Investment Property, Cost Model, Fair Value Model and Value Relevance: Evidence From Malaysia
Bibliographic record
Abstract
The purpose of the study is to investigate the value relevance of investment property of Malaysian listed firms based on cost model and fair value model for measuring their investment properties. Some studies suggested fair value model is more value relevant and some other studies suggested cost model is more value relevant. The sample was selected using a simple random sampling so that all listed firms have equal chance to be selected. A final sample of 108 firm-year from various industries was selected for a period from 2018 to 2019. Equity valuation models developed by Landsman (1986) and Ohlson (1995) were used to test the value relevance of investment property employed by listed firms in Malaysia. The models were used to test the value relevant of pooled sample, fair value sample and cost sample. The results show that firms’ investment properties are value relevant regardless whether cost model or fair value model was selected. It was also found that depreciation included in cost model and fair value gain or loss included in fair value model net profits are value relevant. The study implicates that cost model is more value relevant in measuring investment property. The result provides useful insight to standard setter about the effect of selection of fair value model and cost model towards share market value. Standard setters, researchers and academics would benefit from this as prior research in Malaysia suggests that investment properties (in general) are not value relevant even though investment properties of property companies are value relevant.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".