Predicting Earnings Management in Korean Firms Using Explainable AI

This research explores how machine learning (ML), deep learning (DL), and explainable AI (XAI) can be applied to identify earnings management (EM) among Korean listed companies over the 2015–2024 period. We demonstrate model-task specialization: Ensemble SoftVoting best predicts AEM, while Recurrent Neural Networks (RNN) excel in REM detection, reflecting distinct underlying logics. Our XAI analysis introduces a dual-logic framework explaining distinct theoretical mechanisms behind AEM and REM. We find models independently learn two distinct theoretical logics: (1) AEM is identified as an Opportunistic response to Financial Distress and Liquidity Constraints (driven by Interest Coverage and Net Income, validating the Financial Distress Hypothesis); and (2) REM is identified as a Strategic response to Political Costs and Market Visibility (driven primarily by Firm Size and Profitability, validating the Political Cost Hypothesis). This framework bridges the long-standing accuracy- interpretability gap by uncovering distinct theoretical logics underlying AEM and REM. The findings provide actionable insights for auditors and regulators to design theory-grounded oversight mechanisms in high-stakes financial contexts. Specifically, this study answers two research questions concerning model performance and explainable-theory alignment.