The Decision Usefulness of Current Expected Credit Losses: Users’ Views about the Current Expected Credit Losses Model
Bibliographic record
Abstract
ABSTRACT In 2016, the Financial Accounting Standards Board (FASB) issued ASU 2016-13, “Financial Instruments—Credit Losses,” requiring firms to switch to a current expected credit losses (CECL) model. To assess the impact of this new standard, we performed semistructured interviews with analysts, trade group members, and financial journalists, all of whom have experience with CECL. Overall, interviewees shared the view that the CECL standard-setting process was tumultuous and political. Interviewees also stated that CECL led to perceptions of decreased decision usefulness of loan loss information and decreased comparability among reporting firms but had little impact on firms’ lending operations. Our study answers the call from the FASB to perform research into the impacts of CECL and also contributes to the literature on sell-side analyst decision making and the literature on the determinants of decision usefulness for analysts. Data Availability: Data are not available for confidentiality reasons. JEL Classifications: G21; G28; M41; M48.
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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 | 0.019 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".