Applicability of Fuzzy TOPSIS Method in Optimal Portfolio Selection and an Application in BIST
Bibliographic record
Abstract
General structure of saving-investment cycle and the effectiveness of this structure are included in the most significant issues of the financial system. One of the points of intervention in providing an effective saving-investment cycle is possible through channeling the savings toward optimal investment fields. This study aims at detecting the existence of alternative methods in determining optimal selection combination in the risk and revenue perspective of individual and corporate investors who would like to evaluate their savings in capital markets. For this purpose, the applicability of Fuzzy TOPSIS method, one of the multi-criteria decision making techniques in optimal portfolio selection was researched. The applicability of the stock investment alternatives ranked according to Fuzzy TOPSIS method was examined by comparing them to the optimal selection results determined according to Markowitz, one of the modern portfolio management techniques. In the study where performance indexes were used as assessment criteria the results of both methods were discussed in terms of risk at a certain revenue level and revenue at a certain risk level through Johnson and Sharp Indexes. The results obtained determined that the Fuzzy TOPSIS portfolio alternatives created using Fuzzy TOPSIS method revealed quite positive results in terms of performance, revenue and risk and pointed at applicability of Fuzzy TOPSIS method in optimal portfolio selection as well.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".