Bank Risk-Taking Behavior during a Prolonged Low Interest Rate Era: The Case of Thailand
Bibliographic record
Abstract
ABSTRACT The objective of this study was to investigate the impact of prolonged expansionary monetary policy upon bank risks in Thailand. The data comprised balance sheets for 19 commercial banks from the first quarter of 2001 to the first quarter of 2019. The study hypothesized that prolonged low interest rates could impact the bank sources of liability-side funding, leading to leverage, and that different-sized banks may react differently. The results from a two-stage procedure showed that banks may borrow more to invest in risky projects if investment sensitivity to leverage has an inverse relationship with the prolonged low interest rate. This study used a fixed effects model to compare three risk proxies and justified the usage of leverage as a risk measure. These findings indicated that small- and medium-sized banks tended to take more risks than large banks. The final section used quantile regression to analyze the interest rate impact and other variables upon different levels of bank risks. The results indicated that different-sized banks responded differently to various variables under low-interest rate conditions. Keywords: prolonged low interest rate, leverage, risk, two-stage procedure, quantile regression
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".