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Evaluation of Financial Performance of BIST Participation Banks: CAMELS and Multi-Criteria Decision Making (MCDM) Approach

Yıl 2024, , 923 - 940, 30.09.2024
https://doi.org/10.29023/alanyaakademik.1511040

Öz

This study presented an evaluation of the financial performances of six Turkish participation banks with feats of being registered in Borsa Istanbul for the year 2023. The analysis employed 20 different financial ratios using the CAMELS rating. The weighting method of this study was determined to be the CRITIC approach. The financial performances of the 6 participation banks traded in BIST were performed with the weighting method of the CAMELS rating and the CRITIC approach. The performance evaluation was done with the ARAS, TOPSIS, and COPRAS methodologies. As can be observed from the tables, it is clear that EMLAK Katılım Bank has the highest performance in all three methods. Making an evaluation, one can safely argue that the participation banks’ financial performance provides similar results in the three different ways. The similarity, plus the reliability, and the validity of these analyses conducted in this study are that the utilization of the TOPSIS, COPRAS, and ARAS approaches yield similar results.

Kaynakça

  • Abd Rahim, Z. H., Fahami, N. A., Azhar, F. W., Abd Karim, H., & Rahim, S. K. N. A. (2020). Application of TOPSIS analysis method in financial performance evaluation: a case study of construction sector in Malaysia. Advances in Business Research International Journal, 6(1), 11-19.
  • Abdulgader, F. S., Eid, R., & Rouyendegh, B. D. (2018). Development of decision support model for selecting a maintenance plan using a fuzzy mcdm approach: a theoretical framework. Applied Computational Intelligence and Soft Computing, 2018, 1-14. https://doi.org/10.1155/2018/9346945
  • Abuzarqa, R., & Tarnoczi, T. (2021). Performance evaluation using the CAMELS model: A comparative study of local commercial banks in Qatar and Kuwait. Banks and Bank Systems, 16(3), 152-165.
  • Al-Khulaidi, A. A. G., Nasser, A. A., Al-Ashwal, M. H. Y., Al-Ashwal, M. M. Y., & Altayeb, A. M. (2024). Investigating information security risk management in Yemeni banks: An CILOS-TOPSIS approach. Multidisciplinary Science Journal, 6(9), 2024175-2024175.
  • Altinay, A. T., Doğan, M., & Kevser, M. (2022). Comparing the financial performance of islamic banks in 10 countries: new evidence using entropy and waspas methods. The Economics and Finance Letters, 9(2), 197-210. https://doi.org/10.18488/29.v9i2.3110
  • Arif, M., Haribowo, I., & Suherlan, A. (2018). Spin-off policy and efficiency in the indonesian islamic banking industry. Banks and Bank Systems, 13(1), 1-10. https://doi.org/10.21511/bbs.13(1).2018.01
  • Azad, M. A. K., Yazdi, A. K., Birau, F. R., & Spulbăr, C. (2022). Revisiting camels rating system and the performance of asean banks: a comprehensive mcdm/z-numbers approach. IEEE Access, 10, 54098-54109. https://doi.org/10.1109/access.2022.3171339
  • Beheshtinia, M. A., & Omidi, S. (2017). A hybrid mcdm approach for performance evaluation in the banking industry. Kybernetes, 46(8), 1386-1407. https://doi.org/10.1108/k-03-2017-0105
  • Bekar, E. T., Çakmakçı, M., & Kahraman, C. (2016). Fuzzy copras method for performance measurement in total productive maintenance: a comparative analysis. Journal of Business Economics and Management, 17(5), 663-684. https://doi.org/10.3846/16111699.2016.1202314
  • Bos, G., & Chatterjee, N. D. (2016). Fuzzy hybrid mcdm approach for selection of wind turbine service technicians. Management Science Letters, 6(2016), 1-18. https://doi.org/10.5267/j.msl.2015.12.004
  • Bozdoğan, T., Odabas, A., & Shegiwal, A. H. (2021). Analysis of financial performance of foreign banks having branches in Turkey by TOPSIS and ELECTRE methods. Alanya Akademik Bakış, 5(2), 1049-1067.
  • Bulut, E., & Simsek, A. İ. (2022). Evaluation of financial performance of some technology companies traded in borsa Istanbul by topsis method. Fırat Üniversitesi Uluslararası İktisadi ve İdari Bilimler Dergisi, 6(2), 103-130.
  • Bulut, H. İ., & Er, B. (2012). Katılım finansmanı. Türkiye Katılım Bankaları Birliği Yayın No:3. İstanbul.
  • Chitnis, A., & Vaidya, O. S. (2018). Efficiency ranking method using SFA and TOPSIS (ERM-ST): case of Indian banks. Benchmarking: An International Journal, 25(2), 471-488.
  • Cole, R. A., & Wu, Q. (2009, April). Predicting bank failures using a simple dynamic hazard model. In 22nd Australasian Finance and Banking Conference, 16-18.
  • Coşkun, S. (2023). Evaluation of financial performance of bist sustainability 25 index companies within the framework of sdgs reporting with topsis approach. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 18(3), 707-729. https://doi.org/10.17153/oguiibf.1312831
  • Coşkuner, A., & Rençber, Ö. F. (2024). Determination of performance ranking of participation banks with ciritic-based topsis method. Sakarya Üniversitesi İşletme Enstitüsü Dergisi, 6(1), 57-70. https://doi.org/10.47542/sauied.1448208
  • Danlami, M. R., Abduh, M., & Razak, L. A. (2022). Camels, risk-sharing financing, institutional quality and stability of islamic banks: evidence from 6 oic countries. Journal of Islamic Accounting and Business Research, 13(8), 1155-1175. https://doi.org/10.1108/jiabr-08-2021-0227
  • Dash, M. (2017). A model for bank performance measurement integrating multivariate factor structure with multi-criteria promethee methodology. Asian Journal of Finance & Amp; Accounting, 9(1), 310. https://doi.org/10.5296/ajfa.v9i1.11073
  • Dewi, R. K., Ananta, M. T., Fanani, L., Brata, K. C., & Priandani, N. D. (2018). The development of mobile culinary recommendation system based on group decision support system. International Journal of Interactive Mobile Technologies (iJIM), 12(3), 209. https://doi.org/10.3991/ijim.v12i3.7799
  • Diakoulaki, D., Mavrotas, G., & 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
  • Ecer, F. (2017). An integrated fuzzy ahp and aras model to evaluate mobile banking services. Technological and Economic Development of Economy, 24(2), 670-695. https://doi.org/10.3846/20294913.2016.1255275
  • Ecer, F., & Güneş, E. (2024). G7 ülkelerinin bilgi iletişim teknoloji düzeylerini belirleme: MEREC-CRITIC entegre ağırlıklı CoCoSo metodolojisi. Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 11(1), 219-242.
  • Erol, C., Baklaci, H. F., Aydoğan, B., & Tunç, G. (2014). Performance comparison of islamic (participation) banks and commercial banks in Turkish banking sector. EuroMed Journal of Business, 9(2), 114-128. https://doi.org/10.1108/emjb-05-2013-0024
  • Ghosh, R., & Saima, F. N. (2021). Resilience of commercial banks of bangladesh to the shocks caused by covid-19 pandemic: an application of mcdm-based approaches. Asian Journal of Accounting Research, 6(3), 281-295. https://doi.org/10.1108/ajar-10-2020-0102
  • Gilbert, R. A., Meyer, A. P., & Vaughan, M. D. (2000). The role of a CAMEL downgrade model in bank surveillance. Federal Reserve Bank of St. Louis Working Paper Series, 2000-2021.
  • Haddad, B., Ferreira, P., Tassoult, H., & Liazid, A. (2018). Planning of renewable electricity sources using ahp method. The Algerian case. The 5th International Seminar on New and Renewable Energies.
  • Hamamcı, H. N., & Karkacıer, A. (2022). Evaluation of financial performance of participation banks in Turkey and GCC with TOPSIS method. Uluslararası Ekonomi ve Yenilik Dergisi, 8(1), 55-78.
  • Hwang, C. L., & Yoon, P. (1981). Multiple attribute decision making In: Lecture Notes in Economics and Mathematical Systems, Springer-VerlagBerlin.
  • Islam, M. Z. R. M. S., & Shohidul, M. (2018). Use of CAMEL rating framework: A comparative performance evaluation of selected Bangladeshi private commercial banks. International Journal of Economics and Finance, 10(1), 120-128.
  • İç, Y. T., Celik, B., Kavak, S., & Baki, B. (2020). Development of a multi-criteria decision-making model for comparing the performance of Turkish commercial banks. Journal of Advances in Management Research, 18(2), 250-272. https://doi.org/10.1108/jamr-05-2020-0083
  • Karakaya, A. (2020). Bulanık karar verme yaklaşımıyla katılım bankaları finansal performansı. Uluslararası İktisadi ve İdari İncelemeler Dergisi, Prof. Dr. Talha Ustasüleyman Özel Sayısı.
  • Kushadianto, B. N. D., & Ciptomulyono, U. (2022). Ahp-copras model for determination of suitability of surveyor assignment for survey of new building ships at pt. bki main branch surabaya. IJEBD (International Journal of Entrepreneurship and Business Development), 5(3), 476-486. https://doi.org/10.29138/ijebd.v5i3.1844
  • Maredza, A., Antunes, J., Pimenta, R. d. C., & Tan, Y. (2021). Social welfare and bank performance: evidence from a stochastic neural hybrid mcdm approach. Journal of Economic Studies, 49(7), 1137-1158. https://doi.org/10.1108/jes-05-2021-0236
  • Matić, B., Jovanović, S., Das, D. K., Zavadskas, E. K., Stević, Ž., Sremac, S., … & Marinković, M. (2019). A new hybrid mcdm model: sustainable supplier selection in a construction company. Symmetry, 11(3), 353. https://doi.org/10.3390/sym11030353
  • Mishra, A., & Kumar, R. (2024). Ranking of cloud services by applying bwm-topsis, bwm-aras, and bwm-copras hybrid mcdm methods. https://doi.org/10.21203/rs.3.rs-4094143/v1
  • Mishra, A. R., Rani, P., Hezam, I. M., & Deveci, M. (2023). Dual probabilistic linguistic full consistency additive ratio assessment model for medical equipment supplier selection. International Journal of Fuzzy Systems, 25(8), 3216-3232. https://doi.org/10.1007/s40815-023-01526-w
  • Muhammad, R., & Triharyono, C. (2019). Analysis of islamic banking financial performance before, during and after global financial crisis. Jurnal Ekonomi &Amp; Keuangan Islam, 5(2), 80-86. https://doi.org/10.20885/jeki.vol5.iss2.art5
  • Nanda, I., Rumandan, R. J., & Sinlae, A. A. J. (2022). Implementation of additive ratio assessment (aras) in decision support systems for wi-fi repeater selection. Applied Technology and Computing Science Journal, 5(2), 50-63. https://doi.org/10.33086/atcsj.v5i2.3738
  • Pakšytė, I., & Jurevičienė, D. (2022). Study on the eligibility of venture capital funds in the united states market. 12th International Scientific Conference “Business and Management 2022”. https://doi.org/10.3846/bm.2022.778
  • Parlakkaya, R., & Akten Çürük, A. (2011). Finansal rasyoların katılım bankaları ve geleneksel bankalar arasında bir tasnif aracı olarak kullanımı: Türkiye örneği. Ege Akademik Bakış, 11(3).
  • Prasad, R. (2019). Selection of internal safety auditors in an indian construction organization based on the swara and aras methods. Journal of Occupational Health and Epidemiology, 8(3), 134-140. https://doi.org/10.29252/johe.8.3.134
  • Sakarya, Ş., & Aksu, M. (2020). Ulaşım sektöründeki işletmelerin finansal performanslarının geliştirilmiş Entropi temelli TOPSIS yöntemi ile değerlendirilmesi. Optimum Ekonomi ve Yönetim Bilimleri Dergisi, 7(1), 21-40.
  • Sama, H. R., Kosuri, S. V. K., & Kalvakolanu, S. (2020). Evaluating and ranking the indian private sector banks—a multi‐criteria decision‐making approach. Journal of Public Affairs. https://doi.org/10.1002/pa.2419
  • Sapkota, G., Ghadai, R. K., Čep, R., Shanmugasundar, G., Chohan, J. S., & Kalita, K. (2024). Enhancing efficiency in photo chemical machining: a multivariate decision-making approach. Frontiers in Mechanical Engineering, 10. https://doi.org/10.3389/fmech.2024.1325018
  • Sarı, T., & Kayral, İ. E. (2019). Performance evaluation of Turkish banks with TOPSIS and stepwise regression. In International Conference on Research in Business, Management & Finance, Amsterdam, Netherlands.
  • Sarıçalı, G., & Kundakçı, N. (2016). AHP ve COPRAS yöntemleri ile otel alternatiflerinin değerlendirilmesi. International Review of Economics and Management, 4(1), 45-66.
  • Sliogeriene, J., Turskis, Z., & Streimikiene, D. (2013). Analysis and choice of energy generation technologies: the multiple criteria assessment on the case study of Lithuania. Energy Procedia, 32, 11-20.
  • Taherdoost, H., & Mohebi, A. (2024). A comprehensive guide to the copras method for multi-criteria decision making. Journal of Management Science &Amp; Engineering Research, 7(2), 1-14. https://doi.org/10.30564/jmser.v7i2.6280
  • Terzioğlu, M. K., Kurt, E. S., Yaşar, A., & Köken, M. (2022). BİST100-Enerji sektörü finansal performansı: SWARA-VIKOR ve SWARA-WASPAS. Alanya Akademik Bakış, 6(2), 2439-2455.
  • Terzioğlu, M. K., Temelli, S., Yaşar, A., & Özdemir, Ö. (2023). Bankacılık sektöründe finansal ve çevresel performansların çok kriterli karar verme yöntemleri ile karşılaştırılması. Karadeniz Teknik Üniversitesi Sosyal Bilimler Enstitüsü Sosyal Bilimler Dergisi, 13(25), 21-45.
  • Ünlü, U., Yalçın, N., & Avşarlıgil, N. (2022). Analysis of efficiency and productivity of commercial banks in turkey pre- and during covid-19 with an integrated mcdm approach. Mathematics, 10(13), 2300. https://doi.org/10.3390/math10132300
  • Ünvan, Y. A. (2020). Financial performance analysis of banks with TOPSIS and fuzzy TOPSIS approaches. Gazi University Journal of Science, 33(4), 904-923.
  • Wang, P., Lin, Y., & Wang, Z. (2022). An integrated decision-making model based on plithogenic-neutrosophic rough number for sustainable financing enterprise selection. Sustainability, 14(19), 12473. https://doi.org/10.3390/su141912473
  • Yagli, İ. (2020). Multi-criteria financial performance analysis of Turkish participation banks. Alanya Akademik Bakış, 4(3), 861-873.
  • Yaşar, A., & Terzioğlu, M. K. (2022). Financial performance analysis of enterprises in the energy sector with the entropy based aras and gri method. BİLTÜRK Journal of Economics and Related Studies, 4(3), 145-159.
  • Yazdi, A. K., Hanne, T., & Gómez, J. C. O. (2020). Evaluating the performance of colombian banks by hybrid multicriteria decision making methods. Journal of Business Economics and Management, 21(6), 1707-1730. https://doi.org/10.3846/jbem.2020.11758
  • Zavadskas, E. K., & Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision‐making, Technological and Economic Development of Economy, 16(2), 159-172.
  • Zavadskas, E. K., Cavallaro, F., Podvezko, V., Ubarte, I., & Kaklauskas, A. (2017). Mcdm assessment of a healthy and safe built environment according to sustainable development principles: a practical neighborhood approach in vilnius. Sustainability, 9(5), 702. https://doi.org/10.3390/su9050702
  • Zubiria, A., Menéndez, Á., Grande, H., Meneses, P., & Fernandez, G. (2022). Multi-criteria decision-making problem for energy storage technology selection for different grid applications. Energies, 15(20), 7612. https://doi.org/10.3390/en15207612

Evaluation of Financial Performance of BIST Participation Banks: CAMELS and Multi-Criteria Decision Making (MCDM) Approach

Yıl 2024, , 923 - 940, 30.09.2024
https://doi.org/10.29023/alanyaakademik.1511040

Öz

Bu çalışmada, Borsa İstanbul'da (BİST) işlem gören altı Türk katılım bankasının 2023 yılına ait finansal performansları CAMELS kriterleri çerçevesinde oluşturulmuş olan 20 adet finansal oran kullanılarak değerlendirilmiştir. Çalışmada ağırlıklandırma yöntemi olarak CRITIC yöntemi kullanılmıştır. CAMELS kriterleri CRITIC yöntemi ile ağırlıklandırldıktan sonra BİST’te işlem gören 6 katılım bankasının finansal performansları ARAS, TOPSIS ve COPRAS yöntemleri kullanılarak değerlendirilmiştir. Elde edilen sonuçlar incelendiğinde kullanılan her üç yöntem için de EMLAK Katılım Bankası’nın en iyi performansa sahip banka olduğu ortaya konulmuştur. Yapılan analizler sonucunda katılım bankalarının finansal performanslarının her üç yöntem için de benzer sonuçlar ürettiği görülmektedir. TOPSIS, COPRAS ve ARAS yöntemleri ile yapılan analizlerin benzer sonuçlar vermesi bu çalışmada yapılan analizlerin tutarlılığı, güvenilirliğini ve geçerliliğini artırmaktadır.

Kaynakça

  • Abd Rahim, Z. H., Fahami, N. A., Azhar, F. W., Abd Karim, H., & Rahim, S. K. N. A. (2020). Application of TOPSIS analysis method in financial performance evaluation: a case study of construction sector in Malaysia. Advances in Business Research International Journal, 6(1), 11-19.
  • Abdulgader, F. S., Eid, R., & Rouyendegh, B. D. (2018). Development of decision support model for selecting a maintenance plan using a fuzzy mcdm approach: a theoretical framework. Applied Computational Intelligence and Soft Computing, 2018, 1-14. https://doi.org/10.1155/2018/9346945
  • Abuzarqa, R., & Tarnoczi, T. (2021). Performance evaluation using the CAMELS model: A comparative study of local commercial banks in Qatar and Kuwait. Banks and Bank Systems, 16(3), 152-165.
  • Al-Khulaidi, A. A. G., Nasser, A. A., Al-Ashwal, M. H. Y., Al-Ashwal, M. M. Y., & Altayeb, A. M. (2024). Investigating information security risk management in Yemeni banks: An CILOS-TOPSIS approach. Multidisciplinary Science Journal, 6(9), 2024175-2024175.
  • Altinay, A. T., Doğan, M., & Kevser, M. (2022). Comparing the financial performance of islamic banks in 10 countries: new evidence using entropy and waspas methods. The Economics and Finance Letters, 9(2), 197-210. https://doi.org/10.18488/29.v9i2.3110
  • Arif, M., Haribowo, I., & Suherlan, A. (2018). Spin-off policy and efficiency in the indonesian islamic banking industry. Banks and Bank Systems, 13(1), 1-10. https://doi.org/10.21511/bbs.13(1).2018.01
  • Azad, M. A. K., Yazdi, A. K., Birau, F. R., & Spulbăr, C. (2022). Revisiting camels rating system and the performance of asean banks: a comprehensive mcdm/z-numbers approach. IEEE Access, 10, 54098-54109. https://doi.org/10.1109/access.2022.3171339
  • Beheshtinia, M. A., & Omidi, S. (2017). A hybrid mcdm approach for performance evaluation in the banking industry. Kybernetes, 46(8), 1386-1407. https://doi.org/10.1108/k-03-2017-0105
  • Bekar, E. T., Çakmakçı, M., & Kahraman, C. (2016). Fuzzy copras method for performance measurement in total productive maintenance: a comparative analysis. Journal of Business Economics and Management, 17(5), 663-684. https://doi.org/10.3846/16111699.2016.1202314
  • Bos, G., & Chatterjee, N. D. (2016). Fuzzy hybrid mcdm approach for selection of wind turbine service technicians. Management Science Letters, 6(2016), 1-18. https://doi.org/10.5267/j.msl.2015.12.004
  • Bozdoğan, T., Odabas, A., & Shegiwal, A. H. (2021). Analysis of financial performance of foreign banks having branches in Turkey by TOPSIS and ELECTRE methods. Alanya Akademik Bakış, 5(2), 1049-1067.
  • Bulut, E., & Simsek, A. İ. (2022). Evaluation of financial performance of some technology companies traded in borsa Istanbul by topsis method. Fırat Üniversitesi Uluslararası İktisadi ve İdari Bilimler Dergisi, 6(2), 103-130.
  • Bulut, H. İ., & Er, B. (2012). Katılım finansmanı. Türkiye Katılım Bankaları Birliği Yayın No:3. İstanbul.
  • Chitnis, A., & Vaidya, O. S. (2018). Efficiency ranking method using SFA and TOPSIS (ERM-ST): case of Indian banks. Benchmarking: An International Journal, 25(2), 471-488.
  • Cole, R. A., & Wu, Q. (2009, April). Predicting bank failures using a simple dynamic hazard model. In 22nd Australasian Finance and Banking Conference, 16-18.
  • Coşkun, S. (2023). Evaluation of financial performance of bist sustainability 25 index companies within the framework of sdgs reporting with topsis approach. Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi, 18(3), 707-729. https://doi.org/10.17153/oguiibf.1312831
  • Coşkuner, A., & Rençber, Ö. F. (2024). Determination of performance ranking of participation banks with ciritic-based topsis method. Sakarya Üniversitesi İşletme Enstitüsü Dergisi, 6(1), 57-70. https://doi.org/10.47542/sauied.1448208
  • Danlami, M. R., Abduh, M., & Razak, L. A. (2022). Camels, risk-sharing financing, institutional quality and stability of islamic banks: evidence from 6 oic countries. Journal of Islamic Accounting and Business Research, 13(8), 1155-1175. https://doi.org/10.1108/jiabr-08-2021-0227
  • Dash, M. (2017). A model for bank performance measurement integrating multivariate factor structure with multi-criteria promethee methodology. Asian Journal of Finance & Amp; Accounting, 9(1), 310. https://doi.org/10.5296/ajfa.v9i1.11073
  • Dewi, R. K., Ananta, M. T., Fanani, L., Brata, K. C., & Priandani, N. D. (2018). The development of mobile culinary recommendation system based on group decision support system. International Journal of Interactive Mobile Technologies (iJIM), 12(3), 209. https://doi.org/10.3991/ijim.v12i3.7799
  • Diakoulaki, D., Mavrotas, G., & 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
  • Ecer, F. (2017). An integrated fuzzy ahp and aras model to evaluate mobile banking services. Technological and Economic Development of Economy, 24(2), 670-695. https://doi.org/10.3846/20294913.2016.1255275
  • Ecer, F., & Güneş, E. (2024). G7 ülkelerinin bilgi iletişim teknoloji düzeylerini belirleme: MEREC-CRITIC entegre ağırlıklı CoCoSo metodolojisi. Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 11(1), 219-242.
  • Erol, C., Baklaci, H. F., Aydoğan, B., & Tunç, G. (2014). Performance comparison of islamic (participation) banks and commercial banks in Turkish banking sector. EuroMed Journal of Business, 9(2), 114-128. https://doi.org/10.1108/emjb-05-2013-0024
  • Ghosh, R., & Saima, F. N. (2021). Resilience of commercial banks of bangladesh to the shocks caused by covid-19 pandemic: an application of mcdm-based approaches. Asian Journal of Accounting Research, 6(3), 281-295. https://doi.org/10.1108/ajar-10-2020-0102
  • Gilbert, R. A., Meyer, A. P., & Vaughan, M. D. (2000). The role of a CAMEL downgrade model in bank surveillance. Federal Reserve Bank of St. Louis Working Paper Series, 2000-2021.
  • Haddad, B., Ferreira, P., Tassoult, H., & Liazid, A. (2018). Planning of renewable electricity sources using ahp method. The Algerian case. The 5th International Seminar on New and Renewable Energies.
  • Hamamcı, H. N., & Karkacıer, A. (2022). Evaluation of financial performance of participation banks in Turkey and GCC with TOPSIS method. Uluslararası Ekonomi ve Yenilik Dergisi, 8(1), 55-78.
  • Hwang, C. L., & Yoon, P. (1981). Multiple attribute decision making In: Lecture Notes in Economics and Mathematical Systems, Springer-VerlagBerlin.
  • Islam, M. Z. R. M. S., & Shohidul, M. (2018). Use of CAMEL rating framework: A comparative performance evaluation of selected Bangladeshi private commercial banks. International Journal of Economics and Finance, 10(1), 120-128.
  • İç, Y. T., Celik, B., Kavak, S., & Baki, B. (2020). Development of a multi-criteria decision-making model for comparing the performance of Turkish commercial banks. Journal of Advances in Management Research, 18(2), 250-272. https://doi.org/10.1108/jamr-05-2020-0083
  • Karakaya, A. (2020). Bulanık karar verme yaklaşımıyla katılım bankaları finansal performansı. Uluslararası İktisadi ve İdari İncelemeler Dergisi, Prof. Dr. Talha Ustasüleyman Özel Sayısı.
  • Kushadianto, B. N. D., & Ciptomulyono, U. (2022). Ahp-copras model for determination of suitability of surveyor assignment for survey of new building ships at pt. bki main branch surabaya. IJEBD (International Journal of Entrepreneurship and Business Development), 5(3), 476-486. https://doi.org/10.29138/ijebd.v5i3.1844
  • Maredza, A., Antunes, J., Pimenta, R. d. C., & Tan, Y. (2021). Social welfare and bank performance: evidence from a stochastic neural hybrid mcdm approach. Journal of Economic Studies, 49(7), 1137-1158. https://doi.org/10.1108/jes-05-2021-0236
  • Matić, B., Jovanović, S., Das, D. K., Zavadskas, E. K., Stević, Ž., Sremac, S., … & Marinković, M. (2019). A new hybrid mcdm model: sustainable supplier selection in a construction company. Symmetry, 11(3), 353. https://doi.org/10.3390/sym11030353
  • Mishra, A., & Kumar, R. (2024). Ranking of cloud services by applying bwm-topsis, bwm-aras, and bwm-copras hybrid mcdm methods. https://doi.org/10.21203/rs.3.rs-4094143/v1
  • Mishra, A. R., Rani, P., Hezam, I. M., & Deveci, M. (2023). Dual probabilistic linguistic full consistency additive ratio assessment model for medical equipment supplier selection. International Journal of Fuzzy Systems, 25(8), 3216-3232. https://doi.org/10.1007/s40815-023-01526-w
  • Muhammad, R., & Triharyono, C. (2019). Analysis of islamic banking financial performance before, during and after global financial crisis. Jurnal Ekonomi &Amp; Keuangan Islam, 5(2), 80-86. https://doi.org/10.20885/jeki.vol5.iss2.art5
  • Nanda, I., Rumandan, R. J., & Sinlae, A. A. J. (2022). Implementation of additive ratio assessment (aras) in decision support systems for wi-fi repeater selection. Applied Technology and Computing Science Journal, 5(2), 50-63. https://doi.org/10.33086/atcsj.v5i2.3738
  • Pakšytė, I., & Jurevičienė, D. (2022). Study on the eligibility of venture capital funds in the united states market. 12th International Scientific Conference “Business and Management 2022”. https://doi.org/10.3846/bm.2022.778
  • Parlakkaya, R., & Akten Çürük, A. (2011). Finansal rasyoların katılım bankaları ve geleneksel bankalar arasında bir tasnif aracı olarak kullanımı: Türkiye örneği. Ege Akademik Bakış, 11(3).
  • Prasad, R. (2019). Selection of internal safety auditors in an indian construction organization based on the swara and aras methods. Journal of Occupational Health and Epidemiology, 8(3), 134-140. https://doi.org/10.29252/johe.8.3.134
  • Sakarya, Ş., & Aksu, M. (2020). Ulaşım sektöründeki işletmelerin finansal performanslarının geliştirilmiş Entropi temelli TOPSIS yöntemi ile değerlendirilmesi. Optimum Ekonomi ve Yönetim Bilimleri Dergisi, 7(1), 21-40.
  • Sama, H. R., Kosuri, S. V. K., & Kalvakolanu, S. (2020). Evaluating and ranking the indian private sector banks—a multi‐criteria decision‐making approach. Journal of Public Affairs. https://doi.org/10.1002/pa.2419
  • Sapkota, G., Ghadai, R. K., Čep, R., Shanmugasundar, G., Chohan, J. S., & Kalita, K. (2024). Enhancing efficiency in photo chemical machining: a multivariate decision-making approach. Frontiers in Mechanical Engineering, 10. https://doi.org/10.3389/fmech.2024.1325018
  • Sarı, T., & Kayral, İ. E. (2019). Performance evaluation of Turkish banks with TOPSIS and stepwise regression. In International Conference on Research in Business, Management & Finance, Amsterdam, Netherlands.
  • Sarıçalı, G., & Kundakçı, N. (2016). AHP ve COPRAS yöntemleri ile otel alternatiflerinin değerlendirilmesi. International Review of Economics and Management, 4(1), 45-66.
  • Sliogeriene, J., Turskis, Z., & Streimikiene, D. (2013). Analysis and choice of energy generation technologies: the multiple criteria assessment on the case study of Lithuania. Energy Procedia, 32, 11-20.
  • Taherdoost, H., & Mohebi, A. (2024). A comprehensive guide to the copras method for multi-criteria decision making. Journal of Management Science &Amp; Engineering Research, 7(2), 1-14. https://doi.org/10.30564/jmser.v7i2.6280
  • Terzioğlu, M. K., Kurt, E. S., Yaşar, A., & Köken, M. (2022). BİST100-Enerji sektörü finansal performansı: SWARA-VIKOR ve SWARA-WASPAS. Alanya Akademik Bakış, 6(2), 2439-2455.
  • Terzioğlu, M. K., Temelli, S., Yaşar, A., & Özdemir, Ö. (2023). Bankacılık sektöründe finansal ve çevresel performansların çok kriterli karar verme yöntemleri ile karşılaştırılması. Karadeniz Teknik Üniversitesi Sosyal Bilimler Enstitüsü Sosyal Bilimler Dergisi, 13(25), 21-45.
  • Ünlü, U., Yalçın, N., & Avşarlıgil, N. (2022). Analysis of efficiency and productivity of commercial banks in turkey pre- and during covid-19 with an integrated mcdm approach. Mathematics, 10(13), 2300. https://doi.org/10.3390/math10132300
  • Ünvan, Y. A. (2020). Financial performance analysis of banks with TOPSIS and fuzzy TOPSIS approaches. Gazi University Journal of Science, 33(4), 904-923.
  • Wang, P., Lin, Y., & Wang, Z. (2022). An integrated decision-making model based on plithogenic-neutrosophic rough number for sustainable financing enterprise selection. Sustainability, 14(19), 12473. https://doi.org/10.3390/su141912473
  • Yagli, İ. (2020). Multi-criteria financial performance analysis of Turkish participation banks. Alanya Akademik Bakış, 4(3), 861-873.
  • Yaşar, A., & Terzioğlu, M. K. (2022). Financial performance analysis of enterprises in the energy sector with the entropy based aras and gri method. BİLTÜRK Journal of Economics and Related Studies, 4(3), 145-159.
  • Yazdi, A. K., Hanne, T., & Gómez, J. C. O. (2020). Evaluating the performance of colombian banks by hybrid multicriteria decision making methods. Journal of Business Economics and Management, 21(6), 1707-1730. https://doi.org/10.3846/jbem.2020.11758
  • Zavadskas, E. K., & Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision‐making, Technological and Economic Development of Economy, 16(2), 159-172.
  • Zavadskas, E. K., Cavallaro, F., Podvezko, V., Ubarte, I., & Kaklauskas, A. (2017). Mcdm assessment of a healthy and safe built environment according to sustainable development principles: a practical neighborhood approach in vilnius. Sustainability, 9(5), 702. https://doi.org/10.3390/su9050702
  • Zubiria, A., Menéndez, Á., Grande, H., Meneses, P., & Fernandez, G. (2022). Multi-criteria decision-making problem for energy storage technology selection for different grid applications. Energies, 15(20), 7612. https://doi.org/10.3390/en15207612
Toplam 60 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Çok Ölçütlü Karar Verme
Bölüm Makaleler
Yazarlar

Emre Bulut 0000-0002-2884-1405

Ahmed İhsan Şimşek 0000-0002-2900-3032

Yayımlanma Tarihi 30 Eylül 2024
Gönderilme Tarihi 5 Temmuz 2024
Kabul Tarihi 13 Eylül 2024
Yayımlandığı Sayı Yıl 2024

Kaynak Göster

APA Bulut, E., & Şimşek, A. İ. (2024). Evaluation of Financial Performance of BIST Participation Banks: CAMELS and Multi-Criteria Decision Making (MCDM) Approach. Alanya Akademik Bakış, 8(3), 923-940. https://doi.org/10.29023/alanyaakademik.1511040