The Impact of Internal Financing and Institutional Investors Control on Cash Flows Resulting from Investment Fluctuations (Evidences from Tehran Stock Exchange)
Bibliographic record
Abstract
The aim of the present research is to study the impact of financing within a company and institutional investors control on cash flows resulting from investment fluctuations in companies listed on Tehran Stock Exchange. To this end, historical data of 103 companies listed on Tehran Stock Exchange in the time period of 2007 - 2012 were extracted and analyzed by using panel data. Results indicated that the more increase in cash flows, the more companies have interest for investment. In other words, investment fluctuations are sensitive to cash flows. Also, increased financing within a company reduces company's interest for investment of cash flows and vice versa. To this end, increased supervision of institutional investors increases company's interest for investment of cash flows and reduced supervision of institutional investors reduces company's interest for investment of cash flows. However, this effect was not statistically significant. Also, it was indicated that when there was an increase in activity volume of a company, on the basis of assets, there was an increase in company interest for investment of cash flows and a decrease in activity volume of a company, on the basis of assets, and the company interest for investment of cash flows. Also, increased cash dividends payment increases company interest for investment of cash flows and reduced cash dividends payment reduces company's interest for investment of cash flows. Age of the company and financial leverages did not have any effect on investment fluctuations sensitivity to cash flows.
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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 | 0.001 |
| 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".