Analyzing the Turkish insurance companies’ financial performance traded on BIST implementing the critic-based PIV method
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DOIhttp://dx.doi.org/10.21511/ins.15(2).2024.05
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Article InfoVolume 15 2024, Issue #2, pp. 47-60
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The insurance industry, which is an important component of the financial channel, is an essential part of the Turkish economy, and assessing the financial performance is critical for insurance companies to improve efficiency and productivity, increase competitiveness, and enhance fiscal health. The study presented a technique for assessing the financial performance of all insurance companies registered in Borsa Istanbul by implementing an integrated method that combines the Criteria Importance Through Intercriteria Correlation (CRITIC) and Proximity Indexed Value (PIV) methods. The rationale for implementing the PIV method is the lack of adequate financial studies available on the insurance companies that employed this specific model. Initially, 18 evaluation criteria were defined. The CRITIC method was applied for the criteria weights, and insurance companies were ranked using PIV. Subsequently, the COPRAS, VIKOR, ARAS, and SAW Multi-Criteria Decision-Making (MCDM) methodologies were applied. Performance rankings derived from PIV were compared with those obtained from other MCDM models employed. Finally, Spearman’s Rank Correlation and Kendall’s Rank Correlation Coefficient methods were applied to analyze the extent of correlations and interactions between ranking outcomes. The PIV assessment results pointed out that AGESA received the highest rank for financial performance, and AKGRT had the lowest rank. AGESA consistently received high rankings compared to all other methods examined. Nevertheless, RAYSG and AKGRT constantly ranked poorly. All deployed methods ranked AKGRT and RAYSG in the final two positions. The study’s findings underscore that ranking outcomes of PIV largely align with alternate MCDM methodologies utilized.
- Keywords
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JEL Classification (Paper profile tab)G22, L25, D81
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References48
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Tables19
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Figures1
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- Figure 1. Comparative rankings derived from MCDM methods
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- Table 1. Constructing the decision matrix
- Table 2. Criteria and weights (Wj)
- Table 3. Decision matrix adjusted with Z-value standardization
- Table 4. Normalized decision matrix
- Table 5. Weighted normalized decision matrix
- Table 6. Weighted proximity index
- Table 7. Overall proximity values
- Table 8. Ranking results based on PIV
- Table 9. Relative importance levels of decision alternatives and ranking
- Table 10. Ranked Si, Ri, and Qi values
- Table 11. Ranking results based on VIKOR
- Table 12. Si, Ki, and ranking (R)
- Table 13. Ranking the alternatives
- Table 14. Ranking results of MCDM methodologies
- Table 15. Spearman coefficient of rank correlation
- Table 16. Correlation matrix (Kendall)
- Table A1. Insurance companies traded on BIST and their codes
- Table B1. Groups, evaluation criteria, codes and impact directions
- Table C1. Example of correlation coefficient interpretation
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- Agesa. (2023). Home Page.
- Aksigorta. (2023). Home Page.
- Altıntaş, F. F. (2023). G7 Grubu Ülkelerin Bütçe Şeffaflığı Performanslarının Analizi: MEREC Tabanlı PIV Yöntemi İle Bir Uygulama [Analysis of Budget Transparency Performances of G7 Group Countries: An Application with MEREC-Based PIV Method]. Journal of ASU FEAS, 15(4), 323-340. (In Turkish).
- Anadolu Hayat. (2023). Home Page.
- Anadolu Sigorta. (2023). Home Page.
- Aydın Ünal, E. (2019). Bütünleşik Entropi Ve Edas Yöntemleri Kullanılarak Bıst Sigorta Şirketlerinin Performansının Ölçülmesi [Measuring The Performance Of Bist Insurance Companies Using Integrated Entropy And Edas Methods]. Finans Ekonomi Ve Sosyal Araştırmalar Dergisi – Research of Financial Economic and Social Studies, 4(4), 555-566. (In Turkish).
- Aydın, Y. (2019). Türkiye’de Hayat\Emeklilik Sigorta Sektörünün Finansal Performans Analizi [Financial Performance Analysis of Life / Retirement Insurance Sectors in Turkey]. Finans Ekonomi Ve Sosyal Araştırmalar Dergisi – Research of Financial Economic and Social Studies (RFES), 4(1), 107-118. (In Turkish).
- Baydaş, M., Eren, T., Stević, Ž., Starčević, V., & Parlakkaya, R. (2023). Proposal for an objective binary benchmarking framework that validates each other for comparing MCDM methods through data analytics. PeerJ Computer Science, 9, e1350.
- Bektaş, S. (2023). Evaluation of the Financial Performance of the Companies in the BIST Insurance (XSGRT) Index in 2021 by MCDM Methods. Journal of Management and Economics, 30(4). 787-815. (In Turkish).
- Bhole, G. P., & Deshmukh, T. (1018). Multi Criteria Decision Making (MCDM) Methods and its applications. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 6(5), 899-915.
- Borsa Istanbul. (2024). Bist Insurance.
- Çizgici Akyüz, G. (2022). Hayat Dışı Sigorta Şirketlerinin Finansal Performans Analizinde Topsis ve Mabac Yöntemlerinin Değerlendirilmesi [Evaluation of Topsis and Mabac Methods in Financial Performance Analysis of Non-Life Insurance Companies]. Izmir Journal of Economics, 37(4), 891-912. (In Turkish).
- Demir, G., & Arslan, R. (2021). Analysis of the Performance of Non-Life Insurance Companies in Turkey with the LBWA-PIV MCDM Model. In G. Ibrahimova (Eds.), Proceedings of the 3rd International Baku Scientific Research Congress (pp. 419-435). Baku, Azerbaijan. (In Turkish).
- Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining objective weights in multiple criteria problems: The critic method. Computers & Operations Research, 22(7), 763-770.
- Duc, T., & Ngoc, T. (2023). Combination of DOE and PIV methods for multi-criteria decision making. Journal of Applied Engineering Science, 21(2), 361-373.
- Erdoğan, B. (2022). Evaluation of Financial Performance of Banks Registered on BIST with AHP-SD based PIV Method. PAUSBED, 52, 93-109. (In Turkish).
- Erdoğan, B. (2023). Assessment of the Performance of Insurance Companies through the CRITIC-MAIRCA Model: A Research on the Turkish Insurance Sector. Afyon Kocatepe University Journal of Social Sciences, 25(4), 1438-1455. (In Turkish).
- Ersoy, N. (2021). Application of the PIV Method in the Presence of Negative Data: an Empirical Example from a Real-World Case. Hitit Journal of Social Sciences, 14(2), 318-337.
- Ersoy, N., & Taslak, S. (2023). Comparative Analysis of MCDM Methods for the Assessment of Corporate Sustainability Performance in Energy Sector. Ege Academic Review, 23(3), 341-362.
- Gökdemir, T., & Emel, G. (2023). Performance Analysis of Bist Insurance Companies with Critic Based Promethee II Method. Dicle University Journal of Economics and Administrative Sciences, 13(26), 598-625. (In Turkish).
- Goswami, S. S., Mohanty, S. K., & Behera, D. K. (2022). Selection of a green renewable energy source in India with the help of MEREC integrated PIV MCDM tool. Materials Today: Proceedings, 52, 1153-1160.
- Gülcemal, T., İzci, A. Ç., & Taşcı, M. Z. (2023). BİST 100’de İşlem Gören Sigorta Şirketlerinin CRITICCOCOSO Yöntemiyle Performans Analizi [Performance Analysis of Insurance Companies Traded On BIST 100 By Critic-Cocoso Method]. The Journal of Accounting and Finance, (97), 63-78. (In Turkish).
- Hwang, C. L., & Yoon, K. (1981). Introduction. In Multiple Attribute Decision Making. Lecture Notes in Economics and Mathematical Systems (vol. 186). Springer, Berlin, Heidelberg.
- Kahreman, Y., & Kutlu, M. (2023). Evaluation of Countries’ Sustainable Development Performances Using Hybrid LOPCOW-PIV Techniques. Journal of Management and Economics Research, 21(3), 333-344.
- Kaklauskas, A., Zavadskas, E. K., Banaitis, A., & Šatkauskas, G. (2010). Defining the utility and market value of a real estate: A multiple criteria approach. International Journal of Strategic Property Management, 11(2), 107-120.
- Keleş, M. K., & Alaca, D. (2023). Analysis of Digital Marketing Technologies with PIV and CODAS Methods. Journal of Applied Sciences of Mehmet Akif Ersoy University, 7(1), 84-101. (In Turkish).
- Khoiry, I., & Amelia, D. (2023). Exploring Simple Addictive Weighting (SAW) for Decision-Making. INOVTEK Polbeng – Seri Informatika, 8(2), 281-290.
- Kildienė, S., Kaklauskas, A., & Zavadskas, E. K. (2011). COPRAS based Comparative Analysis of the European Country Management Capabilities within the Construction Sector in the Time of Crisis. Journal of Business Economics and Management, 12(2), 417-434.
- Koca, G., & Bingöl, M. S. (2022). Evaluation of the Performances of Non-Life Insurance Companies with the CRITIC Based MARCOS Method. Bilecik Şeyh Edebali University – Journal of Social Science, 7(1), 70-83. (In Turkish).
- Mardani, A., Jusoh, A., & Zavadskas, E. K. (2015). Fuzzy multiple criteria decision-making techniques and applications – Two decades review from 1994 to 2014. Expert Systems with Applications, 42(8), 4126-4148.
- Mufazzal, S., & Muzakkir, S. M. (2018). A new multi-criterion decision making (MCDM) method based on proximity indexed value for minimizing rank reversals. Computers & Industrial Engineering, 119, 427-438.
- Mufazzal, S., Khan, N. Z., Muzakkir, S. M., Siddiquee, A. N., & Khan, Z. A. (2022). A new fuzzy multi-criteria decision-making method based on proximity index value. Journal of Industrial and Production Engineering, 39(1), 42-58.
- Opricovic, S. (1998). Multicriteria optimization of Civil Engineering systems. Belgrade: University of Belgrad.
- Opricovic, S., & Tzeng, G.-H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445-455.
- Papathanasiou, J., & Ploskas, N. (2018). VIKOR. In Papathanasiou J. & Ploskas N. (Eds.), Multiple criteria decision aid. Methods, examples and Python implementations (pp. 31-55). Springer.
- Ray Sigorta. (2023). Home Page.
- Schober, P., Boer, C., & Schwarte, L. A. (2018). Correlation Coefficients: Appropriate Use and Interpretation. Anesthesia & Analgesia, 126(5), 1763-1768.
- Taherdoost, H. (2023). Analysis of Simple Additive Weighting Method (SAW) as a Multi-Attribute Decision Making Technique: A Step-by-Step Guide. Journal of Management Science & Engineering Research, 6(1), 21-24.
- Taşcı, M. Z. (2022). Analysis of Financial Performance in Insurance Industry with Multi-Criteria Decision Making Techniques. [Unpublished Doctoral Thesis]. Marmara University.
- Türkiye Sigorta. (2023). Home Page.
- Yu, Y., Wu, S., Yu, J., Chen, H., Zeng, Q., Xu, Y., & Ding, H. (2022). An integrated MCDM framework based on interval 2-tuple linguistic: A case of offshore wind farm site selection in China. Process Safety and Environmental Protection, 164, 613-628.
- Yurttadur, M., & Taşcı, M. Z. (2022). Financial Performance Analysis of Participation Banks by PIV Method. Research of Financial Economic and Social Studies, 7(4), 816-827. (In Turkish).
- Zavadskas, E. K., & Kaklauskas, A. (1996). Multiple criteria evaluation of buildings. Vilnius: Technika.
- Zavadskas, E. K., & Turskis, Z. (2010). A new additive ratio assessment (ARAS) method in multicriteria decision-making. Ukio Technologinis ir Ekonominis Vystymas, 16(2), 159-172.
- Zavadskas, E. K., Kaklauskas, A., Turskis, Z., & Tamošaitienė, J. (2009). Multi-Attribute Decision-Making Model by Applying Grey Numbers. Informatica, 20(2), 305-320.
- Zavadskas, E. K., Turskis, Z., & Vilutiene, T. (2010). Multiple criteria analysis of foundation installment alternatives by applying Additive Ratio Assessment (ARAS) method. Archives of Civil and Mechanical Engineering, 10(3), 123-141.
- Zhang, X., Wang, C., Li, E., & Xu, C. (2014). Assessment model of ecoenvironmental vulnerability based on improved entropy weight method. The Scientific World Journal, 2014(1), 797814.
- Zionts, S. (1979). MCDM – If Not a Roman Numeral, Then What? Interfaces, 9(4), 94-101.