Benefits and Consequences of CECL-Adoption During COVID-19
Bibliographic record
Abstract
The current expected credit loss (CECL) accounting model, which came into effect in 2020 in the United States, aims to address banks’ procyclical behavior and the delayed recognition of loan loss provisions (LLPs) experienced under the incurred credit loss model. Starting from 2020, major CECL-adopting banks initially increased LLPs, but quickly made large reversals in 2021, and then increased LLPs again in 2022, indicating procyclicality and contradicting the intended CECL purpose, which is for banks to build up long-term stable reserves. We explore this unexpected phenomenon by studying the consequences of adopting CECL and potential motivations for banks’ short-termism in building and releasing LLPs under the new accounting policy. Using a sample of U.S. Bank Holding Companies, we document that CECL-adopters exhibit increased volatility in LLPs and allowances for loan loss, report timelier and more valid LLPs, and show greater earnings response coefficients than non-CECL-adopters. However, we find that banks’ rapid LLP reversals in 2021 may have been motivated by opportunistic earnings management incentives. Our findings suggest that while the CECL model was intended to foster counter-cyclical and timelier loan loss provisioning, banks could have exploited its flexibility in LLP estimation for managerial opportunism, resulting in challenges for the policy intended to reduce banks’ procyclical behavior.
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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 | 0.009 | 0.040 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".