Bibliographic record
Abstract
In this study, I empirically investigate the decision usefulness of mandatory reporting of comprehensive income in Canada under the erstwhile Accounting Handbook Section 1530 (HB1530). Comprehensive income (CI) equals the sum of Net Income (NI) and Other Comprehensive Income (OCI). I operationalize the concept of decision usefulness in two ways: (i) value relevance, and (ii) association with analysts’ earnings forecasts. As a theoretical framework, I draw upon Ohlson’s (1999) discussion of the attributes of transitory earnings: forecasting irrelevance, unpredictability, and value irrelevance. Ohlson pointed out that the existence of any two attributes implies the third. That is, if an earnings item is irrelevant for forecasting future abnormal earnings, and if current numbers do not predict future ones (that is, if the earnings item is unpredictable), then it must be value irrelevant. Drawing upon a sample of firms listed on the Toronto Stock Exchange, I employ a multivariate regression approach to test whether OCI and its individual components (namely, unrealized gains and losses on cash flow hedges - HEDGE, unrealized gains and losses on available for sale investments – SEC, and foreign currency translation adjustment on foreign subsidiaries – FOREX) possess forecasting relevance, predictability and value relevance. As a specification check, I also include a component of OCI under United States GAAP, minimum pension adjustment, PENADJ, to see if it possesses the same attributes as do components of OCI under HB1530. I also test for the association of OCI and its components with analysts’ earnings forecasts. I find that aggregate OCI and some of its individual components are relevant in forecasting future abnormal earnings, predictable, and incrementally value relevant. Aggregate OCI as well as some of its components are also able to predict future net income and operating cashflows. Aggregate OCI and some of its components are also significantly associated with analysts’ earnings forecasts. Finally, some components of OCI are negatively significantly associated with analysts’ forecast errors. These results suggest that Ohlson’s (1999) description of transitory earnings might not apply to OCI, and that the reporting of comprehensive income in Canada did enhance the decision usefulness of accounting numbers. iii Additional analyses establish that the results are robust to differences observed for size, industry and macro economic variables such as interest rates, exchange rates and the rate of GDP growth. A significantly negative bias is observed for the period of adoption, but this could be attributed to the economic recession of 2007-2008.
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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 | 0.008 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".