Promethee sıralama yöntemi ile portföy oluşturma ve Borsa İstanbul’da 2010-2015 yılları arasında bir uygulama
Bibliographic record
Abstract
Yatırım ile ilgili kararlar verilirken kişiler hem kendi tecrübelerini yansıtacakları hem de bilimsel gerçekleri kullanabilecekleri yöntemler ararlar. Kullanılacak yöntemin kolaylığı ve hızlı sonuç vermesi yöntemde aradıkları ilk şart olmaktadır. Tecrübelerini yansıtabildikleri ve bilimsel bir yöntem olan Promethee sıralama yöntemi çoğu yatırımcı için uygun bir yöntemdir. Bu çalışmada portföy oluştururken tercih edilecek olan hisse senetlerinin seçiminde çok kriterli karar verme tekniği olan Promethee kullanılması amaçlanmıştır. Yöntem seçilirken Promethee’nin finansal kararların alınmasında kullanılabilirliği ve yöntemin uygulanmasının kolaylığı göz önünde bulundurulmuştur. Uygulamaya 2016 yılının ilk iki çeyreğinde BIST-100 endeksinde yer alan şirketler dahil edilmiş ve 2010-2015 yılları arasındaki verileri incelenmiştir. İşlem miktarı, işlem hacmi ve volatilite kriterleri açısından portföye dahil edilmesi gereken hisse senetleri tespit edilmiş, Promethee sıralama yönteminin portföy oluşturmada yararlı bir araç olduğu belirtilmiştir. \n \n-------------------- \nInvestors seek methods in which they can reflect their own experiences and use scientific realities in decision making process. Two of the most important conditions of method choice are convenience and quick result. Promethee ranking method is suitable for the most investors. This study aims to use Promethee, the multi criteria decision making method, for selecting share stocks by creating portfolio. Promethee is easy to use in decision making process and easy to apply. These features are considered by preferring this method. Companies in first two quarter of 2016 BIST-100 Index are involved to the application and share stocks between the years of 2010-2015 are analyzed. Share stocks are located according to such criteria as trade quantity, volatility, traded value and it is stated that Promethee ranking method is useful by creating portfolio.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.008 |
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".