An Application of Analytical Hierarchy Process to Financial Asset Selection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Asset selection involves picking up a particular asset within each asset class in such a manner that it would outperform the rest of the assets to maximize the investor’s goal of increasing value while mitigating risk. This paper aims to implement a widely used “multi-criteria decision making (MCDM)” technique known as Analytical Hierarchy Process (AHP) for best asset selection. In AHP, the qualitative problems are described and transformed quantitatively, and then the quantitative analysis is used to find the relationship among various decision criteria. In this study, ethical and suitability criteria are used along with the financial criteria to rank the assets based on individual investors’ preferences. To demonstrate the efficacy of the problem, a hypothetical data set is used for ethical and suitability criteria, and data set of 10 assets of Nifty 50 companies under the National Stock Exchange (NSE) is collected for financial criteria.
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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 | 0.006 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it