Discretionary Loan Loss Provisions and Systemic Risk in the Banking Industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This study examines the relation between earnings management through discretionary loan loss provisions ( LLP s) and systemic risk in the U. S. banking sector using a large sample of commercial banks from 1996 to 2009. We find that earnings management increases a bank's contribution to systemic crash risk and systemic distress risk, consistent with the notion that earnings management increases information opacity, facilitates bad news hoarding, co‐moves with macroeconomic conditions, and exhibits cross‐sectional correlation and herding in earnings management. However, the effect of earnings management through discretionary LLPs on systemic risk disappears during the crisis period, consistent with weakened earnings management in crisis times. We also find that the same effect strengthens with bank uncertainty and homogenous loans, and weakens in the post‐ SOX period, and when banks are audited by Big 4 auditors.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it