MAKİNE ÖĞRENME YÖNTEMLERİYLE KURUMSAL İFLASLARIN TAHMİN EDİLMESİ: ABD ÖRNEĞİ

Year 2022, , 1007 - 1031, 28.12.2022

Gerçek Özparlak , Menevşe Özdemir Dilidüzgün

https://doi.org/10.17130/ijmeb.1079688

Cited By: 1

Abstract

2019 yılı sonunda ortaya çıkan Covid-19 salgını ile ilgili beraber uluslararası hastanelerin ve sağlık kurumlarının birçoğu ciddi mali zorluklarla karşı karşıya kalmışlardır. Önümüzdeki yıllarda, bu mali zorlukların daha fazla sayıda iflasa sebep olacağı düşünülmektedir. Şirket iflaslarının önceden tahmin edilebilmesi şirket ortaklarını, yatırımcılarını, şirket alacaklılarını ve sağlık sektörünün devamlılığını korumak için önemlidir. Bu alandaki doğru tahminler sayesinde şirket yöneticileri gerekli tedbirleri alarak şirket iflaslarını önleyebilir ve yatırımcılar da zararlarını sınırlandırabilir. Bu çalışmanın amacı, yapay sinir ağlarını (YSA) kullanarak sağlık sektöründeki iflasları tahmin edebilen bir model oluşturmaktır. Bu çalışmanın örneklemi için, ABD’de 01.01.2018 ile 31.12.2020 tarihleri arasında sağlık sektöründe bulunan 23 adet iflasını açıklayan şirket ve kontrol grubu olarak da aynı dönemde ve aynı sektörde bulunan fakat finansal bir sıkıntısı bulunmayan 23 adet şirket seçilmiştir. Bu firmalara ait olan 30 adet finansal oran, araştırmanın girdi verisi olarak kullanılmıştır. Çalışmada, yapay sinir ağları (YSA) yöntem olarak seçilmiştir. Araştırmanın sonuçlarına göre, eğitim seti verisi kullanılarak oluşturulan yapay sinir ağları modellerinin doğru sınıflandırma oranı %100 olarak gerçekleşmiştir. Test seti verisi kullanılarak oluşturulan yapay sinir ağları modellerinin, doğru sınıflandırma oranı %90 olarak gerçekleşmiştir. Araştırma sonuçlarına göre, YSA’lar yüksek sınıflandırma başarıları ve kullanım kolaylıkları ile şirket iflaslarının tahmini için gelecek vaat etmektedirler. Bu nedenle hem araştırmacılar hem de yatırımcılar için kullanılmaları tavsiye edilmektedir.

Keywords

Finansal Başarısızlık, İflaslar, Finansal Başarısızlık Tahmin Modelleri, Yapay Sinir Ağları, Sağlık Sektörü

CORPORATE BANKRUPTCY PREDICTION USING MACHINE LEARNING METHODS: THE CASE OF THE USA

Abstract

Many of the international hospitals and health institutions have faced serious financial difficulties with the Covid-19 epidemic that emerged at the end of 2019. In the coming years, these financial difficulties are expected to cause more bankruptcies. Being able to predict company bankruptcies is important to protect company partners, investors, company creditors and the continuity of the healthcare industry. Thanks to accurate forecasts in this area, company managers can prevent company bankruptcies by taking the necessary precautions and investors can limit their losses. This study aims to build a model that can predict bankruptcies in the health sector by using artificial neural networks (ANN). For the sample of this study, 23 companies in the health sector that declared bankruptcy between 01.01.2018 and 31.12.2020 in the USA, and 23 companies that were in the same period and the same sector but had no financial problems were selected as the control group. 30 financial ratios belonging to these companies were used as input data of the research. In the study, artificial neural networks (ANN) were chosen as the method. According to the results of the research, the correct classification rate of the artificial neural network models created using the training set data was 100%. The correct classification rate of artificial neural network models created using test set data was 90%. According to the results of the research, ANNs are promising for the prediction of company bankruptcies with their high classification success and ease of use. Therefore, it is recommended to be used by both researchers and investors.

Keywords

Financial Failure, Bankruptcies, Financial Failure Prediction Models, Artificial Neural Networks, Health Sector

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