The impact of creative accounting methods on earnings per share
Bibliographic record
Abstract
This study was aimed at investigating the impact of creative accounting methods called “Earnings Manage-ment and Income Smoothing” on earnings per share in the Jordanian industrial companies. The model of Dechow et al. (1995) [Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1995). Detecting earnings management. Accounting Review, 70(2), 193-225.] was adopted to measure earnings management, and the model of Francis et al. (2004) [Francis, J., LaFond, R., Olsson, P. M., & Schipper, K. (2004). Costs of equity and earnings attributes. The accounting review, 79(4), 967-1010.] was adopted to measure income smoothing. In order to achieve the objectives of the study, the analytical quantitative approach was adopted. The study community consisted of the 57 industrial companies listed on the Amman Stock Exchange (ASE). As for the study sample, 36 companies were selected according to the target sample method in the period from 2008 to 2017. The results showed that there was a statistically significant impact of using the creative accounting methods on earnings per share in the industrial companies listed on the ASE, and there was an impact of practicing both earnings management and income smoothing on earnings per share in the industrial companies listed on the ASE. The results also showed that 27.8% of the industrial companies practiced earning management, while 47.2% of the industrial companies practiced income smoothing.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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".