Araştırma Makalesi
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ARALIK TİP-2 BULANIK RANCOM VE ARALIK TİP-2 BULANIK CoCoSo TABANLI ÇOK KRİTERLİ BİR YAKLAŞIMLA YATIRIM ARACI SEÇİMİ

Yıl 2026, Cilt: 29 Sayı: 1, 344 - 369, 03.03.2026

Müslüm Öztürk

https://doi.org/10.17780/ksujes.1834494
https://izlik.org/JA96ED58DE

Öz

Bu çalışmada, yatırım aracı seçiminde karar vericilerin çok kriterli tercihlerini değerlendirmek amacıyla Aralık Tip-2 Bulanık RANCOM-CoCoSo (AT2 B-RANCOM-AT2 B-CoCoSo) hibrit yöntemi kullanılmıştır. Çalışmada AT2 B-RANCOM ile kriter ağırlıkları belirlenmiş, ardından AT2 B-CoCoSo kullanılarak altı yatırım aracının performansı değerlendirilip sıralanmıştır. Çalışmada dört uzman, altı kriter ve altı alternatif üzerinde değerlendirmeler gerçekleştirilmiştir. Elde edilen bulgulara göre, kriter ağırlıklarında Getiri Oranı (%21) ve Enflasyona Karşı Koruma (%19) öncelikli olarak belirlenmiş, diğer kriterler ise orta ve düşük öncelikte kalmıştır. Alternatiflerin performans sıralaması ise A3 > A6 > A2 > A4 > A1 > A5 şeklinde gerçekleşmiş ve bu sonuçlar yatırımcıların risk ve getiri önceliklerini yansıtmıştır. Ayrıca, Tip-1 ve Tip-2 yaklaşımları arasında yapılan istatistiksel karşılaştırma, Tip-2 yönteminin alternatifler arasındaki küçük farkları daha hassas bir şekilde ortaya koyabildiğini göstermiştir. 〖ΔSF〗_i değerleri üzerinden yapılan analizde ortalama 0.019 ve standart sapma 0.032 bulunmuştur. Bu sonuç, Tip-2 yönteminin sıralamada ince farklılıkları daha iyi yansıttığını göstermiştir. Çalışmada, kriter ağırlıkları ile alternatif performansları arasındaki ilişki vurgulanmış ve AT2 B-RANCOM + AT2 B-CoCoSo hibrit yaklaşımının yatırım aracı seçiminde güvenilir ve esnek bir yöntem olduğu gösterilmiştir. Sonuçlar, yatırım stratejilerinin optimize edilmesi ve portföy yönetiminde destek süreçlerine katkı sağlayacaktır.

Anahtar Kelimeler

Tip-2 bulanık kümeler RANCOM yöntemi CoCoSo yöntemi yatırım araçları hibrit ÇKKV

Etik Beyan

Bu çalışmada, “Yükseköğretim Kurumları Bilimsel Araştırma ve Yayın Etiği Yönergesi” kapsamında uyulması gerekli tüm kurallara uyulduğunu, bahsi geçen yönergenin “Bilimsel Araştırma ve Yayın Etiğine Aykırı Eylemler” başlığı altında belirtilen eylemlerden hiçbirinin gerçekleştirilmediğini taahhüt ederiz.

Kaynakça

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  • Alballa, T., Rahim, M., Alburaikan, A., Almutairi, A., & Khalifa, H. A. E. W. (2024). MCGDM approach based on (p, q, r)-spherical fuzzy Frank aggregation operators: applications in the categorization of renewable energy sources. Scientific Reports, 14(1), 23576. https://doi.org/10.1038/s41598-024-74591-2
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  • Baydaş, M., Yılmaz, M., Jović, Ž., Stević, Ž., Özuyar, S. E. G., & Özçil, A. (2024). A comprehensive MCDM assessment for economic data: success analysis of maximum normalization, CODAS, and fuzzy approaches. Financial Innovation, 10(1), 105. https://doi.org/10.1186/s40854-023-00588-x
  • Boonjing, V., & Boongasame, L. (2017). Combinatorial portfolio selection with the ELECTRE III method: case study of the stock exchange of Thailand. Afro-Asian Journal of Finance and Accounting, 7(4), 351-362. https://doi.org/10.1504/AAJFA.2017.087506
  • Candan, G., & Toklu, M. C. (2017). Aralık Tip 2 bulanık TOPSIS yöntemi ile yatırım yeri karar analizi. Ekonometri ve Istatistik Dergisi, (27), 16-28.
  • Castillo, O., & Melin, P. (2014). A review on interval type-2 fuzzy logic applications in intelligent control. Information Sciences, 279, 615-631. https://doi.org/10.1016/j.ins.2014.04.015
  • Çilek, A. (2022). Bütünleşik SV-CoCoSo teknikleriyle etkinlik analizi: mevduat bankaları gruplarında bir uygulama. Karadeniz Sosyal Bilimler Dergisi, 14(26), 52-69. https://doi.org/10.38155/ksbd.1079357
  • De, A. K., Chakraborty, D., & Biswas, A. (2022). Literature review on type-2 fuzzy set theory. Soft Computing, 26(18), 9049-9068. https://doi.org/10.1007/s00500-022-07304-4
  • Durmaz, M., & Çermik, Ö. (2022). Çok kriterli karar verme teknikleri kullanılarak yapısal bir uygulama için kompozit malzeme önceliklendirilmesi. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 25(Özel Sayı), 80-97. https://doi.org/10.17780/ksujes.1164490
  • Emamat, M. S. M. M., Mota, C. M. D. M., Mehregan, M. R., Sadeghi Moghadam, M. R., & Nemery, P. (2022). Using ELECTRE-TRI and FlowSort methods in a stock portfolio selection context. Financial Innovation, 8(1), 11. https://doi.org/10.1186/s40854-021-00318-1
  • Ersoy, N. (2023). Applying an integrated data-driven weighting system-CoCoSo approach for financial performance evaluation of Fortune 500 companies. E+ M Ekonomie a Management, 26(3), 92-108. https://doi.org/10.15240/tul/001/2023-3-006
  • Gupta, N., Garg, P., & Ahuja, N. (2025). An integrated pythagorean fuzzy delphi-AHP-CoCoSo approach for exploring barriers and mitigation strategies for sustainable supply chain in the food industry. Supply Chain Analytics, 10, 100105. https://doi.org/10.1016/j.sca.2025.100105
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INVESTMENT INSTRUMENT SELECTION USING A MULTI-CRITERIA APPROACH BASED ON INTERVAL TYPE-2 FUZZY RANCOM AND INTERVAL TYPE-2 FUZZY CoCoSo

Yıl 2026, Cilt: 29 Sayı: 1, 344 - 369, 03.03.2026

Müslüm Öztürk

https://doi.org/10.17780/ksujes.1834494
https://izlik.org/JA96ED58DE

Öz

This study employs an Interval Type-2 Fuzzy RANCOM–CoCoSo (IT2 F-RANCOM–IT2 F-CoCoSo) hybrid method to evaluate decision-makers’ multi-criteria preferences. Criterion weights were determined using IT2 F-RANCOM, and the performance of six investment instruments was ranked using IT2 F-CoCoSo. Four experts assessed six criteria and six alternatives. The findings indicated that Return Rate (21%) and Inflation Protection (19%) were the most prioritized criteria, while the remaining criteria held medium and low importance. The performance ranking of the alternatives was A3 > A6 > A2 > A4 > A1 > A5, reflecting investors’ risk and return preferences. Furthermore, a statistical comparison between Type-1 and Type-2 approaches demonstrated that the Type-2 method captured small differences among alternatives more precisely; the 〖ΔSF〗_i values had a mean of 0.019 and a standard deviation of 0.032, supporting the superior sensitivity of Type-2 in reflecting nuanced rankings. The study highlights the relationship between criterion weights and alternative performance and shows that the AT2 F-RANCOM + AT2 F-CoCoSo hybrid approach is a reliable and flexible method for investment instrument selection. The results contribute to optimizing investment strategies and enhancing decision-support in portfolio management

Anahtar Kelimeler

Type-2 fuzzy sets RANCOM method CoCoSo method investment instruments hybrid MCDM

Etik Beyan

We hereby declare that all rules stipulated in the “Higher Education Institutions Scientific Research and Publication Ethics Directive” have been fully observed in this study, and that none of the actions listed under the section titled “Acts Contrary to Scientific Research and Publication Ethics” have been engaged in.

Kaynakça

  • Akbulut, O. Y., & Hepşen, A. (2021). Finansal performans ve pay senedi getirileri arasındaki ilişkinin Entropi ve CoCoSo ÇKKV teknikleriyle analiz edilmesi. Ekonomi Politika ve Finans Araştırmaları Dergisi, 6(3), 681-709. https://doi.org/10.30784/epfad.945770
  • Alballa, T., Rahim, M., Alburaikan, A., Almutairi, A., & Khalifa, H. A. E. W. (2024). MCGDM approach based on (p, q, r)-spherical fuzzy Frank aggregation operators: applications in the categorization of renewable energy sources. Scientific Reports, 14(1), 23576. https://doi.org/10.1038/s41598-024-74591-2
  • Bahmani, N., Yamoah, D., Basseer, P., & Rezvani, F. (1987). Using the analytic hierarchy process to select investment in a heterogenous environment. Mathematical Modelling, 8, 157-162. https://doi.org/10.1016/0270-0255(87)90561-6
  • Bakar, A. S. A., Khalif, K. M. N. K., & Gegov, A. (2015, November). Ranking of interval type-2 fuzzy numbers based on centroid point and spread. In 2015 7th International Joint Conference on Computational Intelligence (IJCCI) (Vol. 2, pp. 131-140). IEEE.
  • Baydaş, M., Yılmaz, M., Jović, Ž., Stević, Ž., Özuyar, S. E. G., & Özçil, A. (2024). A comprehensive MCDM assessment for economic data: success analysis of maximum normalization, CODAS, and fuzzy approaches. Financial Innovation, 10(1), 105. https://doi.org/10.1186/s40854-023-00588-x
  • Boonjing, V., & Boongasame, L. (2017). Combinatorial portfolio selection with the ELECTRE III method: case study of the stock exchange of Thailand. Afro-Asian Journal of Finance and Accounting, 7(4), 351-362. https://doi.org/10.1504/AAJFA.2017.087506
  • Candan, G., & Toklu, M. C. (2017). Aralık Tip 2 bulanık TOPSIS yöntemi ile yatırım yeri karar analizi. Ekonometri ve Istatistik Dergisi, (27), 16-28.
  • Castillo, O., & Melin, P. (2014). A review on interval type-2 fuzzy logic applications in intelligent control. Information Sciences, 279, 615-631. https://doi.org/10.1016/j.ins.2014.04.015
  • Çilek, A. (2022). Bütünleşik SV-CoCoSo teknikleriyle etkinlik analizi: mevduat bankaları gruplarında bir uygulama. Karadeniz Sosyal Bilimler Dergisi, 14(26), 52-69. https://doi.org/10.38155/ksbd.1079357
  • De, A. K., Chakraborty, D., & Biswas, A. (2022). Literature review on type-2 fuzzy set theory. Soft Computing, 26(18), 9049-9068. https://doi.org/10.1007/s00500-022-07304-4
  • Durmaz, M., & Çermik, Ö. (2022). Çok kriterli karar verme teknikleri kullanılarak yapısal bir uygulama için kompozit malzeme önceliklendirilmesi. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 25(Özel Sayı), 80-97. https://doi.org/10.17780/ksujes.1164490
  • Emamat, M. S. M. M., Mota, C. M. D. M., Mehregan, M. R., Sadeghi Moghadam, M. R., & Nemery, P. (2022). Using ELECTRE-TRI and FlowSort methods in a stock portfolio selection context. Financial Innovation, 8(1), 11. https://doi.org/10.1186/s40854-021-00318-1
  • Ersoy, N. (2023). Applying an integrated data-driven weighting system-CoCoSo approach for financial performance evaluation of Fortune 500 companies. E+ M Ekonomie a Management, 26(3), 92-108. https://doi.org/10.15240/tul/001/2023-3-006
  • Gupta, N., Garg, P., & Ahuja, N. (2025). An integrated pythagorean fuzzy delphi-AHP-CoCoSo approach for exploring barriers and mitigation strategies for sustainable supply chain in the food industry. Supply Chain Analytics, 10, 100105. https://doi.org/10.1016/j.sca.2025.100105
  • Jankova, Z., & Dostal, P. (2022). Uncertaınty in the type-2 fuzzy logic system for forecasting stock index. Romanian Journal of Economic Forecasting, 25(4), 41
  • Kahraman, C., & Haktanır, E. (2024). Fuzzy Investment Decision Making with Examples (pp. 103-115). Springer. https://doi.org/10.1007/978-3-031-54660-0
  • Kao, C. (2010). Weight determination for consistently ranking alternatives in multiple criteria decision analysis. Applied Mathematical Modelling, 34(7), 1779-1787. https://doi.org/10.1016/j.apm.2009.09.022
  • Kaya, I., Çolak, M., & Terzi, F. (2019). A comprehensive review of fuzzy multi criteria decision making methodologies for energy policy making. Energy strategy reviews, 24, 207-228. https://doi.org/10.1016/j.esr.2019.03.003
  • Khan, K. I., Kabir, M. A., Mata, M. N., Correia, A. B., Rita, J. X., & Martins, J. N. (2021). Portfolio optimization: An application of MOORA model through stochastic process. Academy of Accounting and Financial Studies Journal, 25(S2), 1–14.
  • Kumar, S. (2025). Stock selection with intuitionistic fuzzy combined compromise solutions. Applied Soft Computing, 169, 112526. https://doi.org/10.1016/j.asoc.2024.112526
  • Kumaran, S. (2022). Financial performance index of IPO firms using VIKOR-CRITIC techniques. Finance research letters, 47, 102542. https://doi.org/10.1016/j.frl.2021.102542
  • Kutlu Gündoğdu, F., & Kahraman, C. (2019). Spherical fuzzy sets and spherical fuzzy TOPSIS method. Journal of intelligent & fuzzy systems, 36(1), 337-352. https://doi.org/10.1007/s00500-022-07749-7
  • Lucas, F. F., dos Santos, M., Gomes, C. F. S., de Araújo Costa, A. P., de Oliveira Braga, G., da Costa, L. M. A., ... & de Araújo Costa, V. P. (2024). Valuation of real estate investment trusts using the PSI-CoCoSo multicriteria method. Procedia Computer Science, 242, 881-887. https://doi.org/10.1016/j.procs.2024.08.264
  • Madi, E. N., Zakaria, Z. A., Sambas, A., & Sukono. (2023). Toward effective uncertainty management in decision-making models based on type-2 fuzzy TOPSIS. Mathematics, 11(16), 3512. https://doi.org/10.3390/math11163512
  • Meniz, B., Bas, S. A., Ozkok, B. A., & Tiryaki, F. (2021). Multilevel AHP approach with interval type-2 fuzzy sets to portfolio selection problem. Journal of Intelligent & Fuzzy Systems, 40(5), 8819-8829. https://doi.org/10.3233/JIFS-200512
  • Narang, M., Joshi, M. C., Bisht, K., & Pal, A. (2022). Stock portfolio selection using a new decision-making approach based on the integration of fuzzy CoCoSo with Heronian mean operator. Decision making: applications in management and engineering, 5(1), 90-112. https://doi.org/10.31181/dmame0310022022n
  • Narang, M., Joshi, M. C., Bisht, K., & Pal, A. (2022). Stock portfolio selection using a new decision-making approach based on the integration of fuzzy CoCoSo with Heronian mean operator. Decision making: applications in management and engineering, 5(1), 90-112. https://doi.org/10.31181/dmame0310022022n
  • Özdağoğlu, A., Keleş, M. K., & Eren, F. Y. (2019). Bir üniversite hastanesinde makroelisa ekipmani alternatiflerinin WASPAS ve SWARA yöntemleri ile değerlendirilmesi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 24(2), 319-331.
  • Öztürk, M. (2025). A Hybrid Approach for Battery Selection Based on Green Criteria in Electric Vehicles: DEMATEL-QFD-Interval Type-2 Fuzzy VIKOR. Sustainability, 17(14), 6277. https://doi.org/10.3390/su17146277
  • Öztürk, M., & Paksoy, T. (2016). Otoyollardaki Trafik Işıkları Kontrol Sistemi Modellemesi Bulanık Karar Tabanlı Görsel Uygulaması. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 19(3), 170-183.
  • Öztürk, M., Paksoy, T., & Öztürk, M. (2017). Aralık tip-2 bulanık mantık yönteminin tedarikçi seçiminde kullanımının önemi üzerine bir araştırma. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi, 10(2), 1-18.
  • Öztürk, M., Torğul, B., & Paksoy, T. (2022). Interval Type-2 Fuzzy Rule-Based Bwm Approach for Sustainable Supplier Selection. Konya Journal of Engineering Sciences, 10(2), 312-336.
  • Peng, X., & Huang, H. (2020). Fuzzy decision making method based on CoCoSo with critic for financial risk evaluation. Technological and Economic Development of Economy, 26(4), 695-724. https://doi.org/10.3846/tede.2020.11920
  • Peng, X., Huang, H. H., & Luo, Z. (2023). Fuzzy dynamic MCDM method based on PRSRV for financial risk evaluation of new energy vehicle industry. Applied Soft Computing, 136, 110115. https://doi.org/10.1016/j.asoc.2023.110115
  • Rasoanaivo, R. G., Yazdani, M., Zaraté, P., & Fateh, A. (2024). Combined Compromise for Ideal Solution (CoCoFISo): a multi-criteria decision-making based on the CoCoSo method algorithm. Expert Systems with Applications, 251, 124079. https://doi.org/10.1016/j.eswa.2024.124079
  • Shabani, M., Khodarahmi, A., Ghousi, R., Mohammadi, E., & Ghanbari, H. (2025). An appraisal of fund of funds efficiency based on risk-adjusted performance measures: Application of an augmented WASPAS methodology. PLoS One, 20(7), e0314918. https://doi.org/10.1371/journal.pone.0314918
  • Sharma, S. K. (2025). Enhancing stock portfolio selection with trapezoidal bipolar fuzzy VIKOR technique with Boruta-GA hybrid optimization model: a multicriteria decision-making approach. International Journal of Computational Intelligence Systems, 18(1), 17. https://doi.org/10.1007/s44196-025-00733-7
  • Silva, N. F., dos Santos, M., Gomes, C. F. S., & de Andrade, L. P. (2023). An integrated CRITIC and Grey Relational Analysis approach for investment portfolio selection. Decision analytics journal, 8, 100285. https://doi.org/10.1016/j.dajour.2023.100285
  • State Street Global Advisors. (2025, 5 Mart). How economic factors impact asset performance. State Street. https://www.ssga.com/us/en/intermediary/insights/how-economic-factors-impact-asset-performance
  • Tan, W. W., & Chua, T. W. (2007). Uncertain rule-based fuzzy logic systems: introduction and new directions (Mendel, JM; 2001)[book review]. IEEE Computational intelligence magazine, 2(1), 72-73. https://doi.org/10.1109/MCI.2007.357196
  • Tavana, M., Shaabani, A., Di Caprio, D., & Bonyani, A. (2022). A novel Interval Type-2 Fuzzy best-worst method and combined compromise solution for evaluating eco-friendly packaging alternatives. Expert systems with applications, 200, 117188. https://doi.org/10.1016/j.eswa.2022.117188
  • Vásquez, J. A., Escobar, J. W., & Manotas, D. F. (2021). AHP–TOPSIS methodology for stock portfolio investments. Risks, 10(1), 4. https://doi.org/10.3390/risks10010004
  • Wang, J. Q., Yu, S. M., Wang, J., Chen, Q. H., Zhang, H. Y., & Chen, X. H. (2015). An interval type-2 fuzzy number based approach for multi-criteria group decision-making problems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 23(04), 565-588. https://doi.org/10.1142/S0218488515500257
  • Wang, Y., Hussain, A., Mahmood, T., Ali, M. I., Wu, H., & Jin, Y. (2020). Decision‐Making Based on q‐Rung Orthopair Fuzzy Soft Rough Sets. Mathematical Problems in Engineering, 2020(1), 6671001. https://doi.org/10.1155/2020/6671001
  • Wang, Z., & Rangaiah, G. P. (2025). Multi-Criteria Decision-Making: Reference-Type Methods. arXiv preprint arXiv:2508.16087. https://doi.org/10.48550/arXiv.2508.16087
  • Wu, D., & Mendel, J. M. (2007). Uncertainty measures for interval type-2 fuzzy sets. Information sciences, 177(23), 5378-5393. https://doi.org/10.1016/j.ins.2007.07.012
  • Wu, Q., Liu, X., Qin, J., Zhou, L., Mardani, A., & Deveci, M. (2022). An integrated generalized TODIM model for portfolio selection based on financial performance of firms. Knowledge-Based Systems, 249, 108794. https://doi.org/10.1016/j.knosys.2022.108794
  • Xue, Q., Ling, Y., & Tian, B. (2022). Portfolio Optimization Model for Gold and Bitcoin Based on Weighted Unidirectional Dual‐Layer LSTM Model and SMA‐Slope Strategy. Computational intelligence and neuroscience, 2022(1), 1869897. https://doi.org/10.1155/2022/1869897
  • Yazdani, M., Zarate, P., Kazimieras Zavadskas, E., & Turskis, Z. (2019). A combined compromise solution (CoCoSo) method for multi-criteria decision-making problems. Management decision, 57(9), 2501-2519. https://doi.org/10.1108/MD-05-2017-0458
  • Zadeh, L. A. (2001). A new direction in AI: Toward a computational theory of perceptions. AI magazine, 22(1), 73-73. https://doi.org/10.1609/aimag.v22i1.1545
  • Zhang, L., Feng, J., & Feng, B. (2024). Research on PPP-ABS projects hesitant fuzzy multi-criteria investment decision-making with prospect theory and VIKOR method. Journal of Industrial and Management Optimization, 20(8), 2570-2590. https://doi.org/10.3934/jimo.2024016
  • Zolfaghari, S., Mousavi, S. M., & Antuchevičienė, J. (2021). A type-2 fuzzy optimization model for project portfolio selection and scheduling incorporating project interdependency and splitting. https://etalpykla.vilniustech.lt/handle/123456789/152034
Toplam 52 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bulanık Hesaplama, Çok Ölçütlü Karar Verme
Bölüm Araştırma Makalesi
Yazarlar

Müslüm Öztürk 0000-0003-1941-3115

Gönderilme Tarihi 2 Aralık 2025
Kabul Tarihi 29 Ocak 2026
Yayımlanma Tarihi 3 Mart 2026
DOI https://doi.org/10.17780/ksujes.1834494
IZ https://izlik.org/JA96ED58DE
Yayımlandığı Sayı Yıl 2026 Cilt: 29 Sayı: 1

Kaynak Göster

APA Öztürk, M. (2026). ARALIK TİP-2 BULANIK RANCOM VE ARALIK TİP-2 BULANIK CoCoSo TABANLI ÇOK KRİTERLİ BİR YAKLAŞIMLA YATIRIM ARACI SEÇİMİ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 29(1), 344-369. https://doi.org/10.17780/ksujes.1834494