Presentation Pattern and the Value Relevance of Comprehensive Income --- Evidence from China
Bibliographic record
Abstract
In 2006 the Chinese Ministry of finance(CMF) issued new accounting standards that required companies began to present comprehensive income information in the statement of equity. In 2009 and 2014, CMF changed the comprehensive income presentation pattern consecutively twice, from the equity statement pattern to the performance statement transition pattern, and then to the single performance statement. The purpose of these changes is to harmonize China Accounting Standard (CAS) with International Financial Reporting Standards(IFRS). It also aims to enhance the usefulness of comprehensive income information by improving the transparency of information disclosure. From the perspective of presentation patterns, the paper examines the influence of presentation pattern changes on the value relevance of comprehensive income (CI), and on other comprehensive income (OCI). The results show that, under the equity statement pattern, neither CI nor OCI was correlated with value. Under the performance statement transition pattern, both CI and OCI have the value relevance. Under the single performance statement pattern, the CI has higher value relevance, while the OCI does not reflect higher value relevance. This study reveals the impact of comprehensive income presentation pattern on the usefulness of decision making. It has certain inspiration and reference for improving the quality of accounting standards and financial reporting.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".