A study on the effect of earnings management on restatement and the changes on information content of earnings following restatements: Evidence from Tehran Stock Exchange
Bibliographic record
Abstract
The current impressive increase in the number of the restatements, encourage many researchers to find the causes of restatements including earnings management.Moreover, restatements increase the uncertainties of investors about earnings and decrease its information content.Therefore, the purpose of this research is to investigate the relationship between earnings management and earnings restatements.In addition, this paper examines the information content of earnings and cash flow following restatements period.For this purpose, we use one logistic regression and three multiple regressions with OLS method over the period 2001-2011.The results indicate that there was no significant relationship between discretionary accruals and earnings restatement, but magnitude of the discretionary accruals as the proxy of earnings management was significant and positively associated with the earnings restatement.Moreover, the earnings had more information content than cash flow before and after the earnings restatement.The overall result suggests that the one reason for earnings restatement is to make earnings management in an emerging market.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".