Stock Selection Based on Fundamental Analysis Approach by Grey Relational Analysis: A Case of Turkey
Bibliographic record
Abstract
Determining financial assets for investment is a significant problem for investors. Especially, since it is riskier in comparison with other financial assets, selecting common stock is not only important for investors but also, it is a complicated decision-making process, because, although they agree to accept a risk, common stock investors tend to keep the risk they undertake at a given level. With this purpose, investors prefer to conduct various analysis and predictions to decide which common stocks they will invest. In this study, by using micro and macro variables, which have been determined with a fundamental analysis approach, a common stock selection is conducted for nine different corporations doing activities in ten lines of business in Borsa Istanbul. In the study, Grey Relational Analysis (GRA), which is developed in the framework of Grey System Theory, is used as a mathematical model. Therefore, a conclusion is obtained about which parameters are more important in selecting a common stock, and the efficiency of GRA Method is tested.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".