Expected loss recognition and banks’ management forecasts
Bibliographic record
Abstract
Accounting rules for credit impairment recognition have been shifting to a more forward-looking approach based on expected losses. We examine how the adoption of an expected loss model (ELM) influences banks’ management forecasts, which also are forward-looking. In a difference-in-differences setting of gradual implementations of the ELM worldwide, we find that banks enhance management forecasts after adopting the future-oriented provisioning model, as manifested in higher likelihood of forecast issuance, higher frequency of forecasts, more precise forecasts, and higher overall forecast quality. This forecast-enhancing effect is more prominent when accounting standards are more strictly enforced, when banks experience larger changes in loss allowance after ELM implementation, and when forecasting is more challenging such as during the onset of the COVID-19 pandemic. Moreover, banks’ post-ELM forecasting performance also improves in terms of greater forecast accuracy and persistency. Overall, our results suggest a complementary relation between expected loss recognition and banks’ management forecasts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".