The impact of COVID-19 and bank capital ratio on loan changes of ASEAN-5’s banking industry
Bibliographic record
Abstract
The COVID-19 pandemic has affected economies around the world, including the banking industry, and this depends on various factors. The aim of this study is to understand the influence of COVID-19 independently and through the moderation of bank capital ratios on changes in loans of Association of Southeast Asian Nations 5 (ASEAN-5) banking industry players. The study uses a sample of 86 banking companies listed on the stock markets of Indonesia, Malaysia, the Philippines, Singapore, and Thailand from the first quarter of 2018 to the fourth quarter of 2020 by employing the panel data regression technique. The results showed that COVID-19 had a significant negative effect on changes in bank lending. However, a bank’s capital ratio was not found to play a role in moderating the effect of COVID-19 on changes in bank lending. These findings have three main implications: (i) the role of the government in recapitalization and liquidity injection can eliminate differences in behavior between banks with the classification of capital ratios; (ii) there are no signs of zombie lending in ASEAN-5’s banking industry; and (iii) regulating incentives to change bank lending behavior in future crises must take into account that bank capital categorization will not be effective. AcknowledgmentThis study was made possible with the support of a research grant by Universitas Indonesia number NKB-533/UN2.RST/HKP.05.00/2022.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".