Bibliographic record
Abstract
In this dissertation, I include three essays regarding earnings forecasts, the DuPont analysis and tax expense, all using mandatory International Financial Reporting Standards (IFRS) adoption in Canada as a setting. In the first essay entitled “DuPont Analysis, Earnings Persistence and Return on Equity: Evidence from Mandatory IFRS Adoption in Canada”, I propose four new models to forecast one-year-ahead return on equity and change in return on equity based on prior research in the DuPont analysis and earnings persistence. I also examine whether the persistence of return on equity has improved since Canadian companies adopted IFRS in 2011.\nIn the second essay entitled “Information Content of Tax Expense and the Effect of IFRS Adoption on Tax Expense”, I examine the information content of tax expense about future profitability and the effect of IFRS adoption on tax expense. Prior studies (Lev and Nissim, 2004; Hanlon, 2005; Schmidt, 2006; Ayers et al., 2009) use estimated taxable income, book-tax differences and effective tax rates to investigate the relation between income taxes and future earnings. However, those estimated proxies contain measurement errors and might distort the relationship among variables. Tax expense including current, deferred and other income taxes is directly derived from a Compustat account with no estimation error. The main analysis and robustness tests show that tax expense contains more incremental information content about future profitability beyond pre-tax book income than estimated taxable income.\nIn the third essay entitled “Impact of IFRS Adoption, Value Relevance and Industry Effects: A Canadian Study”, I propose a new comparability index to examine the impact of IFRS adoption on the financial statements of firms from different industries. The study demonstrates that deemed cost of property, plant and equipment is the optional exemption that caused the most discrepancy among first-time IFRS adopters; and that only transitional adjustments related to income accounts are value relevant.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".