Do Canadian Companies Employ Big Bath Accounting When Recording Goodwill Impairment?
Bibliographic record
Abstract
In the transition year (2002) during which the respective goodwill impairment standards were implemented in the U.S. and Canada, these impairment losses received favorable treatment (i.e., as below-the-line expenses in the U.S. and as adjustments to retained earnings in Canada). Research in this transition year showed that goodwill impairments were recorded opportunistically in both the U.S. and Canada. Subsequent to the transition year, however, accounting principles in all countries require that goodwill impairments be presented in a more punitive fashion, with the write downs appearing as above-the-line operating expenses in the income statement. Research in the U.S. during the post-transition period provides mixed results as some studies indicate goodwill impairments are opportunistically recorded in a manner reflective of big bath behavior while others suggest these write downs convey economic information from management to users about a firm’s financial performance. No such studies have been conducted in Canada during the post-transition period. The present research fills this void in the literature by examining recent data on Canadian firms and finds evidence suggesting that goodwill impairments in this country are not being recorded opportunistically to take big baths but instead are being recognized only after multiple years of substandard earnings have occurred, thus indicating managers are recording these impairments to provide relevant information to financial statement users.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".