Compliance With International Financial Reporting Standards and Value Relevance of Accounting Information in South Africa
Bibliographic record
Abstract
The objective of the study is to investigate the relationship between the International Financial Reporting Standard (IFRS 1) and the value relevance (VR) of accounting information. In this study forty-six companies listed on the Johannesburg Stock Exchange during the period 1993 to 2017. Panel data is used to compare the period before and after IFRS. The companies in the sample are composed of the following sectors; mining, manufacturing, banks and investment companies, real estate, general industry, retailers, construction and material, chemical and software, and computers. Based on the yearly financial reports published by public companies in South Africa, the study employed the Cookes (1992) Unweighted Disclosure Index to measure the level of compliance in South Africa. Fifty-six disclosure elements from IFRS 1 were utilized to measure the compliance level. Thereafter Ohlson (1995) Model is used with dummy variables to compare the pre-and post-IFRS period. First, the study reflected that most of the South African companies exhibit higher compliance rates ranging from 87 to 93.417 which is impressive. On the other hand, 4 companies recorded Medium level compliance that is between 60% to 79% compliance level. The findings further revealed that there is a significant positive association between compliance with IFRS 1 and the value relevance of accounting information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".