Bibliographic record
Abstract
Purpose – The purpose of this paper is to assess the practice of income smoothing in the Gulf Cooperation Council (GCC) emerging markets; Saudi Arabia, Kuwait, United Arab Emirates, Oman and Qatar. Then, to examine the impact of income smoothing on the earnings quality to decide whether income smoothing can serve as either a tool to enhance earnings quality or a tool for opportunistic behavior. Audit quality and corporate governance as additional factors are considered in this study. Design/methodology/approach – The study methodology measures income smoothing behavior based on the coefficient of variation method. Earnings quality is measured as an outcome of the explained variations in stock returns by earnings based on the efficient market hypothesis. Audit quality is measured based on brand as higher quality assigned to auditor from any of the Big 4, while the corporate governance is addressed based on the extent of governmental ownership. The initial study sample comprises 55 companies over a ten year period, from 1999 to 2008; the final sample represents approximately 64 percent of the industrial sector that have public data during the study. Findings – The results suggest that income smoothing behavior in the GCC markets has many variations in practice. Income smoothing, on average, improves earnings quality in three countries out of four, but not significantly for the whole sample based on earnings level. The earnings changes model demonstrated a positive and significant impact of income smoothing on earnings quality. Audit quality and earnings quality have a positive relationship within the region, and companies dominated by the government perform well in accordance with the earnings-return model. Research limitations/implications – The study is limited to the industrial sector of the GCC. Practical implications – The study opens the door to future applications to other sectors within the GCC, same sectors and other sectors for Middle East countries and other emerging markets. Social implications – The study may foster a better understanding of accounting practices in the GCC and Middle East. The study reveals variations in different aspects among GCC countries, this matter should be considered in separate studies across different areas. Originality/value – The study makes an original contribution to being the first to explore this topic in the GCC. Additionally, this study shows that the GCC markets have different characteristics in the practice and impact of income smoothing on earnings’ quality. Further, audit quality and corporate governance was investigated for each country and for the region, in addition to the interaction between these factors with the income smoothing and earnings quality.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".