Earnings management motives and firm value following mandatory IFRS adoption – evidence from Canadian companies
Bibliographic record
Abstract
When Canada already has a set of well- established legal enforcement and investor protection mechanism to control earnings management; and the quality of Canadian GAAP is high, I examine if the accounting quality for Canada can still be improved since its adoption of IFRS mandatorily in 2011. The extant literature argues that IFRS adoption benefits firms domiciled in countries with strong legal and financial institutions. However, when the quality of IFRS is as good as the local standards for many Anglo-Saxon countries such as Canada, it is questionable for these countries to receive substantial economic consequences. Following the literature, I estimate a set of comprehensive measurements of earnings management as the proxies of accounting quality. Empirically, I document evidence that even though the results are mixed, there are still certain significant improvements in accounting quality. However, I find that firms issuing more equities are motivated to associate with lower earnings quality. Also, firms engaging in two distinct strategic directions (prospector vs. defender) have systemically dissimilar effects on earnings quality in IFRS adoption. Finally, I document evidence that firm value following IFRS adoption has been increased, but at the expense of lower accounting quality. Overall, my study shed some lights into the literature that accounting standards per se is not sufficient to ensure a uniform-level of accounting quality because firm-level earnings management motives are important factors too.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".