An Investigation of The Income Smoothing Behavior of Growth and Value Firms (Case Study: Tehran Stock Exchange Market)
Bibliographic record
Abstract
The major objective of this study is to investigate the income smoothing behaviour of two different types of firms - value and growth - in the Tehran Stock Exchange (TSE) Market. All firms listed in the TSE between 2003 and 2007 were examined using the Jones Model to investigate their income smoothing behaviours. Using the Jones model, the discretionary part of accruals was investigated. The results of this study revealed that growth firms tend to apply discretionary accruals more intensively than value firms. In order to support the robustness of the findings, the Eckel model, was also applied. The same result was found for the Eckel model as for the Jones model. The results indicated that growth firms achieved a higher degree of income smoothing than value firms. The effects of various confounding factors which are different between these two types of firms, such as size of the company, standard deviation of earnings, market capitalization and consecutive trend of earnings, were also investigated. The results indicated that income smoothing in growth firms is larger than in value firms, and also that other items, which are known as representatives of the risk, are larger for growth firms than for value firms.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".