Assessing the difference between accounting quality of the incurred versus expected credit loss allowances
Bibliographic record
Abstract
The dataset contains the variables used to test the impact on accounting quality in common law countries of banks following the change from the impairment model in International Accounting Standard 39 Financial Instruments: Recognition and Measurement (IAS 39), which is an incurred credit losses (ICL) model, to the impairment model in International Financial Reporting Standard 9 Financial Instruments (IFRS 9), which is an expected credit losses (ECL) model.The accounting quality measures used are income smoothing, timely loss recognition and value relevance.The sample used for the study consists of banks that have a primary listing on the main boards of the Australian Securities Exchange, the Johannesburg Stock Exchange, the London Stock Exchange, and the Toronto Stock Exchange, and the sample period spans from 2013 to 2021.This study is a quantitative study and makes use of regression models. Most of the data were collected from the London Stock Exchange Group (LSEG) Datastream. The impairment of financial assets (IMP) and LA were hand-collected from each sample bank’s annual financial statements. All collected data were analysed using SAS software.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.015 |
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".