Dynamics of Funding Liquidity and Risk-Taking: Evidence from Commercial Banks
Bibliographic record
Abstract
The purpose of this study is to investigate the impact of funding liquidity risk on the banks’ risk-taking behavior. To test the hypotheses, we apply the two-step system GMM technique on US commercial banks data from 2002 to 2018. We find that funding liquidity increases the banks’ risk-taking of US commercial banks. Furthermore, banks with higher deposits are less likely to face a funding shortage, and bank managers’ aggressive risk-taking activity is less likely to be monitored. Our findings infer that increases in bank funding liquidity increase both risk-weighted assets and liquidity creation, and deposit insurance creates a moral risk issue for banks taking excessive risks in response to deposit rises. The relationship between funding liquidity and the banks’ risk-taking varies with their capitalization and market conditions; the impact of funding liquidity on risk-taking is pronounced for well-capitalized banks and the Global Financial Crisis 2007. Our tests are robust for the usage of alternate proxy of funding liquidity and by controlling economic conditions. The findings of this study have implications for regulators to develop guidelines for the level of liquidity and risk-taking of commercial banks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".