An application of multiple attribute group decision making in ranking investors’ concerns: A case study of Tehran Stock Exchange
Bibliographic record
Abstract
During the past few years, stock exchange investors confront numerous problems in terms of legal, environmental issues, etc.In this paper, we present an empirical study to detect important issues as barriers for investment in Tehran Stock Exchange.The study has categorized the issues into two groups of real world and legal issues.Since there are different issues involved as major barriers, the study uses analytical hierarchy process to rank them.The study extracts 18 important factors, which influence investors' participation in Tehran Stock Exchange and using Borda method, prioritize them.The results of the survey indicate that in terms of real issues, Increase in quality of firms Financial Statement is number one priority followed by Increase stock exchange and agents' proficiency and electronic equipment, Unchangeable investment market rules and bounding organization managers to flow them and Strict supervision on agents' activities.In terms of legal issues, good supervision of provisions to force firms to reveal information correctly and restrict their secret bargaining is the most important factor followed by Using indirect investment guideline instead of direct investment, Increase in quality of firms' Financial Statement and Investors' training toward their rights.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".