Value Relevance of Intangible Assets Before and After FRS 138 Adoptions: Evidence From Malaysia
Bibliographic record
Abstract
The economic environment has changed from the era of agriculture, industrial and now to an information era. In this information era, intangible assets dominate the environment compared to during industrial era that was mainly dominated by tangible assets. Intangible asset plays an important role in today’s economy with the shift from being an industrialised economy to a high-tech and service-oriented. In Malaysian capital market, there is an upward trend of intangible assets development. Hence, the question of whether the value relevance of intangible assets is properly reflected in financial statements arises. The objective of this study is to examine the value relevance of intangible assets in Malaysia before and after the adoption of FRS 138. This study used a sample of 113 public listed companies from four main sectors namely Industrial Product, Trading services, Consumer Product and Technology. The period under study was divided into two, that is, pre adoption period (2002-2005) and post adoption period (2008-2011) to observe if there were any improvements on the value relevance of intangible assets after the adoption of International Financial Reporting Standards (IFRS). The data was analysed to examine the value relevance of intangible assets in Malaysia before and after the adoption of FRS 138. The finding of this study suggests that intangible assets are value relevant in the pre adoption period but are not value relevant in the post adoption period. This study may contribute to the existing literature on the economic consequences of adopting IFRS and also preliminary indication of the impact of FRS 138 adoption.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".