Estimating Probability of Return on Loss and Its Effect on Future Abnormal Return in Iran
Bibliographic record
Abstract
The present study aims at investigating, at first, the relationship between the features of loss-making companies and the probability of achieving the first profit in the years following the loss; and then, analyzing the relationship between probable profitability of the loss-making companies and future abnormal return on shares. This study is carried out according to the information available on the companies listed on Tehran Stock Exchange, during 2002-2011, on a selected sample consisting of 72 companies. The method used for hypotheses testing is logistic regression and generalized regression model (linear mixed model). The results obtained from hypotheses testing for return on loss model in this study indicate the existence of a significant positive relationship between the level of the loss-making companies' investment in capital assets and the probability of return on loss; the existence of a significant negative relationship between accounting conservatism level and the probability of profitability; as well as lack of relationship between special expenses (R & D, sales promotion, inventions and discoveries) and the probability of return on loss. The results from the final model test show the significant positive relationship between the probability of return on loss and abnormal return on shares of the companies. The main limitation of the current study is the lack of proper disclosure of some of the variables of this study by firms, including investing in specific costs in financial statements and notes as separate and distinct costs from other expenditures.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".