Top Companies Ranking Based on Financial Ratio with AHP-TOPSIS Combined Approach and Indices of Tehran Stock Exchange - A Comparative Study
Bibliographic record
Abstract
This paper aims to explore the relationship between ranking of the top-50 listed companies on Tehran Stock Exchange (TSE) for the years 2009- 2011 in terms of their liquidity, operation, leverage and profitability ratios using combined AHP-TOPSIS approach and the ranking made by the stock exchange. Ranking of the companies on the stock exchange is done based on their state in terms of the above ratios and it serves as a criterion for decision making on investment. Using a questionnaire, views of experts, scholars and the capital market authorities on the effect of financial ratios were gathered and then using AHP- TOPSIS technique the companies were ranked based on these ratios. The obtained results from the Spearman Test show a weak correlation between the rankings based on AHP-TOPSIS approach and the ranking of the stock exchange. Finally, our results indicate that financial ratios of the top stock exchange selected companies are crucial factors in investment and ranking. This paper contributes to a signaled need for investigation on how and why financial statements of top listed companies cannot be regarded as a critical factor in ranking and decision making on investment.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".