SavedAdd your journal

Journals / Ekonomik ve Sosyal Araştırmalar Dergisi / 2026 / Cilt: 22 Sayı: 1

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

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

Bülent Ordu(ABANT İZZET BAYSAL ÜNİVERSİTESİ)0000-0002-5647-7559
Rahmi Yücel(BOLU ABANT İZZET BAYSAL ÜNİVERSİTESİ)0000-0001-8601-921X
Fatih Eroğlu(BOLU ABANT İZZET BAYSAL ÜNİVERSİTESİ)0000-0002-0285-8030
Pages
242–258
Publication Date
2026-06-29
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Overall, the study demonstrates that combining PCA and clustering analysis provides a practical framework for assessing firm-level financial resilience and making sectoral comparisons.

📌 3 Key Takeaways

🎯
Objective & Hypothesis: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.
📊
Key Findings & Data: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.
💡
Conclusion & Impact:Overall, the study demonstrates that combining PCA and clustering analysis provides a practical framework for assessing firm-level financial resilience and making sectoral comparisons.
🔬Design:Araştırma Makalesi
📍Scope:2010–2024 Verileri
⚙️Variables:Finansal dayanıklılık · otomotiv sektörü · PCA

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.

Özet

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.

🔖

Cite This Article

Ordu, B., Yücel, R., Eroğlu, F. (2026). Measuring Financial Resilience in the Automotive and Automotive Supplier Sectors: A PCA and Cluster Analysis Approach. Ekonomik ve Sosyal Araştırmalar Dergisi, Cilt: 22 Sayı: 1, 242–258. https://ajindex.com/articles/1217916

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (58)

  1. Akgüç, Ö. (2013). Mali tablolar analizi (15. baskı). Avcıol Yayınları.
  2. Al-galal, R. H. A. N., & Kahraman, Ü. M. (2024). Classification of Financial Ratios for BIST Manufacturing Sector Companies By Principal Axis Factor Analysis. DergiPark (Istanbul University). https://dergipark.org.tr/en/pub/neusbf/issue/88295/1574060
  3. Allee, K. D., Do, C., & Raymundo, F. G. (2022). Principal Component Analysis and Factor Analysis in Accounting Research. Journal of Financial Reporting, 7(2), 1. https://doi.org/10.2308/jfr-2021-005
  4. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
  5. Arslan, Ö., Polatgil, M., & Arslan, E. (2022). Examining the financial ratios and profitability of companies by using ANFIS: A study on leather and clothing industries traded in BIST. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, (61), 369-384. https://doi.org/10.18070/erciyesiibd.1013035
  6. Arthur, D., & Vassilvitskii, S. (2007). k-means++: The advantages of careful seeding. Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA 2007), 1027-1035.
  7. Basut, U., & Gümüş, U. T. (2025). BİST’te işlem gören otomotiv sektöründeki firmaların 2018–2023 yılları arasındaki DuPont analizi. R&S- Research Studies Anatolia Journal, 8(3), 455-467. https://doi.org/10.33723/rs.1714032
  8. Bates, T. W., Kahle, K. M., & Stulz, R. M. (2009). Why do U.S. firms hold so much more cash than they used to? The Journal of Finance, 64(5), 1985-2021. https://doi.org/10.1111/j.1540-6261.2009.01492.x
  9. Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111. https://doi.org/10.2307/2490171
  10. Briguglio, L., Cordina, G., Farrugia, N., & Vella, S. (2009). Economic vulnerability and resilience: Concepts and measurements. Oxford Development Studies, 37(3), 229–247. https://doi.org/10.1080/13600810903089893
  11. Carreras Simó, M., & Coenders, G. (2020). Principal component analysis of financial statements: A compositional approach. Revista de Métodos Cuantitativos para la Economía y la Empresa, 29, 18-37. https://doi.org/10.46661/revmetodoscuanteconempresa.3580
  12. Cattell, R. B. (1966). The scree test for the number of factors. Multivariate Behavioral Research, 1(2), 245-276. https://doi.org/10.1207/s15327906mbr0102_10
  13. Cenger, H. (2025). Financial performance analysis of Borsa Istanbul-listed automotive firms: A DEA and benchmarking approach. MANAS Sosyal Araştırmalar Dergisi, 14(3), 1309-1323. https://doi.org/10.33206/mjss.1638816
  14. Christopher, M., & Peck, H. (2004). Building the resilient supply chain. The International Journal of Logistics Management, 15(2), 1–13. https://doi.org/10.1108/09574090410700275
  15. Daadmehr, E. (2024). Workplace sustainability or financial resilience? Composite-financial resilience index. Risk management, 26(2), 7. https://doi.org/10.1057/s41283-023-00139-9
  16. Deloof, M. (2003). Does working capital management affect profitability of Belgian firms? Journal of Business Finance & Accounting, 30(3–4), 573–588. https://doi.org/10.1111/1468-5957.00008
  17. Ding, C., & He, X. (2004). K-means clustering via principal component analysis. Proceedings of the Twenty-First International Conference on Machine Learning (ICML 2004), 29.
  18. Ding, W., Levine, R., Lin, C., & Xie, W. (2021). Corporate immunity to the COVID-19 pandemic. Journal of Financial Economics, 141(2), 802–830. https://doi.org/10.1016/j.jfineco.2021.03.005
  19. Dolgui, A., & Ivanov, D. (2021). Ripple effect and supply chain disruption management: New trends and research directions. International Journal of Production Research, 59(1), 102–109. https://doi.org/10.1080/00207543.2021.1840148
  20. Duval, R., Elmeskov, J., & Vogel, L. (2007). Structural policies and economic resilience to shocks (OECD Economics Department Working Papers, No. 567). OECD Publishing. https://doi.org/10.1787/140152385131
  21. Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD 1996), 226–231.
  22. Fahlenbrach, R., Rageth, K., & Stulz, R. M. (2021). How valuable is financial flexibility when revenue stops? Evidence from the COVID-19 crisis. Review of Financial Studies, 34(11), 5474–5521. https://doi.org/10.1093/rfs/hhaa134
  23. Forkman, J., Josse, J., & Piepho, H.-P. (2019). Hypothesis tests for principal component analysis when variables are standardized. Journal of Agricultural, Biological and Environmental Statistics, 24, 289-308. https://doi.org/10.1007/s13253-019-00355-5
  24. Gamesalingam, S., & Kumar, K. (2001). Detection of financial distress via multivariate statistical analysis. Managerial Finance, 27(4), 45. https://doi.org/10.1108/03074350110767132
  25. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). Pearson.
  26. Hallegatte, S. (2014). Economic Resilience: Definition and Measurement. In World Bank, Washington, DC eBooks. https://doi.org/10.1596/1813-9450-6852
  27. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer.
  28. Herman, E., Zsido, K.-E., & Fenyves, V. (2022). Cluster analysis with K-Mean versus K-Medoid in financial performance evaluation. Applied Sciences, 12(16), 7985. https://doi.org/10.3390/app12167985
  29. Hidayat, R., Zamri, S. N. A. S., & Zulnaidi, H. (2018). Exploratory and confirmatory factor analysis of achievement goals for Indonesian students in mathematics education programmes. Eurasia Journal of Mathematics, Science and Technology Education, 14(12), em1648. https://doi.org/10.29333/ejmste/99173
  30. Homayoun, S., Pazhohi, M., & Manzarzadeh Tamam, H. (2024). The effect of innovation and information technology on financial resilience. Sustainability, 16(11), 4493. https://doi.org/10.3390/su16114493
  31. Ivanov, D., & Dolgui, A. (2021). OR-methods for coping with the ripple effect in supply chains during COVID-19 pandemic: Managerial insights and research implications. International Journal of Production Economics, 232, 107921. https://doi.org/10.1016/j.ijpe.2020.107921
  32. Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651–666. https://doi.org/10.1016/j.patrec.2009.09.011
  33. Janićijević, S., Mizdraković, V., & Kljajić, M. (2022). Principal Component Analysis in Financial Data Science. In IntechOpen eBooks. IntechOpen. https://doi.org/10.5772/intechopen.102928
  34. Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: a review and recent developments [Review of Principal component analysis: a review and recent developments]. Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences, 374(2065), 20150202. Royal Society. https://doi.org/10.1098/rsta.2015.0202
  35. Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141-151.
  36. Kaiser, H. F., & Rice, J. (1974). Little Jiffy, Mark IV. Educational and Psychological Measurement, 34(1), 111-117. https://doi.org/10.1177/001316447403400115
  37. Kaplan, G. (2023). Evaluation of performances of automotive sector companies traded in BIST, including the COVID-19 pandemic period, with TOPSIS, ELECTRE and CRITIC methods. Aksaray University Journal of Science and Engineering, 7(2), 85–98. https://doi.org/10.29002/asujse.1225001
  38. Kaya, O. (2024). The impact of late payments on SMEs’ access to finance: Evidence from credit rationing and loan terms. Economic Modelling, 141, 106896. https://doi.org/10.1016/j.econmod.2024.106896
  39. Kayalı, N., Soysal, M., & Aktaş, İ. (2023). Borsa İstanbul otomotiv endeksindeki şirketlerin COVID-19 dönemindeki finansal performanslarının TOPSIS yöntemine göre değerlendirilmesi. İşletme Araştırmaları Dergisi, 15(2), 1293–1304. https://doi.org/10.20491/isarder.2023.1650
  40. Keleş, E. (2023). Sosyal Sermaye ve Finansal Dayanıklılık. In Özgür Yayınları eBooks. https://doi.org/10.58830/ozgur.pub67.c321
  41. Kendirli, S., & Yıldırım, M. V. (2022). BİST’de kayıtlı otomotiv imalat sektörü şirketlerinin finansal performanslarının TOPSIS yöntemi ile incelenmesi. Global Journal of Economics and Business Studies, 11(21), 87–97.
  42. Kocadüz, B. (2023). Otomotiv sektöründe faaliyet gösteren işletmelerin entropi temelli ağırlıklandırma yöntemi üzerinden finansal performanslarının TOPSİS, ARAS ve COPRAS ile analizi (Borsa İstanbul’da uygulama) [Yüksek lisans tezi, Marmara Üniversitesi, Sosyal Bilimler Enstitüsü]. (Tez No: 776832).
  43. Li, B., Tao, R., & Li, M. (2022). Identification of Enterprise Financial Risk Based on Clustering Algorithm. Computational Intelligence and Neuroscience, 2022, 1. https://doi.org/10.1155/2022/1086945
  44. Lloret, S., Ferreres, A., Hernández, A., & Tomás, I. (2017). The exploratory factor analysis of items: Guided analysis based on empirical data and software. Anales de Psicología, 33(2), 417-432. https://doi.org/10.6018/analesps.33.2.270211
  45. Mulyaningsih, S., & Heikal, J. (2022). K-Means Clustering Using Principal Component Analysis (PCA) Indonesia Multi-Finance Industry Performance Before and During Covid-19. Asia Pacific Management and Business Application, 11(2), 131. https://doi.org/10.21776/ub.apmba.2022.011.02.1
  46. Nkundabanyanga, S. K., Mugumya, E., Nalukenge, I., Muhwezi, M., & Najjemba, G. M. (2019). Firm characteristics, innovation, financial resilience and survival of financial institutions. Journal of Accounting in Emerging Economies, 10(1), 48. https://doi.org/10.1108/jaee-08-2018-0094
  47. OECD. (2020). The impact of COVID-19 on SME financing: A special edition of the OECD Financing SMEs and Entrepreneurs Scoreboard (OECD SME and Entrepreneurship Papers, No. 22). OECD Publishing. https://doi.org/10.1787/ecd81a65-en
  48. Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131. https://doi.org/10.2307/2490395
  49. Pettit, T. J., Fiksel, J., & Croxton, K. L. (2010). Ensuring supply chain resilience: Development of a conceptual framework. Journal of Business Logistics, 31(1), 1–21. https://doi.org/10.1002/j.2158-1592.2010.tb00125.x
  50. Potkány, M., Hašková, S., Lesníková, P., & Schmidtová, J. (2022). PERCEPTION OF ESSENCE OF CONTROLLING AND ITS USE IN MANUFACTURING ENTERPRISES IN TIME OF CRISIS: DOES CONTROLLING FULFILL ITS ESSENCE? Journal of Business Economics and Management, 23(4), 957. https://doi.org/10.3846/jbem.2022.17391
  51. Pranata, K. S., Gunawan, A. A. S., & Gaol, F. L. (2023). Development clustering system IDX company with k-means algorithm and DBSCAN based on fundamental indicator and ESG. Procedia Computer Science, 216, 319. https://doi.org/10.1016/j.procs.2022.12.142
  52. Puri, N., Singh, H., Chakrabarti, A., & Tandon, D. (2022). Application of principal component analysis to facilitate financial statement analysis: An automobile industry case. Academy of Accounting and Financial Studies Journal, 26(1), 1-19.
  53. Song, F., Huang, Y., & Liu, C. (2025). Extended Application of Double Machine Learning in Corporate Financial Resilience Research: Based on Data Factor Marketization. Systems, 13(4), 292. https://doi.org/10.3390/systems13040292
  54. Sreenivasan, A., & Suresh, M. (2023). Readiness of financial resilience: A study on start-ups through total interpretive structural modelling. Journal of Safety Science and Resilience, 4(3), 241–252. https://doi.org/10.1016/j.jnlssr.2023.02.004
  55. Tang, C. S. (2006). Robust strategies for mitigating supply chain disruptions. International Journal of Logistics: Research and Applications, 9(1), 33–45. https://doi.org/10.1080/13675560500405584
  56. Tufan, C., Çiğdem, Ş., Kılıç, Y., & Sayar, G. (2024). Agility and Resilience in Supply Chains: Investigating Their Roles in Enhancing Financial Performance. Sustainability, 16(17), 7842. https://doi.org/10.3390/su16177842
  57. Turunç, A., & Ersoy, H. (2023). Determination of financial performance of automotive manufacturing companies trade on BIST 100 via entropy based TOPSIS method. Finans Ekonomi ve Sosyal Araştırmalar Dergisi, 8(2), 287–297. https://doi.org/10.29106/fesa.1233893
  58. Yanık, L., & Eren, T. (2017). Borsa İstanbul’da işlem gören otomotiv imalat sektörü firmalarının finansal performanslarının AHP, TOPSIS, ELECTRE ve VIKOR yöntemleri ile analizi. Yalova Sosyal Bilimler Dergisi, 7(13), 165–188. https://doi.org/10.17828/yalovasosbil.333899