Developing a hybrid multi-criteria model for investment in stock exchange
Bibliographic record
Abstract
One of the main challenges in Stock Market is to choose an appropriate combinations of various assets.The aim of this study is to propose a hybrid method, which is able to survey one problem with some criteria that it is very good for investment problem.In this study, we use a hybrid multiple criteria decision-making (MCDM) model, which shows the dependent relationships among criteria with DEMATEL method to build a relations-structure among criteria.We then use Analytical Network Process (ANP) to determine the relative weights of each criterion with dependence and feedback, and the VIKOR method is implemented to rank and select the best alternatives for investment.This study is in stock exchange in Iran to select the best stocks and the data are gathered through the years (2006)(2007)(2008)(2009)(2010).There are a lot of methods to rank and select of firms that most of the methods just do one, ranking or selecting; but the used method in this study not only ranks the firms but also determines which firms (stocks) are best for investment, so that in this study 2 of 50 firms are proposed for 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".