

To create new insights and understanding of earnings management, this study attempts to diagnose firms’ financial profiles using machine learning methods and thereby provide a visual representation of the financial profiles that characterize earnings management strategies (upward and downward) and tools (accruals and real activities). By applying a novel machine learning method to detect signs of earnings management, this research reveals diverse financial profiles related to earnings management. Firms that conduct downward manipulation (accruals and real activities) share a sound financial profile. For firms that manipulate earnings upward, different types of financial distress influence the earnings management tool they use: Companies with liquidity constraints undertake accruals earnings management; companies with solvency difficulties are prone to real activities management. Notably, the proposed machine learning method outperforms traditional prediction methods in detecting signals of earnings management.

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  • Veganzones, David & Séverin, Eric, 2025. "Earnings management visualization and prediction using machine learning methods," International Journal of Accounting Information Systems, Elsevier, vol. 56(C).
    • Handle: RePEc:eee:ijoais:v:56:y:2025:i:c:s1467089525000193
    DOI: 10.1016/j.accinf.2025.100743
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    1. Stratopoulos, Theophanis C. & Wang, Victor Xiaoqi, 2025. "Artificial intelligence and accounting research: a framework and agenda," International Journal of Accounting Information Systems, Elsevier, vol. 56(C).

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