Abstract

This study investigates the effects of the Expected Credit Loss (ECL) model under IFRS 9 (K－IFRS No. 1109), which was implemented in 2018, from the perspective of banks’ loan loss provisions. The ECL model requires the forward－looking recognition of expected credit losses, thereby enabling the timely recognition of losses. However, it also entails the risk of excessive provisioning, which may undermine financial soundness. To address this, the study examines the time－series trend of loan loss provisions and their reflection in the capital market. Specifically, abnormal loan loss provisions are estimated using the residuals from regression and machine learning (Random Forest, XGBoost) models that control for the determinants of provisions identified in prior studies. Using data from domestic banks between 2014 and 2024, the analysis finds that abnormally recognized loan loss provisions increased after the adoption of the ECL model in 2018. These abnormal provisions continued to rise during the COVID－19 pandemic period. Moreover, while abnormal provisions exhibited a positive value relevance with stock prices in the pre－ECL period, their incremental value relevance tended to diminish following the adoption of the ECL model. The findings provide practical insights for bank practitioners in recognizing and assessing the adequacy of loan loss provisions. In addition, the results are expected to offer policy implications for financial regulators and accounting standard－setters by contributing to the evaluation of the ECL model’s application and its consistency with regulatory frameworks.

Keywords

• IFRS 9
• Expected Credit Loss (ECL) model
• Bank
• Abnormal Loan Loss Provisions
• Value Relevance
• Machine Learning

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