BWM—RAPS Approach for Evaluating and Ranking Banking Sector Companies Based on Their Financial Indicators in the Saudi Stock Market
Bibliographic record
Abstract
Seeking the greatest possible return on long-term investments, investors naturally seek equities of the best-performing companies that fit their investment timeframe. Long-term investment success rests on selecting the best companies, which requires a challenging analysis reviewing voluminous and often-conflicting data about companies and understanding broader economic forecasts. This paper undertook a case study deployment of MCDM methodologies to examine the suitability and effectiveness of Multi-Criteria Decision-Making (MCDM) methods in assessing and ranking the best stocks for portfolio inclusion. A combination of MCDM techniques comprised a methodology to evaluate and rank Saudi Arabian banking stocks based on their performance in the Saudi stock market. Specifically, the paper combined the Best–Worst Method (BWM) and Ranking Alternatives by Perimeter Similarity (RAPS) for the analysis. BWM calculated each criterion’s relative impact (weight) in selecting a stock. RAPS then used the weighting to rank the results of the investigation. The study’s findings yielded encouraging results regarding using an integrated MCDM technique to derive optimal banking sector securities in the expansive Saudi stock market. The novel application of the robust RAPS technique combined with BWM encourages continued and increased use of MCDM techniques in financial matters and broader application in evaluating equities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".