The effect of financial distress on earnings management and unpredicted net earnings in companies listed on Tehran Stock Exchange
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it