The effect of financial distress on earnings management and unpredicted net earnings in companies listed on Tehran Stock Exchange
Bibliographic record
Abstract
Many financial crisis are related to public corporations, which are increasing. Many investors and creditors are having trouble predicting a financial crisis, especially when managing profits. Recent studies identify the factors associated with earnings management to determine the relationship between the factors and manipulated profits. In order to reduce the risk of financial crises and to help investors avoid large losses in the stock market, it is necessary to develop a model for predicting profit management. In addition, for traditional auditing technologies, it is also difficult to limit the time, human resources, costs, and the impact of abnormal behaviors on complex and large financial information. Therefore, developing a prediction model for managing profits for auditors is useful in identifying the degree of manipulation in financial statements. This paper examines the effect of corporate financial distress on unpredicted net earnings and corporate profits on accepted companies in Tehran Stock Exchange over the period 2010-2015. The models used to test the hypotheses of the research are linear regression using panel data. The results show that the coefficients of the financial distress, institutional ownership, annual sales growth, company loss, company size, the company's market share and firm fixed costs are statistically meaningful. In other words, these independent variables influence on unforeseeable profit and earnings management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".