Moving toward the expected credit loss model under IFRS 9: capital transitional arrangement and bank systematic risk
Bibliographic record
Abstract
This paper examines banks’ option to adopt the capital transitional arrangement (CTA) set out by the Basel Committee on Banking Supervision, in response to the introduction of the International Financial Reporting Standard 9 (IFRS 9), which requires the use of an expected credit loss model instead of an incurred loss model to estimate the impairment of financial assets. Using a sample of publicly listed European banks from 2016 to 2019, we find that bank CTA adoption choice is associated with neutral factors captured by bank-specific fundamental characteristics, and potential opportunistic factors related to regulatory constraints implied by the application of IFRS 9. We further find that banks that adopted the CTA (CTA adopters) decrease their exposure to systematic risk during the transitional period. However, this relationship is only significant in countries with powerful banking authorities. In those with less powerful banking authorities, CTA adopters tend to exercise more aggressively their accounting discretion. Our study is the first to address banks’ voluntary choice to adopt the CTA policy under the mandatory application of IFRS 9.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".