Bibliographic record
Abstract
Using a panel of commercial, co-operative and savings banks from G7 countries, we investigate whether change in sentiment and its volatility affect banks' lending behavior. Sentiment indicators, which gauge the state of the economy from the perspective of the economic agents, are widely considered as a critical component by academics, policy makers and media in the transmission of shocks into the economic activity. We also know that leading indicators usually change before the economic activities change as a whole and provide useful information on the state of the economy. Surprisingly, earlier studies have not examined the impact of the level and volatility of economic agents sentiment on banks' lending behavior. As each type of agent acts on a specific set of (imperfect) information that emanate from the state of the economy, rational inattention, or their own asymmetric goals and strategies, it is important find out whether bank managers respond to changes and variability in sentiment. To carry out our investigation, we construct a large panel of commercial, co-operative, and savings banks collected from the Bankscope database for the G7 countries including Canada, Germany, France, the UK, Italy, Japan, and the US. This database provides detailed bank-level information yet the sample size is constrained due to the fact that we seek to examine the role of Core Tier 1 capital on banks' lending. The final dataset that we employ in our analysis is comprised of more than 9, 000 banks and retains bank, country and time dimensions. The analysis covers the period between 1999-2014. The investigation implements GMM and fixed effects models to test various hypotheses. We show that the changes in economic agents' sentiment and its volatility affect bank lending negatively, while the impact sizes differ across indicators. We also find that the impact of volatility effects on banks' loan growth varies at excessive levels. We highlight the role of several bank-specific characteristics in transmission of uncertainty effects on the growth of bank loans, as uncertainty affects extenuate or mitigate through them.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".