BIST Sanayi Endeksi'nde Finansal Başarısızlık Tahmini Geleneksel Modellerin ve Kümelenme Tekniklerinin Değerlendirilmesi

Year 2023, , 660 - 680, 30.12.2023

Ömer Serkan Gülal , Gökhan Seçme , Eda Köse

https://doi.org/10.30784/epfad.1370893

Abstract

Firmalar, kredi verenler, yatırımcılar ve bir bütün olarak ekonomi için bir firmanın iflas veya tasfiyesi ile sonuçlanabilecek finansal sıkıntı kavramı çok önemli bir konudur. Son dönemde yaşanan finansal krizler ve küresel ekonomik dalgalanmalar bu konunun önemini artırmıştır. Önceki çalışmalar göz önünde bulundurulduğunda, finansal sıkıntıyı öngörmek amacıyla Altman Z-skoru gibi yöntemlerin geliştirildiği görülmektedir. Fakat son dönemlerde makine öğrenmesi gibi yeni tekniklerin de bu amaçla kullandığı görülmektedir. Bu çalışmanın amacı, k-ortalamalar kümeleme algoritması ile Altman Z-skoru ve Springate S-skoru modellerinden faydalanarak, BIST Sanayi Endeksi (XUSIN) firmalarında finansal sıkıntıyı tahmin etmektir. Araştırmanın bulgularına göre iki firma 2011, 2012, 2015 ve 2017 yıllarında Altman z-skoru, Zꞌ-skoru, S-skoru ve mali durum kriterlerinin üçünü de karşılamaktayken, 2016 ve 2018 yıllarında 2 firma, 2013 ve 2014 yıllarında 5 firma, 2019 yılında 4 firma, 2020 yılındaysa 1 firma bu kriterleri karşılamaktadır. 2021 yılına bakıldığında hiçbir şirketin aynı gruplarda gruplanmadığı görülmektedir. Bu durum kullanılan yöntemlerin farklı sonuçlara ulaştığı anlamına gelmektedir. Özellikle k-means kümeleme algoritmasının, daha yüksek ayırıcı özelliği sayesinde ilgili taraflar için, diğer yöntemlere göre daha doğru kümeleme sonuçları verdiği tespit edilmiştir.

Keywords

Finansal Başarısızlık, Finansal Sıkıntı, Altman Z-skor, S-skor Yöntemi, K-Ortalamalar Kümeleme

Predicting Financial Distress in the BIST Industrials Index: Evaluating Traditional Models and Clustering Techniques

Abstract

Financial distress, which can lead to bankruptcy or liquidation, is important for companies, creditors, investors, and the economy. Recent financial crises and global economic fluctuations have brought this issue to the forefront. In an effort to foresee financial distress, methods like Altman's Z-score have been proposed while, recent developments have allowed for the incorporation of recent techniques like machine learning. The purpose of this study is to forecast the emergence of financial distress in BIST Industrials Index (XUSIN) companies by using the k-means clustering algorithm, Altman Z-score and Springate S-score models with firm level financial indicators where we investigated successful and unsuccessful companies. Our findings show that two companies met all three Altman Z-score, Zꞌ-score, S-score and financial situation criteria in 2011, 2012, 2015, and 2017; 2 companies in 2016 and 2018; 5 companies in 2013 and 2014; 4 companies in 2019; 1 company in 2020 where no companies are grouped in the same groups in 2021, which means the methods reach different results. It has been determined that the k-means clustering algorithm, particularly due to its higher separability, provides more accurate clustering results for the concerned parties compared to other methods.

Keywords

Financial Distress, Altman Z-score, S-score Method, K-Means Clustering

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