Current expected credit loss model adoption
Bibliographic record
Abstract
Abstract The mandatory switch from the incurred loss model to the more forward‐looking current expected credit loss (CECL) model was originally scheduled to begin in 2020. However, when the COVID‐19 pandemic started in early 2020, US regulators made the switch voluntary. Our study investigates how banks' exposure to the pandemic affects their decision to adopt CECL as well as adopting banks' pandemic‐era pattern of loan loss provisions. First, consistent with pandemic‐driven economic uncertainty reducing banks' willingness to adopt the new model, we find a negative association between banks' pandemic exposure and their CECL adoption. This association is more pronounced for banks with more lending opportunities, more lending competition, and worse loan quality. Second, compared with non‐adopters, CECL adopters report more loan loss provisions during the pandemic's early period, and less or even negative loan loss provisions during the late period. The latter scenario reflects a reversal of earlier loan loss reserves and is more pronounced for banks with more exposure to states with a higher level of vaccination, consistent with banks having a more positive economic outlook because of improving pandemic conditions. Overall, our study offers useful insights into the adoption and implementation of accounting standards during periods of economic uncertainty.
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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 | 0.005 | 0.031 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".