The Explanatory Power of Canadian Accounting Measures of Earnings Dilution
Bibliographic record
Abstract
This paper examines the properties of the accounting measures of dilution under pre-2001 Canadian GAAP. Fully diluted earnings per share (EPS) presents investors with a per-share figure that attempts to capture the maximum potential dilution that would occur if all dilutive convertible securities were converted and all dilutive stock options and rights exercised. We examine how the difference between basic and fully diluted EPS, which we refer to as the dilutive adjustment, affects the ability of EPS to predict one-period-ahead EPS. Moreover, we address the issue of the explanatory power of changes in the dilutive adjustment for unexpected stock returns over the year and at the earnings announcement date. Surprisingly, in contrast with the traditional accounting view that increases in the dilutive adjustment present the investor with bad news due to potential dilution of the future earnings stream, the dilutive adjustment is positively related to next period's earnings and increases in the dilutive adjustment are positively correlated with contemporaneous long-window stock returns. These results can be attributed to the relation between the dilutive adjustment and the earnings process combined with a partial resolution of the uncertainty attached to growth firms. We find no evidence that investors use information from the disclosure of fully diluted EPS at the earnings announcement date. These results are consistent with increases in the dilutive adjustment capturing the partial realization of a firm's growth potential that more than outweighs the potential dilution attached to the convertible securities; however, this information appears to be already embedded in price prior to the disclosure of fully diluted EPS.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".