Predicting Financial Distress in ASEAN Banking: A Logistic Regression Approach

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DOI: https://doi.org/10.28924/2291-8639-23-2025-287
Published: Nov 13, 2025
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Evaliati Amaniyah, Abdul Mongid, Nadia Asandimitra Haryono, Predicting Financial Distress in ASEAN Banking: A Logistic Regression Approach, Int. J. Anal. Appl., 23 (2025), 287.

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Evaliati Amaniyah, Abdul Mongid, Nadia Asandimitra Haryono

Abstract

Banking sector stability is crucial to a country's economy, but the global financial crisis and macroeconomic pressures such as the COVID-19 pandemic have heightened the risk of financial distress. This study aims to develop a prediction model for banking financial distress in ASEAN countries by combining CAMEL indicators and macroeconomic variables. Using panel data from 435 banks during the period 2017–2021 and logistic regression analysis, this study shows that the capital adequacy ratio (CAR) and return on assets (ROA) have a negative effect on the likelihood of distress, while the non-performing loan ratio (NPL) and exchange rate have a positive effect. The model that combines macroeconomic variables shows higher prediction accuracy than the model that only uses internal financial indicators. Model 1, excluding macroeconomic variables, correctly predicted 67.14% of the sample banks, while Model 2, including macroeconomic variables, increased the prediction to 72.01%. This study expands the literature on early warning systems using empirical evidence from ASEAN countries and contributes to the applied analytics domain by proposing logistic models relevant for policy-making and banking regulation.

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