Effect of Earnings Management on Earnings Quality and Sustainability: Evidence from Gulf Cooperation Council Distressed and Non-Distressed Companies
Bibliographic record
Abstract
This study evaluates the effect of earnings management on earnings quality and sustainability in the GCC region, particularly in distressed and non-distressed companies. Studies on earnings quality and sustainability have mostly concentrated on developed markets, with little attention paid to emerging markets like the GCC region. This research is the first to examine how manipulating earnings impacts the quality and sustainability of earnings in distressed and non-distressed companies. This study utilized a unique dataset that represents the GCC region, which has a specific socio-cultural context. We collected data from 839 publicly listed companies in the GCC region between 2011 and 2022 using DataStream®, WorldScope (WS), and Refinitiv Eikon. To test our hypotheses and ensure accuracy, we used three types of regressions (the fixed effects model, OLS, and 2SLS) and conducted robustness and endogeneity tests. The results of this study indicate that accruals-based earnings management has a negative impact on earnings quality for distressed and non-distressed firms but a positive effect on earnings sustainability for both types of companies. The results of this study also find variations in earnings management practices across industries. These findings provide valuable guidance for auditors, investors, and other stakeholders to evaluate the earnings quality and sustainability of distressed and non-distressed companies, benefiting the GCC economy and similar economies.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".