2008 finansal krizi döneminde işletmelerde piyasa verilerine dayalı olarak finansal başarısızlık olasılığının tahmini: İMKB uygulaması
Bibliographic record
Abstract
Bu çalışmada, firmaların finansal başarısızlık olasılıklarının piyasa verileri kullanılarak tahmin edilmesi amaçlanmaktadır. Finansal başarısızlık olasılığının tahmin edilmesi iki farklı Black Scholes Merton (BSM) opsiyon modeli ve iki farklı KMV modeli kullanılarak yapılmıştır. Modellerin uygulandığı dönem 2008 Finansal Krizinin etkili olduğu 2007-2008 ve 2009 yılları olarak belirlenmiştir. Modellerin uygulanma alanı ise İstanbul Menkul Kıymetler Borsası (İMKB) imalat sektöründe faaliyet gösteren 489 firma olarak belirlenmiştir. \nModellere göre, firmaların finansal başarısızlık olasılıkları hesaplanmış ve finansal başarısızlık kriterleri çerçevesinde elde edilen firmaların başarısızlık durumları ile karşılaştırılmıştır. Modellerin başarım güçlerinin analizi için ROC eğrisi yöntemi kullanılmıştır. Bilgi içerikleri ise modellerin genel sınıflama oranlarına bakılarak analiz edilmiştir. Çalışma sonucunda başarılı bulunun BSM modeli ve KMV modeli değişkenleri panel regresyon analizine tabi tutulmuştur. Yapılan analiz sonucunda firmaların finansal başarısızlık olasılıklarına firma değeri, firma yükümlülükleri ve varlık volatilitesi değişkenlerinin etki ettiği tespit edilmiştir. \n \nABSTRACT \nThe purpose of this study to estimate the financial failure probability of firms using market data. Estimating the probabilities of financial failure was made using two different Black Scholes Merton (BSM) option models and two different KMV models. The period in which the models were applied was determined as the 2007-2008 and 2009 years when the 2008 Financial Crisis was effective. The scope of application of the models is determined as 489 companies operating in Istanbul Stock Exchange (ISE) manufacturing sector. \nAccording to the models, firms' financial failure probabilities were calculated and compared with firm failures obtained within the framework of financial failure criteria. The ROC curve method was used to analyze the performance of the models. Information contents were analyzed by considering the general classification ratios of the models. The BSM model and KMV model variables were analyzed by panel regression analysis. As a result of the analysis, it has been determined that firm value, firm liabilities and asset volatility variables affect firms' financial failure probability.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".