Sector-Specific Financial Failure Prediction Model: Agriculture, Forestry and Fisheries Sector

Koray Yapa; Metin Coşkun

Article Details

Sector-Specific Financial Failure Prediction Model: Agriculture, Forestry and Fisheries Sector

Authors:

Koray Yapa | Metin Coşkun

Publisher:

Eskişehir Osmangazi University

Main Topics:

Finans

Journal:

Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi Vol.19 Issue 2, pp.351-377

Document Type:

Article

Publication Date:

1/2024

Document Language:

Turkish

Document ID (Org No.):

2001312728

pISSN:

1306-6730

Keywords:

Abstract:

 In this study, it is aimed to create a sector-specific prediction model in order to eliminate the problems arising from creating a common model for businesses operating in different sectors in the financial failure literature. In this direction, prediction models were created by using the data of businesses operating in the Agriculture, Forestry and Fisheries sector between 2009 and 2019, taking into account the dynamics specific to the sector. In order to determine these dynamics, Random Forest, Stepwise Forward Backward Feature and K-Nearest Neighbors variable reduction methods were used and the performances of five different definitions of financial failure were evaluated. As a result of the evaluation of these definitions by Random Forest, Logistic Regression, Artificial Neural Network and K-Nearest Neighbors classification methods, the definition of FF_5, which includes the concepts of profitability and net working capital, differs from other definitions. As a result of the RF method applied to the model, 96.5 accuracy, 94.5 accuracy and 99 accuracy rates were calculated.

 In this study, it is aimed to create a sector-specific prediction model in order to eliminate the problems arising from creating a common model for businesses operating in different sectors in the financial failure literature. In this direction, prediction models were created by using the data of businesses operating in the Agriculture, Forestry and Fisheries sector between 2009 and 2019, taking into account the dynamics specific to the sector. In order to determine these dynamics, Random Forest, Stepwise Forward Backward Feature and K-Nearest Neighbors variable reduction methods were used and the performances of five different definitions of financial failure were evaluated. As a result of the evaluation of these definitions by Random Forest, Logistic Regression, Artificial Neural Network and K-Nearest Neighbors classification methods, the definition of FF_5, which includes the concepts of profitability and net working capital, differs from other definitions. As a result of the RF method applied to the model, 96.5 accuracy, 94.5 accuracy and 99 accuracy rates were calculated.

 In this study, it is aimed to create a sector-specific prediction model in order to eliminate the problems arising from creating a common model for businesses operating in different sectors in the financial failure literature. In this direction, prediction models were created by using the data of businesses operating in the Agriculture, Forestry and Fisheries sector between 2009 and 2019, taking into account the dynamics specific to the sector. In order to determine these dynamics, Random Forest, Stepwise Forward Backward Feature and K-Nearest Neighbors variable reduction methods were used and the performances of five different definitions of financial failure were evaluated. As a result of the evaluation of these definitions by Random Forest, Logistic Regression, Artificial Neural Network and K-Nearest Neighbors classification methods, the definition of FF_5, which includes the concepts of profitability and net working capital, differs from other definitions. As a result of the RF method applied to the model, 96.5 accuracy, 94.5 accuracy and 99 accuracy rates were calculated.

 Bu çalışmada finansal başarısızlık literatüründe farklı sektörlerde faaliyet gösteren işletmeler için ortak bir model oluşturulmasından kaynaklanan sorunları ortadan kaldırabilmek amacıyla sektöre özgü öngörü modeli oluşturmak amaçlanmıştır. Bu doğrultuda Tarım, Orman ve Balıkçılık sektöründe 2009-2019 yıllarında faaliyet gösteren işletme verileri kullanılarak sektöre özgü dinamiklerin dikkate alındığı öngörü modelleri oluşturulmuştur. Bu dinamiklerin tespit edilmesi amacıyla Rastgele Orman, Adım adım ileri geri seçim ve K-en yakın komşuluk değişken azaltma yöntemleri kullanılıp beş farklı finansal başarısızlık tanımının performansları değerlendirilmiştir. Bu tanımları Rastgele orman, Lojistik regresyon, Yapay sinir ağları ve K-en yakın komşuluk sınıflandırma yöntemlerince değerlendirilmesi sonucunda kârlılık ve net işletme sermayesi kavramlarını içeren FF_5 tanımı diğer tanımlardan farklılaşmıştır. Modele uygulanan RF yöntemi neticesinde 96,5 doğruluk, 94,5 kesinlik ve 99 hassasiyet oranlarına hesaplanmıştır.

Article Details

Sector-Specific Financial Failure Prediction Model: Agriculture, Forestry and Fisheries Sector

Authors:

Koray Yapa | Metin Coşkun

Publisher:

Eskişehir Osmangazi University

Main Topics:

Finans

Journal:

Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi Vol.19 Issue 2, pp.351-377

Document Type:

Article

Publication Date:

1/2024

Document Language:

Turkish

Document ID (Org No.):

2001312728

pISSN:

1306-6730

Financial Failure

Variable Reduction

Classification

Random Forest

Financial RatiosFinansal Başarısızlık

Değişken Azaltma

Sınıflandırma

Rastgele Orman

Finansal Oranlar

Issues

Article Details

Authors: Koray Yapa | Metin Coşkun

Publisher: Eskişehir Osmangazi University

Main Topics: Finans

Journal: Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi Vol.19 Issue 2, pp.351-377

Document Type: Article

Publication Date: 1/2024

Document Language: Turkish

Document ID (Org No.): 2001312728

pISSN: 1306-6730

Issues

Authors:

Koray Yapa | Metin Coşkun

Publisher:

Eskişehir Osmangazi University

Main Topics:

Finans

Journal:

Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi Vol.19 Issue 2, pp.351-377

Document Type:

Article

Publication Date:

1/2024

Document Language:

Turkish

Document ID (Org No.):

2001312728

pISSN:

1306-6730