Does Financial Statement Comparability Enhance The Usefulness of Earnings? Evidence From Canada
Bibliographic record
Abstract
Building on the comparability construct developed by De Franco, Kothari, and Verdi (2011), the study examines whether the comparability enhances the usefulness – relevance and reliability – of earnings, as suggested in the International Financial Reporting Standards (IFRS) Conceptual Framework. By far, researchers have examined the benefit of comparability from the users’ perspective. However, the relationships between comparability and earnings relevance or earnings reliability have not been directly examined. This paper is motivated to address such a question using Canadian firms’ data in the post-IFRS period to estimate firm-specific comparability, and then to test the roles of comparability in earnings relevance and earnings reliability. The results document that comparability has a significantly positive impact on both relevance and reliability of earnings. Additionally, the study conducts comparability analysis with size and industry effect. Overall, the results are consistent with the prediction, indicating that comparability enhances the decision-usefulness of earnings.
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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 | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".