Market Valuation of Research and Development Spending under Canadian GAAP*
Bibliographic record
Abstract
ABSTRACT Section 3450 of the Canadian Institute of Chartered Accountants (CICA) Handbook requires Canadian firms to capitalize development costs that meet certain criteria and to expense those that relate to research. International Accounting Standard (IAS) No. 38 favours a similar approach. In the United States, Statement of Financial Accounting Standard (SFAS) No. 2 recommends the immediate expensing of all research and development (R&D) spending. The only exception is SFAS No. 86, which requires software development costs to be capitalized when a product successfully passes a technological feasibility test. Consequently, the Canadian financial disclosure regime provides a rich setting for testing the market valuation of capitalized R&D. Our primary research question asks whether capitalized R&D provides useful information to market participants investing in Canadian firms. We use price‐level and return models to assess the value relevance of capitalized R&D disclosed in the financial statements under Canadian GAAP. In line with expectations, using a price‐level model, we find that capitalized R&D and R&D expense as disclosed in the financial statements provide information that is value relevant to market participants. However, we find that R&D capitalized during the year helps explain returns while R&D expense does not. Thus we conclude that the application of section 3450 of the CICA Handbook produces value‐relevant information.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".