The Impact of Surplus Free Cash Flow, Corporate Governance and Firm Size on Earnings Predictability in Companies Listed in Tehran Stock Exchange
Bibliographic record
Abstract
Among the most important cases considered in financial statements by investors and other users of financial statements is earnings-related information. Given the need of the users of financial statements for the future information of companies and use of past data to predict the future, it seems that earnings forecast is among the favorite items of investors. In fact, earnings forecast by the management provides information about the future of companies. The main objective of the present study is to investigate the effect of surplus free cash flow, corporate governance and firm size on earnings predictability in companies listed in Tehran Stock Exchange. This research is an applied study and of post-event causal type. For data analysis, OLS regression method has been applied using Eviews software. The research results demonstrate that there is a statistically significant relationship between earnings predictability and surplus free cash flow and good corporate mechanisms play a positive role in the relationship between surplus free cash flow and earnings predictability. According to the results, in large companies, good corporate mechanisms enhance the relationship between surplus free cash flow and earnings predictability.
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How this classification was reachedexpand
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 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".