What Do Analysts' Provision Forecasts Tell Us about Expected Credit Loss Recognition?
Bibliographic record
Abstract
ABSTRACT We document potential cross-sectional differences in how expected loss accounting will affect provision timeliness to provide important policy insights and contribute to the literature regarding the estimation of the expected loss model adoption impact and provision timeliness determinants. Our findings that analyst provision forecasts incrementally predict future nonperforming loans (NPLs) and market returns suggest that the incurred loss provision does not incorporate all available future loss information. Higher incremental coefficients on provision forecasts for banks with greater unrecognized future losses and incurred loss constraints suggest CECL could affect cross-sectional provision timeliness differences by removing these constraints. Specifically, the provision forecast and future NPL association increases with banks' unconstrained future loss estimates reflected in loan fair value disclosures and incurred loss constraints indicated by heterogeneous loans individually reviewed for impairment. This association also increases with EPS forecast errors, but decreases with target price and NPL forecast errors.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".