The Effect of Quality and Timeliness of Limited Review Report on Perceived Interim Financial Reporting Quality during COVID-19 Pandemic Crisis: Evidence from Egypt
Bibliographic record
Abstract
In order for financial information to be used by investors to take informed decisions, it should be characterized by its relevance and faithful representation. In times of crisis, investors’ demand and reliance on financial information will be higher in order to reduce the level of uncertainty and information asymmetry and increase their confidence in management’s performance. The objective of this study is to investigate and analyze the impact of quality of limited review reports, measured by adding a key audit matters paragraph (KAM hereafter) related to COVID-19 pandemic to the limited review report and its timeliness, measured by the limited review report lag, on the perceived quality of interim financial reports issued at the end of the third quarter of 2020. Based on a sample of 95 firms listed on the Egyptian stock exchange (EGX hereafter), the researcher found that the timeliness of limited review reports is positively and significantly associated with the perceived quality of financial reporting from the investors’ point of view. Investors appreciate and value the timeliness of financial reports and the limited review report during COVID-19 pandemic crisis time. Concerning the impact of the quality of limited review report, the researcher didn’t find evidence regarding the informational value of KAM paragraph and its effect on the perceived quality of interim financial reports. This might be because adding KAM paragraph on COVID-19 pandemic is not firm specific and at the same time, not presenting additional information to investors other than that available in the interim financial reports.
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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 | 0.017 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".