Goodwill’s Accounting Practices in Belgium and Compliance with IAS 36 Required Disclosures
Bibliographic record
Abstract
This paper aims at studying the impact of the accounting treatment of goodwill on the mandatory disclosure required by the International Accounting Standard (IAS) 36 on the impairment test of goodwill. We use a sample comprising 79 companies listed on Brussels stock exchange to show that there is a great heterogeneity in current accounting treatment of goodwill. We identify two groups of companies: those that display the goodwill on a separate line in their balance sheet and those that integrate it in their intangible assets. For the later, the only way to notice the presence of goodwill is by looking at the financial statement’s notes presumably because those notes are expected to receive less scrutiny. Even if the compliance is not complete, the first group complies more with the paragraph 134 of IAS 36 than the other. Moreover, companies with a significant goodwill compared to both total assets and intangible assets are more compliant with IAS 36. The findings finally reveal that the notices issued by the Financial Service and Markets Authority (FSMA) have a limited impact on the disclosure level. There are some areas of improvement but others such as goodwill allocation to cash generating unit, determination of the recoverable amount, description of key hypothesis and the sensitivity test need more effort on compliance.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".