Institutional Ownership and Investment Efficiency: Evidence from Iran
Bibliographic record
Abstract
Investment efficiency shows how well a company invests its assets. Although institutional shareholders play undeniable roles in companies, it is not clear whether they are able to monitor managers and make investment decisions or not. This study gives answers to stakeholders, addresses concerns about the effect of the owners on investment efficiency, and aims to add to the literature on emerging markets by investigating the relationship in Iran, a different environment from developed ones. Based on monitoring power, the shareholders are divided into two types: active and passive ones. Investment problems are classified into two types: over- and under-investment problems. The sample consists of 101 firms listed on the Tehran Stock Exchange between 2010 and 2016. Some regression models are used. The results illustrated that institutional owners have a positive effect on investment efficiency and decrease both over- and under-investment problems and so, the efficient monitoring school is approved. Additionally, active ones are positively correlated with investment efficiency and decrease both investment inefficiency problems. Institutional ownership is the cause of investment efficiency, not the reverse. Based on findings, in emerging markets like Iran’s market, investors are recommended to give notice to the level of active ownership in firms; ownership structure is a good sign of efficiency.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".