Navigating bank risk-taking under excess liquidity: the moderating role of economic policy uncertainty and lessons from the Global Financial Crisis
Bibliographic record
Abstract
The study investigates the moderation effect of economic policy uncertainty (EPU) towards the relationship between excess liquidity and bank risk-taking as well as explores its stronger impact in countries severely affected by the 2008 Global Financial Crisis (GFC). Using System Generalized Methods of Moments (SGMM) on an unbalanced dataset for 33 countries from 2000 to 2019, the study finds that an increase in the EPU index attenuates the positive impact of excess liquidity on bank risk-taking. The study also finds that the attenuating effect of EPU on the relationship between excess liquidity and bank risk-taking is stronger in countries that were most severely affected by the GFC. It argues that the mechanisms by which excess liquidity induces risk-taking are disrupted under high EPU. Our study also extends behavioral theories to shed light on how the GFC altered bank risk-taking in the presence of excess liquidity and high EPU.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".