The Credit-Risk Relevance of Bank Loan Loss Provisions Under IFRS 9: Early Evidence
Bibliographic record
Abstract
Since the first fiscal quarter of 2018, banks have implemented a new expected credit loss (ECL) model to estimate loan loss provisions (LLPs) under International Financial Reporting Standard (IFRS) 9, which replaces the incurred-loss model under International Accounting Standard (IAS) 39. The major difference in provisioning approaches between the two models is the incorporation of forward-looking information under the IFRS 9 ECL model. This paper examines whether IFRS 9 improves the credit-risk relevance of LLPs for credit default swap (CDS) market participants. The findings suggest that LLPs under IFRS 9 are marginally more credit-risk relevant than under IAS 39. Moreover, LLPs under IFRS 9, to a greater extent than under the IAS 39 regime, affect the pricing of credit risk for longer CDS maturities. This finding is consistent with the IFRS 9 ECL model that provides a more forward-looking measure of credit risk. Finally, cross-sectional tests highlight the importance of institutional features that play a significant role in the relevance of accounting information. To address endogeneity concerns, I implement a dynamic panel data two-step model and a two-stage instrumental variable approach. These alternative tests confirm the enhancement of the credit-risk relevance of LLPs following the adoption 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 | 0.015 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".