Predicting Financial Distress in ASEAN Banking: A Logistic Regression Approach
Bibliographic record
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".