Investigating the relationship between financial distress and investment efficiency of companies listed on the Tehran Stock Exchange
Bibliographic record
Abstract
The present study is aimed at investigating the relationship between financial distress and investment efficiency of companies listed in the Tehran Stock Exchange (with emphasis on the role of ownership type). To calculate investment efficiency, the Richardson’s model (2006) [Richardson, S. (2006). Over-investment of free cash flow. Review of Accounting Studies, 11(2-3), 159-189.] was employed. The aim of the present study is applied and its method is correlational- ex post facto. Using the exclusion sampling method and by applying the conditions of selecting the sample, 94 companies were selected from 2008 to 2013. To test the research hypotheses, multiple regression was used. Findings of the research indicate that there was a correlation between financial distress and investment efficiency in companies listed in the Tehran Stock Exchange, and institutional ownership had positive effects on the relationship between financial distress and investment efficiency of companies listed in the Tehran Stock Exchange. Furthermore, management ownership had no effect on the relationship between financial distress and investment efficiency in companies listed in the Tehran Stock Exchange.
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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 | 0.000 |
| 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".