Performance Assessment of Real Estate Investment Trusts (REIT) Listed in BIST Via Different Multi Criteria Decision Making Methods
Bibliographic record
Abstract
<p>Firms’ performance assessment which gained crucial importance in last decades is essential issue for decision makers in financial sector. They can acquire competitive power by this way. In this study financial performance of twelve real estate investment trusts (REITs) listed in BIST is analyzed by using four financial indicators within the period of 2011-2015. Therefore firstly weights of criteria related to financial ratios are obtained by using Chang’s Extent Analysis Method on Fuzzy Analytic Hierarchy Process (FAHP). Following to this firms’ final rankings are determined by means of TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and VIKOR (Vise Kriterijumska Optimizacija I Kompromisno Resenje) methods respectively. Also ranking performance of these two methods is interpreted.</p>
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".