The Role of Corporate Governance in Investment Efficiency and Financial Information Disclosure Risk in Companies Listed on the Tehran Stock Exchange
Bibliographic record
Abstract
This study’s primary purpose is to investigate corporate governance’s role in investment efficiency and financial information disclosure risk in companies listed on the Tehran Stock Exchange. A multivariate linear regression model based on the panel data model was used to test the research hypotheses. The results of the survey of 140 companies listed on the Tehran Stock Exchange from 2015 to 2021 indicate that investment efficiency has increased by increasing the quality of corporate governance. In addition, research findings show that improving the quality of corporate governance reduces the risk of financial information disclosure. The life cycle and firm size were used to evaluate the robustness of the results obtained in this study. It was observed that improving corporate governance in companies in the stages of growth and maturity increases investment efficiency and reduces the financial information disclosure risk. In contrast, in companies that are in the decline stage, it reduces investment efficiency and increases the risk of financial information disclosure. In terms of firm size, it was also observed that, in small firms, as corporate governance increases, investment efficiency decreases, and the risk of financial information disclosure increases. However, investment efficiency and financial information disclosure reduce risk by improving large companies’ corporate governance.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".