Assessing Financial Reporting Quality of Listed Companies in Developing Countries: Evidence from Ghana
Bibliographic record
Abstract
The adoption of International Financial Reporting Standards (IFRS) in Ghana is expected to improve the quality of financial reporting among companies in Ghana. This paper assesses the extent to which financial reports of companies listed on the Ghana Stock Exchange (GSE) meet financial reporting quality (FRQ) dimensions of IFRS. It was a descriptive study that employed two experienced professional chartered accountants who practice as independent auditors to use FRQ criteria to assess financial reports of 20 purposively selected companies listed on GSE for 2012 and 2013. Given the high inter-rater reliability (r = .96, 95% C.I., p < .0001), the findings indicate that, overall, FRQ of the listed companies meet FRQ standards by 56.48%. Generally, the financial reports were 60.95% faithfully represented, 51.01% relevant, 50.10% understandable, 40.09% comparable and 19.75% timely audited (or 80.25% untimely). Fundamental FRQ characteristics were more prevalent than enhancing FRQ. Poorly rated FRQ areas were in the use of historical cost as measurement basis, no use of graphs and tables to clarify information, no inclusion of comprehensive glossary, ratios and index, no information on adjustment in past accounting figures for future decisions, and no comparison of current and previous accounting periods and with those of other firms. The study concludes that FRQ of the listed companies is moderate but needs considerable improvement. Implications to theory, practitioners, policy makers and industry regulators are discussed. This study fills the dearth of empirical research in FRQ in IFRS-compliance companies in Sub-Saharan Africa in general and Ghana in particular.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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".