The Credit-Risk Relevance of Loan Impairments Under IFRS 9 for CDS Pricing: Early Evidence
Bibliographic record
Abstract
Since 2018, banks have implemented the expected credit loss (ECL) model under International Financial Reporting Standard (IFRS) 9 to estimate loan losses, which replaces the incurred loss model under International Accounting Standard (IAS) 39. The key novelty of the ECL model is the incorporation of forward-looking information for recognizing accounting loan loss provisions (LLPs), which provides ample room for managerial discretion. Over the period 2014–2019, I first show that the shift to the ECL model improves the timeliness of loan loss recognition. However, under the IFRS 9 regime managers also use their accounting discretion more aggressively over LLP estimates to smooth earnings. I then investigate whether IFRS 9 improves the relevance of LLPs for credit default swap (CDS) pricing. I report that LLPs under IFRS 9 are incrementally more relevant than under IAS 39 for CDS pricing but mostly concentrated amongst banks with weaker pre-IFRS 9 information environments. I further show that under the IFRS 9 regime, LLPs are relevant for CDS pricing only when LLPs consistently reflect future expected losses while earnings smoothing via LLP generally impair the credit-risk relevance of LLPs. Finally, I find that strong governance is imperative for providing useful LLP estimates for CDS pricing.
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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 | 0.018 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".