Yapay Zeka Yöntemleri ile İşletmelerin Finansal Başarısızlığının Tahmin Edilmesi: Bist İmalat Sektörü Uygulaması

Yürük, Muhammed fatih; Ekşi̇, İbrahim Halil

doi:10.19059/mukaddime.533151

Cited by 9 publications

(6 citation statements)

References 5 publications

(5 reference statements)

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“…According to the results of the research, the accuracy rate of the SVM models was 79.03%. In other literature studies on the prediction of financial failures with SVMs, Yürük et al (2019) 72.88%, Shafiee et al (2021) approximately 88.8%, Ptak-Chmielewska (2021) 72.1%. According to the results of the research, the accuracy rate of the artificial neural network models developed using the training set data of the model obtained by ANN is 88.89%.…”

Section: Discussionmentioning

confidence: 91%

“…The empirical result shows that the ANN and nearest neighbour methods are the most accurate. In their study, Yürük & Ekşi (2019) used the data of 140 enterprises in the manufacturing sector traded on the BIST between 2008 and 2016. As a financial failure criterion; They have accepted the condition of losing 10% of their assets for 2 consecutive years.…”

Section: Conceptual Frameworkmentioning

confidence: 99%

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Corporate Bankruptcy Prediction Using Machine Learning Methods: The Case of the Usa

ÖZPARLAK

DİLİDÜZGÜN²

2022

ijmeb

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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.

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Section: Discussionmentioning

confidence: 91%

Section: Conceptual Frameworkmentioning

confidence: 99%

Corporate Bankruptcy Prediction Using Machine Learning Methods: The Case of the Usa

ÖZPARLAK

DİLİDÜZGÜN²

2022

ijmeb

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“…It was also found that SVM did not perform better than traditional statistical methods. Yürük and Ekşi (2019) used the data of 140 businesses in the manufacturing sector traded in Borsa Istanbul (BIST) between 2008 and 2016. In this study, bankruptcy, taking part in the BIST detention market, cessation of operations, having a loss for two consecutive years and losing 10% of the asset amount were considered as financial failure criteria.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As the prediction time increases, the model accuracy starts to decrease (Jardin and Séverin, 2011, p.710) Although the models used in the literatüre (e.g. Yakut (2012), Yürük and Ekşi (2019)) are significantly different depending on the modeling method, the variables used and the sampling used, there is a common feature that the classification accuracy decreases significantly when the prediction time exceeds one year. Model accuracy decreases by 15% on average between 1 and 3 years.…”

Section: Financial Failure Indicators Based On Financial Statementsmentioning

confidence: 99%

Comparison of Classification Performance of Machine Learning Methods in Prediction Financial Failure: Evidence From Borsa İstanbul

Aksoy

Boztosun

2021

Hitit Sosyal Bilimler Dergisi

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This study aimed to predict the 1 to 2 year future time of the financial failure of 86 manufacturing companies that are operating in Borsa İstanbul. The data comprised of 2010-2012 period, and it depends on 8 quantitative financial variables. Beside 6 variables come from non financial statements. In the study, Artificial Neural Network (NN), Classification and Regression Trees (CART), Support Vector Machine (SVM) and k-Nearest Neighbors (KNN) were used to compare classification performances of related methods. ROC Curve was used to compare the classification performance of the methods. As a result of the analyseis, the overall classification accuracy from the highest to the lowest was SVM (92,31%), CART (88,46%), ANN (84,62%) and KNN (80,77%) 2 years before the financial failure. The overall classification accuracy from the highest to the lowest was CART (96,15%), ANN (92,31%), SVM (80,77%) and KNN (84,62%) 1 year before the financial failure. Return on Equity (ROE) and Return on Assets Ratio (ROA) were found as important variables in the creation of the CART decision tree. The fact that the four models obtained in thise study predicted financial success/failure at a higher rate, and it shows that the models obtained in this study can be included in the models used by relevant people.

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“…, Akkaya, Demirelli ve Yakut (2009),Yıldız ve Akkoç (2009),Çelik (2010),Ekinci vd. (2010),Altunüz (2013),Altınırmak ve Karamaşa (2016),Yürük ve Ekşi (2019) çalışmalarında tarafımızca belirlenen değişkenler ile yapay sinir ağı modellerinin etkinliğini test etmişlerdir. Analize dahil edilen değişkenler ilgili çalışmalar baz alınarak belirlenmiştir.…”

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Yapay Sinir Ağı Modeli ile BİST'e Kote 10 Bankanın Covid-19 Dönemindeki Finansal Verilerinin Tahminlenme Başarısı Üzerine Bir Araştırma

Varsak

ÖZKAN

2022

The Journal of International Scientific Researches

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Yapay sinir ağı modeli, yapay zekâ ve makine öğrenmesi modellerinin temelini oluşturmaktadır. Yapay sinir ağı modelinden yola çıkılarak evrişimli sinir ağları, destek vektör makine ve genetik algoritmalar gibi pek çok farklı model türetilmiştir. Bu çalışma yapay zekâ teknolojilerinin temelini oluşturan, yapay sinir ağı modelinin Covid-19 döneminde, bankaların finansal verilerine dayanarak modelin tahmin gücünü test etmek amacıyla yapılmıştır. Çalışmada Borsa İstanbul (BİST)’e kote olan 10 adet bankanın 2005-2021 yılları arasındaki finansal verileri kullanılmıştır. Çalışmanın birinci bölümünde yapay sinir ağı modelinin tanımı ve genel gösterimi verilmiştir. İkinci bölümde ekonomi literatüründe daha önce çeşitli yapay zekâ yöntemleri ile yapılmış çalışmalara yer verilmiştir. Çalışmanın üçüncü bölümünde ise yapay sinir ağı modeli ile elde edilen analiz sonuçları özetlenmiştir. Verilerin analizi noktasında toplamda 17 yıllık veriden oluşan veri setinden Covid-19 dönemine ait olan 2020-2021 dönemine ait 2 yıllık veri test verisi olarak kullanılmıştır. Geriye kalan 15 yıllık verinin içerisinden 13 yıllık kısmı modelin eğitimi için geriye kalan rastgele seçilmiş 2 yıllık kısmı ise modelin Covid-19 dönemi dışındaki başarısını ölçmek üzere test verisi olarak kullanılmıştır.

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Yapay Zeka Yöntemleri ile İşletmelerin Finansal Başarısızlığının Tahmin Edilmesi: Bist İmalat Sektörü Uygulaması

Cited by 9 publications

References 5 publications

Corporate Bankruptcy Prediction Using Machine Learning Methods: The Case of the Usa

Corporate Bankruptcy Prediction Using Machine Learning Methods: The Case of the Usa

Comparison of Classification Performance of Machine Learning Methods in Prediction Financial Failure: Evidence From Borsa İstanbul

Yapay Sinir Ağı Modeli ile BİST'e Kote 10 Bankanın Covid-19 Dönemindeki Finansal Verilerinin Tahminlenme Başarısı Üzerine Bir Araştırma

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