Economic Reasons for Reporting Property, Plant, and Equipment at Fair Market Value by Foreign Cross-Listed Firms in the United States
Bibliographic record
Abstract
This paper attempts to provide some preliminary evidence of possible implementation outcome of the use of fair value option for non-financial assets in the U.S. The characteristics of foreign-listed firms in the U.S. Stock Exchanges who use fair value (revaluation) option for measurement and reporting of property, plant and equipment (PPE) are examined. These firms already use the standard without being required to provide reconciliation to the U.S. GAAP. But only 38 of 232 firms choose to report their assets at fair value. As such, the revaluation model is not very popular among the cross-listed firms and the majority of these firms do not choose the option. We test for differences between adopters and non-adopters using leverage ratios, the intensity of PPE, firm size in terms of sales, market value, and total assets and profitability ratios. Our results show that those who adopt the fair value model for PPE (revaluers) have fundamentally different economic characteristics. We find that larger firms with higher value of PPE, and a higher ratio of the total amount of property, plant, and equipment to total assets are more likely to revalue their long-term assets. Our Probit and Factor analyses further show that larger firms with higher debt ratios (e.g., debt-to-equity), are more likely to adopt the PPE revaluation model.
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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 | 0.008 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".