Earnings quality as a predictor of firm performance: empirical analysis in the USA
Bibliographic record
Abstract
This study explores the link between firm performance and earnings quality in the USA, employing real earnings management (REM) and accruals earnings management (AEM) models. Through panel data robust regression analysis, we assess firm performance proxies, including return on assets, return on equity, operating cash flow, cash ratio, current ratio, and receivable accruals. Results highlight the significant impact of measures such as return on assets, return on equity, operating cash flow, cash ratio, current ratio, receivables accruals ratio, leverage, and firm industry on earnings quality. Notably, increasing liquidity and higher debt positively influence earnings quality, particularly in low-interest-rate environments. However, qualitative insights are essential for a comprehensive understanding of firm performance. Varied firm performance metrics exhibit distinct impacts on earnings quality, with return on assets and return on equity having the most significant effect. This research, integrating real and accrual earnings management, contributes original insights into corporate earnings management and firm performance in the USA context.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".