SavedAdd your journal

Journals / Ekonomi Politika ve Finans Araştırmaları Dergisi / 2026 / Cilt: 11 Sayı: 3

Tensor TOPSIS Approach to Dynamic Performance of Turkish Banks: A LOPCOW–TOPSIS-Driven Hybrid Model

Türk Bankalarının Dinamik Performansına Tensör TOPSIS Yaklaşımı: LOPCOW–TOPSIS Odaklı Hibrit Bir Model

Ali Özarslan(HACETTEPE ÜNİVERSİTESİ, REKTÖRLÜK)0000-0002-9407-1787
Pages
984–1009
Publication Date
2026-09-30
🏛️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)

Results show that Asset Quality and Balance Sheet Structure are the most differentiating dimensions, while Capital Adequacy has the lowest weight.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study evaluates the dynamic financial performance of 23 Turkish banks over the 2002–2024 period using a hybrid LOPCOW–TOPSIS–Tensor TOPSIS model.
📊
Key Findings & Data:The methodology comprises three stages: (1) 46 financial ratios under seven CAMELS‑aligned main criteria are objectively weighted using LOPCOW; (2) within‑group TOPSIS generates seven main criterion scores for each bank‑year; (3) Tensor TOPSIS extracts trend, mean, and standard deviation from the 23‑year panel to produce a final dynamic ranking.
💡
Conclusion & Impact:Results show that Asset Quality and Balance Sheet Structure are the most differentiating dimensions, while Capital Adequacy has the lowest weight.
🔬Design:Modeling & Simulation
📍Scope:2002–2024 Verileri
⚙️Variables:Dinamik Performans Değerlendirmesi · Bankacılık · Tensör TOPSIS

Abstract

This study evaluates the dynamic financial performance of 23 Turkish banks over the 2002–2024 period using a hybrid LOPCOW–TOPSIS–Tensor TOPSIS model. The methodology comprises three stages: (1) 46 financial ratios under seven CAMELS‑aligned main criteria are objectively weighted using LOPCOW; (2) within‑group TOPSIS generates seven main criterion scores for each bank‑year; (3) Tensor TOPSIS extracts trend, mean, and standard deviation from the 23‑year panel to produce a final dynamic ranking. Robustness is tested via Borda count. Results show that Asset Quality and Balance Sheet Structure are the most differentiating dimensions, while Capital Adequacy has the lowest weight. Foreign‑owned niche banks (Deutsche Bank, Citibank) rank first and second, followed by private (Akbank, QNB) and state‑owned banks (T.C. Ziraat Bank). Public banks improve their relative performance during crisis periods, consistent with international evidence on the counter‑cyclical role of state‑owned banks. The high Spearman correlation between Tensor TOPSIS and Borda rankings confirms model robustness. Regulators can use the proposed dynamic framework to monitor structural risks and design early‑warning systems that capture both performance levels and temporal stability.

Özet

Bu çalışma, 23 Türk bankasının 2002–2024 dönemindeki dinamik finansal performansını hibrit bir LOPCOW–TOPSIS–Tensör TOPSIS modeliyle değerlendirmektedir. Yöntem; CAMELS uyumlu yedi ana kriter altındaki 46 finansal rasyonun LOPCOW ile ağırlıklandırılması, grup içi TOPSIS ile her banka‑yıl için yedi ana kriter skorunun üretilmesi ve Tensör TOPSIS ile 23 yıllık panelden trend, ortalama ve standart sapma özniteliklerinin çıkarılarak nihai dinamik sıralamanın elde edilmesi olmak üzere üç aşamadan oluşmaktadır. Modelin sağlamlığı Borda sayımı ile test edilmiştir. Bulgular, aktif kalitesi ve bilanço yapısının en belirleyici boyutlar olduğunu, sermaye yeterliliğinin ise en düşük ağırlığa sahip olduğunu göstermiştir. Yabancı sermayeli uzmanlaşmış bankalar (Deutsche Bank, Citibank), ilk iki sırada yer alırken bunları özel (Akbank, QNB) ve kamu bankaları (T.C. Ziraat Bankası) izlemiştir. Kamu bankalarının kriz dönemlerinde performansının iyileşmesi, devlet bankalarının konjonktür karşıtı rolüne ilişkin uluslararası bulgularla örtüşmektedir. Tensör TOPSIS ile Borda sıralamaları arasındaki yüksek Spearman korelasyonu modelin güvenilirliğini teyit etmektedir. Düzenleyici otoriteler, önerilen dinamik çerçeveyi yapısal riskleri izlemek, performans düzeyini ve zamansal istikrarı dikkate alan erken uyarı sistemleri tasarlamak için kullanabilmektedir.

🔖

Cite This Article

Özarslan, A. (2026). Tensor TOPSIS Approach to Dynamic Performance of Turkish Banks: A LOPCOW–TOPSIS-Driven Hybrid Model. Ekonomi Politika ve Finans Araştırmaları Dergisi, Cilt: 11 Sayı: 3, 984–1009. https://doi.org/10.30784/epfad.1980623

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (53)

  1. Akbulut, O.Y. (2023). Evaluation of financial performance of participation banks operating in Turkey with CRITIC based COPRAS method. International Journal of Current Social Science, 2(1), 15-24. https://izlik.org/JA75NN46CN
  2. Akbulut, O.Y. and Akgül, Y. (2026). CAMELS-ESG risk performance assessment in banking using a grey multi-criteria decision-making approach. Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 13(2), 800-827. https://izlik.org/JA23JG24TJ
  3. Akbulut, O.Y. and Aydın, Y. (2024). A hybrid multidimensional performance measurement model using the MSD-MPSI-RAWEC model for Turkish banks. Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 11(3), 1157-1183. https://doi.org/10.30798/makuiibf.1464469
  4. Berger, A.N. and Mester, L.J. (1997). Inside the black box: What explains differences in the efficiencies of financial institutions? Journal of Banking & Finance, 21(7), 895-947. https://doi.org/10.1016/s0378-4266(97)00010-1
  5. Bertay, A.C., Demirgüç-Kunt, A. and Huizinga, H. (2015). Bank ownership and credit over the business cycle: Is lending by state banks less procyclical? Journal of Banking & Finance, 50, 326-339. https://doi.org/10.1016/j.jbankfin.2014.03.012
  6. Bilal, M. (2025). Fractional fuzzy tensor: A new approach with application in smartphone selection and fitness tracker selection using TOPSIS method. International Journal of Fuzzy Systems, Advance online publication. https://doi.org/10.1007/s40815-025-02155-1
  7. Biswas, S., Bandyopadhyay, G. and Mukhopadhyaya, J.N. (2022). A multi-criteria framework for comparing dividend pay capabilities: Evidence from Indian FMCG and consumer durable sector. Decision Making: Applications in Management and Engineering, 5(2), 140-175. https://doi.org/10.31181/dmame0306102022b
  8. Bozdoğan, T., Ersoy, B. and Kaygusuz, M. (2018). CAMELS değerlendirme sistemiyle katılım bankalarının finansal performanslarının TOPSIS yöntemiyle analizi. International Journal of Social and Humanities Sciences Research, 5(30), 4309-4323. https://doi.org/10.26450/jshsr.898
  9. Campello, B. and Duarte, L.T. (2025). Integrating tensor-based data analytics and adaptive prediction for informed decision-making support. In A. Paes & F.A.N. Verri (Eds.), Brazilian conference on intelligent systems (pp. 335-345). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-79035-5_23
  10. Campello, B.S.C., Duarte, L.T. and Romano, J.M.T. (2023). Exploiting temporal features in multicriteria decision analysis by means of a tensorial formulation of the TOPSIS method. Computers & Industrial Engineering, 175, 108915. https://doi.org/10.1016/j.cie.2022.108915
  11. Chinh, D.T., Quynh, P.H., Hien, N.T.T., Minh, V.Q. and Quang, V.M. (2025). Comparing methodologies for ranking alternatives: A case study in assessing bank financial performance. Engineering, Technology and Applied Science Research, 15(6), 29173–29179. https://doi.org/10.48084/etasr.14024
  12. Cole, R.A. and Gunther, J.W. (1998). Predicting bank failures: A comparison of on- and off-site monitoring systems. Journal of Financial Services Research, 13(2), 103-117. https://doi.org/10.1023/a:1007954718966
  13. Demir, E. (2025). Multi-criteria sustainability performance analysis of commercial banks in Türkiye based on MPSI and RAWEC methods. International Journal of Insurance and Finance, 5(1), 45-58. https://doi.org/10.52898/ijif.2025.4
  14. Diakoulaki, D., Mavrotas, G. and Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The CRITIC method. Computers & Operations Research, 22(7), 763-770. https://doi.org/10.1016/0305-0548(94)00059-h
  15. Dincer, H., Gencer, G., Orhan, N. and Sahinbas, K. (2011). A performance evaluation of the Turkish banking sector after the global crisis via CAMELS ratios. Procedia-Social and Behavioral Sciences, 24, 1530-1545. https://doi.org/10.1016/j.sbspro.2011.09.051
  16. Ecer, F. and Pamučar, D. (2022). A novel LOPCOW-DOBI multi‐criteria sustainability performance assessment methodology: An application in developing country banking sector. Omega, 112, 102690. https://doi.org/10.1016/j.omega.2022.102690
  17. Gönüllü, O., Çakır, N. and Küçükkocaoğlu, G. (2026). Analysing the financial performance of Borsa Istanbul energy companies: Integrated LOPCOW-MAIRCA MCDM approach. Sosyoekonomi, 34(68), 33-58. https://doi.org/10.17233/sosyoekonomi.2026.02.02
  18. Hatunoğlu, Z., Satır, H. and Yaşar, F. (2019). Katılım bankalarının kurumsal sosyal sorumluluk performanslarının değerlendirilmesi: ENTROPI ve TOPSIS yöntemleri ile uygulama. Kahramanmaraş Sütçü İmam Üniversitesi Sosyal Bilimler Dergisi, 16(2), 613-639. https://doi.org/10.33437/ksusbd.526458
  19. Hien, N.T.T., Quynh, P.H. and Minh, V.Q. (2025). A comparative analysis of multi-criteria decision-making methods. Engineering, Technology and Applied Science Research, 15(5), 26369–26375. https://doi.org/10.48084/etasr.12782
  20. Hwang, C.L., Yoon K. (1981) Multiple attribute decision making: Methods and applications: A state-of-the-art survey. Berlin: Springer. https://doi.org/10.1007/978-3-642-48318-9_3
  21. Işık, Ö., Adalar, İ. and Shabir, M. (2025c). Measuring efficiency, productivity and sustainability performance for Islamic banks: A fuzzy expert-based multi-criteria decision support model using spherical fuzzy information. International Journal of Islamic and Middle Eastern Finance and Management, 18(6), 1482-1519. https://doi.org/10.1108/imefm-09-2024-0477
  22. Işık, Ö., Shabir, M., Demir, G., Puska, A. and Pamucar, D. (2025a). A hybrid framework for assessing Pakistani commercial bank performance using multi-criteria decision-making. Financial Innovation, 11(1), 38. https://doi.org/10.1186/s40854-024-00728-x
  23. Işık, Ö., Shabir, M., Oğuz, A. and Alkan, O. (2026). Multidimensional sustainability performance measurement in the banking industry: A data‐driven grey decision‐support framework. Business Strategy and the Environment, Advance online publication. https://doi.org/10.1002/bse.71305
  24. Işık, Ö., Zolfani, S.H., Shabir, M. and Šaparauskas, J. (2025b). A grey-based hybrid decision support framework for assessing the Environmental, Social, and Governance (ESG) sustainable performance: A case study of BIST-listed banks. Technological and Economic Development of Economy, 31(4), 1237-1273. https://doi.org/10.3846/tede.2025.24359
  25. Karabulut, T. and Akkaynak, B. (2023). Türkiye'deki mevduat bankalarının CRITIC-COPRAS yöntemleriyle sıralaması. EKEV Akademi Dergisi, 94, 315-329. https://doi.org/10.17753/sosekev.1234728
  26. Karahan, M. and Kızkapan, L. (2022). Çok kriterli karar verme teknikleriyle bankaların finansal performanslarının karşılaştırmalı analizi. Verimlilik Dergisi, 3, 441-462. https://doi.org/10.51551/verimlilik.953606
  27. Karki, U., Kumar, A. and Sharma, D. (2025). Banking in sustainability: An integrated MCDM framework for evaluating the environmental, social, and governance (ESG) sustainable banking performance. Global Knowledge, Memory and Communication, Advance online publication. https://doi.org/10.1108/GKMC-04-2024-0241
  28. Katı, M. (2024). Kamu sermayeli mevduat bankalarının finansal performans analizi: SV-TOPSIS uygulaması. Mali Çözüm Dergisi, 34, 159-178. Retrieved from https://openurl.ebsco.com/
  29. Katı, M. (2026). Financial performance analysis of privately-owned deposit banks: CRITIC-MARCOS method. Doğuş Üniversitesi Dergisi, 27(1), 59-79. https://doi.org/10.31671/doujournal.1597922
  30. Keršuliene, V., Zavadskas, E.K. and Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management, 11(2), 243-258. https://doi.org/10.3846/jbem.2010.12
  31. Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E.K., Turskis, Z. and Antucheviciene, J. (2021). Determination of objective weights using a new method based on the removal effects of criteria (MEREC). Symmetry, 13(4), 525. https://doi.org/10.3390/sym13040525
  32. Kırbaşlı, M. (2025). Türk bankacılık sektöründe finansal performans analizi: Kamu ve özel mevduat bankaları üzerine TOPSIS tabanlı bir inceleme (2018-2023). Business and Management Studies: An International Journal, 13(4), 2101-2119. https://doi.org/10.15295/bmij.v13i4.2674
  33. Krishankumar, R., Ecer, F., Korucuk, S., Aytekin, A. and Yadav, A. (2025). Evaluating zero-carbon measures through a probabilistic hesitant fuzzy LOPCOW and WISP approach. Applied Soft Computing, 114216. https://doi.org/10.1016/j.asoc.2025.114216
  34. Kumar, P. and Sharma, D. (2023). Benchmarking the financial performance of Indian commercial banks by a hybrid MCDM approach. International Journal of Process Management and Benchmarking, 15(3), 285-309. https://doi.org/10.1504/ijpmb.2023.134147
  35. Li, W., Yi, P. and Zhang, D. (2018). Sustainability evaluation of cities in northeastern China using dynamic TOPSIS-entropy methods. Sustainability, 10(12), 4542. https://doi.org/10.3390/su10124542
  36. Meral, İ.G. (2025). Efficiency measurement of OECD health systems using objective weighting: An integrated LOPCOW-DEA model. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 72, 1-10. https://doi.org/10.18070/erciyesiibd.1698502
  37. Muhammad Ghazali, W.N.I.B., Khairollah, K.N.B., Tumiran, A.B. and Rodzi, Z. (2022). A MCDM-based model of Malaysian commercial bank performance prior to and during COVID-19. International Journal of Academic Research in Accounting Finance and Management Sciences, 12(4), 290–301. https://doi.org/10.6007/ijarafms/v12-i4/15912
  38. Ömürbek, V., Aksoy, E. and Akçakanat, Ö. (2017). Bankaların sürdürülebilirlik performanslarının ARAS, MOOSRA ve COPRAS yöntemleri ile değerlendirilmesi. Süleyman Demirel Üniversitesi Vizyoner Dergisi, 8(19), 14-32. https://doi.org/10.21076/vizyoner.329346
  39. Paksoy, Ö.B. and Küçüker, M. (2024). Bankacılık sektöründe finansal performans analizi: BIST bankacılık endeksi üzerine bir inceleme. Muhasebe ve Finans İncelemeleri Dergisi, 7(2), 170-189. https://doi.org/10.32951/mufider.1519449
  40. Peci, A., Dervishaj, B. and Puška, A. (2025). Using fuzzy analytic hierarchy process and technique for order of preference by similarity to the ideal solution in performance evaluation in the Albanian banking sector. Journal of Risk and Financial Management, 18(3), 116. https://doi.org/10.3390/jrfm18030116
  41. Saaty, T.L. (1980). The analytic hierarchy process: Planning, priority setting, resource allocation. New York: McGraw-Hill.
  42. Shannon, C.E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379-423. Retrieved from https://dl.acm.org/
  43. Shemshadi, A., Shirazi, H., Toreihi, M. and Tarokh, M.J. (2011). A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting. Expert Systems with Applications, 38(10), 12160-12167. https://doi.org/10.1016/j.eswa.2011.03.027
  44. Şimşek, O. (2022). Hibrid Bir ÇKKV modeli ile Türk bankacılık sektöründe finansal performans değerlendirmesi. Turkish Studies-Economics, Finance, Politics, 17(2), 447. https://dx.doi.org/10.7827/TurkishStudies.62308
  45. Süzülmüş, S. and Yakut, E. (2024). CRITIC temelli PROMETHEE ve EDAS teknikleriyle bankaların finansal performanslarının belirlenerek karşılaştırılması. MANAS Sosyal Araştırmalar Dergisi, 13(1), 218-239. https://doi.org/10.33206/mjss.1267073
  46. TBB. (2025). 2024 yıllık seçilmiş rasyolar. Retrieved from https://www.tbb.org.tr/download/node/54500/field_raporlar_ekler/305602
  47. Tekgün, B. (2025). Evaluating ESG sustainability performance in the Turkish banking industry: A grey LOPCOW-MSD-PIV approach. Ekonomi Politika ve Finans Araştırmaları Dergisi, 10, 59-87. https://doi.org/10.30784/epfad.1813013
  48. Tutar, H., Kavas, Y.B., Medetoğlu, B. and Koltai, J. (2025). Analysis of financial performance of deposit banks in Turkey using multi-criteria decision-making methods. Journal of International Studies, 18(1), 196-215. https://doi.org/10.14254/2071-8330.2025/18-1/12
  49. Ünlü, U. and Çıtak, Ö. (2025). Assessing corporate sustainability in Turkish banks: WASPAS and ARAS approaches in the COVID-19 and the post- COVID-19 era. Ekonomi Politika ve Finans Araştırmaları Dergisi, 10(2), 758-780. https://doi.org/10.30784/epfad.1678030
  50. Wu, H.Y., Tzeng, G.H. and Chen, Y.H. (2009). A fuzzy MCDM approach for evaluating banking performance based on Balanced Scorecard. Expert Systems with Applications, 36(6), 10135-10147. https://doi.org/10.1016/j.eswa.2009.01.005
  51. Yang, Y., Yang, M. and Shan, Z. (2025). Decision making method for operational performance of Chinese commercial banks based on scenario fuzzy sets. Scientific Reports, 15(1), 19829. https://doi.org/10.1038/s41598-025-04584-2
  52. Yılmaz, Ö. and Yakut, E. (2021). Entropi temelli TOPSIS ve VIKOR yöntemleri ile bankacılık sektöründe finansal performans değerlendirmesi. Atatürk Üniversitesi İktisadi ve İdari Bilimler Dergisi, 35(4), 1297-1321. https://doi.org/10.16951/atauniiibd.874660
  53. Yurttadur, M. and Taşcı, M.Z. (2025). Kurumsal yönetim endeksinde yer alan bankaların finansal performansları ile kurumsal yönetim derecelendirme notları arasındaki ilişkinin PSI–CRADIS entegre ÇKKV modeli ile değerlendirilmesi. Uluslararası Bankacılık Ekonomi ve Yönetim Araştırmaları Dergisi, 8(1), 25-45. https://doi.org/10.52736/ubeyad.1660522