The Impact of Financial Distress on Earnings Management: The Moderating Role of Audit Quality
Bibliographic record
Abstract
The main objective of this study is to analyze the interrelationship between financial distress, earnings management, and audit quality of listed firms in Ghana. The study sampled 16 non-financial firms listed on the Ghana Stock Exchange from 2010 to 2022. Secondary data source was utilized in the study which were the annual reports of these listed firms. The research employed a quantitative approach, using random effect regression. The study found that financial distress had a statistically significant positive relationship with earnings management in Ghanaian firms. Audit quality exhibited a significant negative relationship with earnings management, indicating that higher audit quality can lead to lower earnings management. It was also found that audit quality did not moderate the relationship between financial distress and earnings management. To mitigate the inclination toward earnings management in times of financial distress, firms should strengthen their internal control systems and corporate governance practices. This could involve establishing stricter oversight mechanisms, such as forming an independent audit committee that regularly reviews financial reporting processes and outcomes. This study is the first study to analyze the interrelationship between financial distress, earnings management, and audit quality of listed firms in Ghana.
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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 | 0.001 | 0.010 |
| 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) | 0.003 | 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".