POST-PANDEMIC FINANCIAL EVALUATION OF BORSA ISTANBUL HEALTHCARE COMPANIES THROUGH CLUSTER ANALYSIS

Yıl 2025, Cilt: 34 Sayı: 2, 1234 - 1249, 24.10.2025

Merve Tekinarslan , Ahmet Bahadır Şimşek

https://doi.org/10.35379/cusosbil.1643022

Öz

The Covid-19 pandemic has led to significant changes in the healthcare sector and deeply affected economic balances. This study aims to analyze the financial performance of healthcare companies in Turkey after the pandemic and identify companies with similar financial characteristics.

The study examines the financial indicators, such as liquidity, profitability, debt, and asset management, of 12 healthcare companies over the period from the last quarter of 2019 to the second quarter of 2024. The companies are classified based on financial differences using the K-means clustering algorithm. The analysis revealed that during the pandemic, healthcare companies exhibited a heterogeneous financial structure and sector-specific variations. In the post-pandemic period, however, a trend toward homogenization of financial performance and increased stability across companies was observed.

The study provides an in-depth understanding of the financial resilience and performance differences within the sector after the pandemic. Finally, it provides insights that will help develop more resilient financial strategies in times of crisis. In this context, the study aims to create a strategic roadmap to support the growth of the healthcare industry in Turkey.

Anahtar Kelimeler

Covid-19 , healthcare sector , financial performance , cluster analysis , Borsa Istanbul.

PANDEMİ SONRASI BORSA İSTANBUL SAĞLIK ŞİRKETLERİNİN KÜMELEME ANALİZİ İLE FİNANSAL DEĞERLENDİRİLMESİ

Öz

Covid-19 pandemisi, sağlık sektöründe büyük değişimlere yol açmış ve ekonomik dengeleri derinden etkilemiştir. Bu çalışma ile pandemi sonrası Türkiye sağlık sektöründeki şirketlerin finansal performanslarının analiz edilmesi ve benzer özelliklere sahip şirketlerin belirlemesi amaçlanmıştır.
Çalışmada, 2019 yılının son çeyreğinden 2024 yılının ikinci çeyreğine kadar olan dönemi kapsayan, 12 sağlık şirketinin likidite, kârlılık, borçlanma ve varlık yönetimi gibi finansal göstergeleri incelenmiş ve K-ortalamalar algoritması ile şirketler arasında finansal farklılıklar sınıflandırılmıştır. Yapılan analizler sonucunda; pandemi döneminde sağlık sektörü şirketlerinin finansal göstergelerinde heterojen bir yapı sergilediği ve alt sektörler bazında farklılaşma gösterdiği ortaya çıkmıştır. Pandemi sonrası dönemde ise sektör genelinde finansal performansın homojenleştiği ve şirketler arasında istikrarın arttığı gözlemlenmiştir.
Çalışma, pandemi sonrası sektördeki finansal dayanıklılık ve performans farklılıklarını detaylı bir şekilde analiz ederek sektöre dair derinlemesine bir anlayış sunmaktadır. Son olarak, kriz dönemlerine karşı daha dirençli finansal stratejiler geliştirilmesine yardımcı olacak öngörüler sunmaktadır. Bu bağlamda, çalışma sağlık sektörünün büyümesini desteklemek amacıyla stratejik bir yol haritası oluşturmaktadır.

Anahtar Kelimeler

Covid-19 , sağlık sektörü , finansal performans , kümeleme analizi , Borsa İstanbul.

Etik Beyan

Bu çalışma, kamuya açık ve açık erişimli veri setlerinden elde edilen veriler kullanılarak gerçekleştirilmiştir. Kullanılan tüm veriler, herhangi bir özel bilgi içermeyen, anonim ve toplu verilerdir. Bu nedenle, çalışmamız etik kurul onayı gerektirmemektedir. Araştırma sürecinde, verilerin doğru, şeffaf ve etik standartlara uygun bir şekilde analiz edilmesine özen gösterilmiştir.

Teşekkür

Teşekkür eder iyi çalışmalar dileriz

Kaynakça

• Al-Amin, M., Li, K., Hefner, J., & Islam, M. N. (2023). Were hospitals with sustained high performance more successful at reducing mortality during the pandemic’s second wave? Health Care Management Review, 48(1), 70–79. https://doi.org/10.1097/HMR.0000000000000354
• Celebi, M. E., Kingravi, H. A., & Vela, P. A. (2013). A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Systems with Applications, 40(1), 200–210. https://doi.org/10.1016/j.eswa.2012.07.021
• Chi, D. (2021). Research on the application of K-means clustering algorithm in student achievement. In 2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) (pp. 435–438). IEEE. https://doi.org/10.1109/ICCECE51280.2021.9342164
• Fa’rifah, R. Y., & Pramesti, D. (2022). Cluster analysis of inclusive economic development using K-means algorithm. Jurnal Varian, 5(2), 171–178. https://doi.org/10.30812/varian.v5i2.1894
• Fahim, A. M., Salem, A.-B. M., Torkey, F. A., & Ramadan, M. A. (2006). An efficient enhanced k-means clustering algorithm. Journal of Zhejiang University-SCIENCE A, 7(10), 1626–1633. https://doi.org/10.1631/jzus.2006.A1626
• Fiscarina, K. D., & Paranita, E. S. (2023). Financial performance of health service providers sub-industry companies before and during the Covid-19 pandemic. Journal of Applied Management Research, 3(1), 51–61. https://doi.org/10.36441/jamr.v3i1.1648
• Foong, S. Z. Y., Andiappan, V., Aviso, K. B., Chemmangattuvalappil, N. G., Tan, R. R., Yu, K. D. S., & Ng, D. K. S. (2022). A criticality index for prioritizing economic sectors for post-crisis recovery in oleo-chemical industry. Journal of the Taiwan Institute of Chemical Engineers, 130, 103957. https://doi.org/10.1016/j.jtice.2021.06.051
• Gierusz, M., Hońko, S., Strojek-Filus, M., & Świetla, K. (2022). The quality of goodwill disclosures and impairment in the financial statements of energy, mining, and fuel sector groups during the pandemic period—Evidence from Poland. Energies, 15(16), 5763. https://doi.org/10.3390/en15165763
• Golubeva, O. (2021). Firms’ performance during the COVID-19 outbreak: International evidence from 13 countries. Corporate Governance, 21(6), 1011–1027. https://doi.org/10.1108/CG-09-2020-0405
• Halkidi, M., Batistakis, Y., & Vazirgiannis, M. (2001). On clustering validation techniques. Journal of Intelligent Information Systems, 17(2–3), 107–145. https://doi.org/10.1023/A:1012801612483
• Ihm, L., Zhang, H., van Vijfeijken, A., & Waugh, M. G. (2021). Impacts of the Covid‐19 pandemic on the health of university students. The International Journal of Health Planning and Management, 36(3), 618–627. https://doi.org/10.1002/hpm.3145
• Jalilian, H., Mohammad Riahi, S., Heydari, S., & Taji, M. (2023). Performance analysis of hospitals before and during the COVID-19 in Iran: A cross-sectional study. PLOS ONE, 18(6), e0286943. https://doi.org/10.1371/journal.pone.0286943
• Kamble, A. M., & Bharte, A. D. (2023). Covid 19 effect on medical technology. International Journal of Advanced Research in Science, Communication and Technology, 524–533. https://doi.org/10.48175/IJARSCT-11679
• Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2002). An efficient k-means clustering algorithm: Analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 881–892. https://doi.org/10.1109/TPAMI.2002.1017616
• Karaboga, D., & Ozturk, C. (2011). A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Applied Soft Computing, 11(1), 652–657. https://doi.org/10.1016/j.asoc.2009.12.025
• Kaufman, L., & Rousseeuw, P. J. (2009). Finding groups in data: An introduction to cluster analysis. John Wiley & Sons. https://doi.org/10.1002/9780470316801
• Kgatla, M. N., Mothiba, T. M., Sodi, T., & Makgahlela, M. (2021). Nurses’ experiences in managing cardiovascular disease in selected rural and peri-urban clinics in Limpopo Province, South Africa. International Journal of Environmental Research and Public Health, 18(5), 2570. https://doi.org/10.3390/ijerph18052570
• Lavalle, C., Magnocavallo, M., Straito, M., Santini, L., Forleo, G. B., Grimaldi, M., ... Ricci, R. P. (2021). Flecainide how and when: A practical guide in supraventricular arrhythmias. Journal of Clinical Medicine, 10(7), 1456. https://doi.org/10.3390/jcm10071456
• Likas, A., Vlassis, N., & Verbeek, J. (2003). The global k-means clustering algorithm. Pattern Recognition, 36(2), 451–461. https://doi.org/10.1016/S0031-3203(02)00060-2
• Lu, J., & Khan, S. (2023). Are sustainable firms more profitable during COVID-19? Recent global evidence of firms in developed and emerging economies. Asian Review of Accounting, 31(1), 57–85. https://doi.org/10.1108/ARA-04-2022-0102
• Muttaqin, M. F. J. (2022). Cluster analysis using K-means method to classify Sumatera regency and city based on human development index indicator. Seminar Nasional Official Statistics, 2022(1), 967–976. https://doi.org/10.34123/semnasoffstat.v2022i1.1299
• Muttaqin, M. F. J., & Zulkarnain. (2020). Cluster analysis using K-means method to classify Indonesia regency/city based on human development index indicator. In Proceedings of the 3rd Asia Pacific Conference on Research in Industrial and Systems Engineering 2020 (pp. 81–85). ACM. https://doi.org/10.1145/3400934.3400951
• Piech, K. (2022). Health care financing and economic performance during the Coronavirus pandemic, the war in Ukraine and the energy transition attempt. Sustainability, 14(17), 10601. https://doi.org/10.3390/su141710601
• Pokharel, M., Bhatta, J., & Paudel, N. (2021). Comparative analysis of K-means and enhanced K-means algorithms for clustering. NUTA Journal, 8(1–2), 79–87. https://doi.org/10.3126/nutaj.v8i1-2.44044
• Ratten, V., da Silva Braga, V. L., & da Encarnação Marques, C. S. (2021). Sport entrepreneurship and value co-creation in times of crisis: The COVID-19 pandemic. Journal of Business Research, 133, 265–274. https://doi.org/10.1016/j.jbusres.2021.05.001
• Rothwell, J. T., Cojocaru, A., Srinivasan, R., & Kim, Y. S. (2024). Global evidence on the economic effects of disease suppression during COVID-19. Humanities and Social Sciences Communications, 11(1), Article 78. https://doi.org/10.1057/s41599-023-02571-4
• Rousseeuw, P. J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53–65. https://doi.org/10.1016/0377-0427(87)90125-7
• Shahapure, K. R., & Nicholas, C. (2020). Cluster quality analysis using silhouette score. In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 747–748). IEEE. https://doi.org/10.1109/DSAA49011.2020.00096
• Shahin, M. A. H., & Alabed, H. H. (2023). Healthcare management challenges and opportunities during COVID pandemic. Current Research in Public Health, 3(1), 53–59. https://doi.org/10.31586/crph.2023.666
• Shahzad, S. J. H., Bouri, E., Kristoufek, L., & Saeed, T. (2021). Impact of the COVID-19 outbreak on the US equity sectors: Evidence from quantile return spillovers. Financial Innovation, 7(1), 14. https://doi.org/10.1186/s40854-021-00228-2
• Ueda, M., Hayashi, K., & Nishiura, H. (2023). Identifying high-risk events for COVID-19 transmission: Estimating the risk of clustering using nationwide data. Viruses, 15(2), 456. https://doi.org/10.3390/v15020456
• Wahyuni, S. N., Khanom, N. N., & Astuti, Y. (2023). K-means algorithm analysis for election cluster prediction. JOIV: International Journal on Informatics Visualization, 7(1), 1. https://doi.org/10.30630/joiv.7.1.1107
• Waitzberg, R., Quentin, W., Webb, E., & Glied, S. (2021). The structure and financing of health care systems affected how providers coped with COVID‐19. The Milbank Quarterly, 99(2), 542–564. https://doi.org/10.1111/1468-0009.12530
• Wang, C. (2023). Development of student biochemical index monitoring system based on K-means cluster analysis. In 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1–5). IEEE. https://doi.org/10.1109/ICICACS57338.2023.10100254
• Younis, H., Alsharairi, M., Younes, H., & Sundarakani, B. (2023). The impact of COVID-19 on supply chains: Systematic review and future research directions. Operational Research, 23(3), 48. https://doi.org/10.1007/s12351-023-00790-w
• Zamani, F. E., Kusnandar, T., Silmi, F. E., & Rachman, R. (2023). Analysis of public service satisfaction using artificial intelligence K-means cluster. Majalah Bisnis & IPTEK, 16(1), 181–187. https://doi.org/10.55208/bistek.v16i1.428