MACHINE LEARNING METHODS IN CAPITAL STRUCTURE DECISIONS; APPLICATION IN BIST

Şafak Sönmez Soydaş

Makale detayları

MACHINE LEARNING METHODS IN CAPITAL STRUCTURE DECISIONS; APPLICATION IN BIST

Yazarlar:

Şafak Sönmez Soydaş

Yayıncı:

Karamanoglu Mehmetbey University

Ana Başlıklar:

Ekonometrik ve İstatistiksel Yöntemler | Finansal Ekonomi

Dergi:

Karamanoğlu Mehmetbey Üniversitesi Sosyal Ve Ekonomik Araştırmalar Dergisi Vol.27 Issue 48, pp.94-110

Belge Tipi:

Makale

Yayın Tarihi:

4/2025

Belge Dili:

Türkçe

Document ID (Org No.):

2001345592

pISSN:

2147-7833

Anahtar kelimeler:

Özet:

 This study was conducted to create a model based on machine learning methods to determine and develop firm-specific factors that significantly affect the funding decisions of firms. The study consists of firms listed in the forest, paper and printing index of Borsa Istanbul between 2014-2023, and their capital structure decisions were analyzed with machine learning methods. As a result of the analyzes made using machine learning methods, it was determined that the performance values of the model created with the XGBoost method and the Random Forest model, in particular, performed better than the AdaBoost and SVM models. It was seen that the models created with the XGBoost and Random Forest methods were a better option for determining corporate capital structures. It was understood that the SVM model had the worst performance value.

 This study was conducted to create a model based on machine learning methods to determine and develop firm-specific factors that significantly affect the funding decisions of firms. The study consists of firms listed in the forest, paper and printing index of Borsa Istanbul between 2014-2023, and their capital structure decisions were analyzed with machine learning methods. As a result of the analyzes made using machine learning methods, it was determined that the performance values of the model created with the XGBoost method and the Random Forest model, in particular, performed better than the AdaBoost and SVM models. It was seen that the models created with the XGBoost and Random Forest methods were a better option for determining corporate capital structures. It was understood that the SVM model had the worst performance value.

 This study was conducted to create a model based on machine learning methods to determine and develop firm-specific factors that significantly affect the funding decisions of firms. The study consists of firms listed in the forest, paper and printing index of Borsa Istanbul between 2014-2023, and their capital structure decisions were analyzed with machine learning methods. As a result of the analyzes made using machine learning methods, it was determined that the performance values of the model created with the XGBoost method and the Random Forest model, in particular, performed better than the AdaBoost and SVM models. It was seen that the models created with the XGBoost and Random Forest methods were a better option for determining corporate capital structures. It was understood that the SVM model had the worst performance value.

 Bu çalışma, firmaların fonlama kararlarını önemli ölçüde etkileyen firmaya özgü faktörleri belirlemek ve geliştirmek için makine öğrenmesi yöntemlerine dayalı bir model oluşturmak amacıyla gerçekleştirilmiştir. Çalışma, 2014-2023 yılları arasında Borsa İstanbulda orman, kâğıt ve basım endeksinde sıralanan firmalardan oluşmakta olup makine öğrenmesi yöntemleri ile sermaye yapısı kararları analiz edilmiştir. Makine öğrenmesi yöntemleri kullanılarak yapılan analizler sonucunda oluşturulan modeller içerisinde özellikle XGBoost yöntemi ile oluşturulan model ve Random Forest modelinin performans değerlerinin AdaBoost ve SVM modeline göre daha iyi performans gösterdiği tespit edilmiştir. Özellikle XGBoost ve Random Forest yöntemi ile oluşturulan modellerin kurumsal sermaye yapılarının belirlenmesi için daha iyi bir seçenek olduğu görülmüştür. En kötü performansın ise SVM modeli olduğu anlaşılmıştır.

Makale detayları

MACHINE LEARNING METHODS IN CAPITAL STRUCTURE DECISIONS; APPLICATION IN BIST

Yazarlar:

Şafak Sönmez Soydaş

Yayıncı:

Karamanoglu Mehmetbey University

Ana Başlıklar:

Ekonometrik ve İstatistiksel Yöntemler | Finansal Ekonomi

Dergi:

Karamanoğlu Mehmetbey Üniversitesi Sosyal Ve Ekonomik Araştırmalar Dergisi Vol.27 Issue 48, pp.94-110

Belge Tipi:

Makale

Yayın Tarihi:

4/2025

Belge Dili:

Türkçe

Document ID (Org No.):

2001345592

pISSN:

2147-7833

Capital Structure

Financial Ratio

Machine LearningSermaye Yapısı

Finansal Oran

Makine Öğrenme

Sayı

Makale detayları

MACHINE LEARNING METHODS IN CAPITAL STRUCTURE DECISIONS; APPLICATION IN BIST

Yazarlar: Şafak Sönmez Soydaş

Yayıncı: Karamanoglu Mehmetbey University

Ana Başlıklar: Ekonometrik ve İstatistiksel Yöntemler | Finansal Ekonomi

Dergi: Karamanoğlu Mehmetbey Üniversitesi Sosyal Ve Ekonomik Araştırmalar Dergisi Vol.27 Issue 48, pp.94-110

Belge Tipi: Makale

Yayın Tarihi: 4/2025

Belge Dili: Türkçe

Document ID (Org No.): 2001345592

pISSN: 2147-7833

Capital Structure

Financial Ratio

Machine LearningSermaye Yapısı

Finansal Oran

Makine Öğrenme

Sayı

Yazarlar:

Şafak Sönmez Soydaş

Yayıncı:

Karamanoglu Mehmetbey University

Ana Başlıklar:

Ekonometrik ve İstatistiksel Yöntemler | Finansal Ekonomi

Dergi:

Karamanoğlu Mehmetbey Üniversitesi Sosyal Ve Ekonomik Araştırmalar Dergisi Vol.27 Issue 48, pp.94-110

Belge Tipi:

Makale

Yayın Tarihi:

4/2025

Belge Dili:

Türkçe

Document ID (Org No.):

2001345592

pISSN:

2147-7833