Fair Value Accounting for Property, Plant, and Equipment: Impact of IFRS 1 Adoption
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT This paper uses a unique setting of Canadian public firms adopting International Financial Reporting Standards (IFRS) to investigate the factors that motivate companies to revalue Property, Plant, and Equipment (PP&E) under the deemed cost provision in IFRS 1, and whether revaluations help predict future performance, and what is the market reaction to such revaluations. Utilizing the probit model, difference-in-differences approach, and Wald test, we find that large firms and/or firms with higher net PP&E to total assets ratios are more likely to revalue PP&E, and firms adopting the fair value option for PP&E record lower depreciation in the post-IFRS period. In addition, we show that investors react negatively to the firms electing the fair value option for PP&E and the market discounts such revaluation information.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it