An empirical investigation on ranking financial risk factors using AHP method
Bibliographic record
Abstract
This paper determines and ranks financial risk factors in Iranian corporations, using analytical hierarchy process (AHP).The present research includes one main question and four subquestions.Its universe population includes managers, production and financial personnel of great corporations activating in Tehran Stock Exchange, who were selected to explain importance and weight of economic risks indices.The source of great corporations recognition is the Companies Registration Organization in Tehran Province, and according to this, there are 120 corporations.The results have indicated that financing risk maintains the highest priority followed by credit risk, liquidity risk, inflation risk and exchange risk.In terms of different risks associated with financing risk, risk of profit per share has been the number one priority followed by the risk of divisional profit per share, the risk of recessionary or boom and the risk of increasing partial pay profit rate.In terms of credit risk, the risk of loan has been number one priority followed by the risk of inability of loan payment and interest payment.Liquidity risk is another risk factor where demand has been the most important factor followed by rules and regulations and inflation risk.In terms of inflation, producers price risk has been the most important factor followed by consumer price risk, gross domestic product and producers price risk.Finally, in terms of different factors influencing exchange risk, export related issues are considered as the most important factors.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".