DuPont Analysis, Earnings Persistence, and Return on Equity: Evidence from Mandatory IFRS Adoption in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract This paper proposes four new models to forecast one‐year‐ahead return on equity (ROE) and change in ROE based on prior research in the DuPont analysis and earnings persistence, and also examines whether the persistence of ROE has improved upon mandatory IFRS adoption in Canada. Using the Granger causality test to establish the usefulness of additional explanatory variables in forecasting future earnings, I show that the DuPont components are useful in predicting one‐year‐ahead ROE, and that the persistence of ROE has decreased since Canadian firms adopted IFRS in 2011. This paper contributes to accounting research in two ways. First, it introduces a new approach to forecasting one‐year‐ahead ROE. Second, it sheds some light on the impact of IFRS adoption on reporting quality in Canada.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it