Can Impairment Recognition under IAS 36 Be Improved by Financial Performance?
Bibliographic record
Abstract
The study seeks first to examine how companies implement impairment test as required by IAS 36.Secondly, to explore and evaluate factors which may explain the effect of specific financial indicators on impairment loss. Quantitative analysis of a panel data sample of (30) companies listed on the Amman Stock Exchange (ASE) over 2005-2008 was carried out. Despite the fact that sample of companies listed on the ASE supposed to implement IAS36; only (41.10%) of the sample recorded an impairment recognition loss (30 out of 73). We find that impairment loss ratio showed fluctuated trends. We also find that probability of an impairment loss is positively affected by company size (SIZE) and operating cash flows (OCF). Research also reported a positive significant relationship between financial leverage (LEV), return on assets (ROA) and return on equity (ROE) .This can be explained by most public companies utilize financial leverage heavily to increase (ROA) and (ROE). We confirm insignificant statistical positive relationship between profitability measured by (ROA) and impairment loss recognition (IMP). Other controlling variables such as LEV; has a negatively insignificant relationship with IMP. This paper introduces necessary background and fundamentals to understand current practice of impairment recognition in Jordan. To urge Companies to use high standards of accounting quality in their financial statements according to IASs, this presentation would attract investors’ attention and would further reinforce the credibility of their financial statements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".