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Earnings management visualization and prediction using machine learning methods
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DOI：10.1016/j.accinf.2025.100743.png)
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Abstract
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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.
Keywords:
Earnings management
Machine learning
Financial profiles
Prediction
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