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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".