?MKB metal ana endeksine kote olan ?irketlerin etkinliklerinin veri zarflama ile analizi
Bibliographic record
Abstract
Etkinlik ?l?me y?ntemlerinden olan Veri Zarflama Analizi yard?m?yla ?MKB Metal Ana Endeksi'ne kote olan ?irketlerin etkinli?i ara?t?r?lm??t?r. ?irketlerin etkinli?ini belirlemek amac?yla yap?lan ?al??mada ?irket bilan?olar? kullan?lm?? ve bilan?olardan hareketle ara?t?rma de?i?kenleri olan finansal rasyolar belirlenmi?tir. Firmalar?n performanslar?n? etkileyece?i d???n?len temel finansal rasyolar girdi ve ??kt? de?i?kenler olarak kullan?lmaktad?r. Uygulamada, ?irketlerin kriz d?nemi etkinlikleri 2008 3. ?eyrek ile 2009 2. ?eyrek aras?ndaki 4 d?nem i?in hesaplanm??t?r. Veri zarflama analizi ile belirlenen d?rt d?nemin etkinlikleri birbiriyle kar??la?t?r?lm??t?r. ?irketlerin ?MKB'de hisse senetlerinin i?lem g?rmesi ve genel bir de?erleme yapmak i?in f?rsat sunmas? nedeniyle ayn? d?nemde etkinlikleri ile ?MKB fiyat de?i?imi kar??la?t?rmas? yap?lm??t?r. Firmalar?n karl?l??? ile veri zarflama analiziyle elde edilen etkinlik sonu?lar? aras?nda y?ksek bir ili?ki bulunamam??t?r. Bunun en ?nemli nedeni finans piyasas?ndaki spek?latif i?lemler olarak d???n?lm??t?r. Tezin amac?na uygun olarak veri zarflama analizinin ?irketlerin etkinli?ini belirleme ve yat?r?mc?ya yol g?stermesi a??s?ndan fiyat de?i?imine g?re daha g?venilir sonu? verdi?i g?sterilmi?tir. Efficiency of businesses quoted to the IMKB overall metal index had been searched by using Data Envelopment Analysis, one of the efficiency measurement methods. In the study committed for the purpose of determining the businesses' efficiency, balance sheets of the firms had been used and through these balance sheets, variables of the study, financial ratios, had been determined. Fundamental financial ratios, those thought to affect the firm performance, had been used as the input and output variables. In the application, businesses' crisis period efficiency had been calculated for four periods including those between the third quarter of 2008 and second quarter of 2009. The efficiency of those four periods determined by data envelopment analysis had been compared. Efficiency of businesses and IMKB price fluctuations had been compared for the same periods because their shares trade on stock exchange and also it allows making an overall evaluation. A high level of relation between business profitability and efficiency results derived by data envelopment analysis hasn't been found. Main cause of this situation is thought to be the speculative operations in financial markets. In accordance with the aim of the study, it is denoted that data envelopment analysis gives more reliable results, in terms of determining business efficiency and guide to investors.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.073 | 0.024 |
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".