Otomotiv ve Yan Sanayi Sektörlerinde Finansal Dayanıklılığın Ölçülmesi: PCA ve Kümeleme Analizi Yaklaşımı

Abstract

Bu çalışmanın amacı, Borsa İstanbul’da (BİST) işlem gören otomotiv ana sanayi ve otomotiv yan sanayi firmalarının finansal dayanıklılığını Temel Bileşenler Analizi ve K-Means kümeleme analizi kullanarak incelemektir. Çalışmada, 2010-2024 dönemine ait finansal verilerden hareketle 19 firma için cari oran, nakit oran, aktif kârlılık, net kâr marjı ve borç/kaynak oranı esas alınmıştır. Söz konusu oranlar standartlaştırıldıktan sonra finansal dayanıklılığı temsil eden ortak yapının belirlenmesi amacıyla Temel Bileşenler Analizi uygulanmıştır. Kaiser-Meyer-Olkin değerinin 0,751 ve Bartlett küresellik testinin anlamlı bulunması, veri setinin analiz için uygun olduğunu göstermiştir. Analiz sonucunda özdeğeri 1’in üzerinde tek bir bileşen elde edilmiş ve bu bileşen toplam varyansın %77,281’ini açıklamıştır. Elde edilen bileşen skorları kullanılarak yapılan K-Means kümeleme analizi firmaları iki gruba ayırmıştır: yüksek finansal dayanıklılık gösteren firma grubu ve düşük finansal dayanıklılık gösteren firma grubu. Bulgular, yüksek dayanıklılık grubunda yer alan firmaların daha güçlü likidite ve kârlılık göstergelerine, daha düşük borç baskısına sahip olduğunu ortaya koymaktadır. Çalışma, finansal dayanıklılığın firma düzeyinde ölçülmesinde Temel Bileşenler Analizi ile kümeleme analizinin birlikte kullanılmasının sektörel karşılaştırmalar açısından işlevsel bir çerçeve sunduğunu göstermektedir.

Keywords

• Finansal dayanıklılık
• otomotiv sektörü
• PCA
• K-Means
• çok değişkenli analiz.

Supporting Institution

Çalışmada herhangi bir destek alınmamıştır.

Ethical Statement

Bu makalede hiçbir insan çalışması sunulmamış olup, makale araştırma ve yayın etiğine uygun hazırlanmıştır.

Measuring Financial Resilience in the Automotive and Automotive Supplier Sectors: A PCA and Cluster Analysis Approach

Abstract

This study examines the financial resilience of automotive and automotive supplier firms listed on Borsa Istanbul (BIST) using Principal Component Analysis (PCA) and K-Means clustering. Based on financial data for the 2010–2024 period, five indicators were used for 19 firms: current ratio, cash ratio, return on assets, net profit margin, and debt-to-equity ratio. After standardization, PCA was applied to identify the common structure underlying financial resilience. The Kaiser-Meyer-Olkin value of 0.751 and the significant Bartlett’s sphericity test indicated that the dataset was suitable for analysis. The results yielded a single component with an eigenvalue greater than 1, explaining 77.281% of the total variance. The component scores obtained from PCA were then used in K-Means clustering, which classified firms into two groups: a high financial resilience cluster and a low financial resilience cluster. The findings show that firms in the high-resilience cluster exhibit stronger liquidity and profitability indicators and lower debt pressure. Overall, the study demonstrates that combining PCA and clustering analysis provides a practical framework for assessing firm-level financial resilience and making sectoral comparisons.

Keywords

• Financial resilience
• automotive industry
• PCA
• K-Means clustering
• multivariate analysis

Supporting Institution

No support was received in the study.

Ethical Statement

No human work has been presented in this article, suitable for article research and publication ethics prepared.

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