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An Application On Evaluation Of Fund Selection Criteria In The Individual Retirement System (IRS) With Multi-Criteria Decision-Making Methods

Year 2025, Volume: 40 Issue: 3, 752 - 769, 16.07.2025
https://doi.org/10.24988/ije.1519486

Abstract

Investing involves allocating current financial resources with the expectation of generating future returns. The primary goal of investing is to grow wealth, ensure financial security, and achieve specific financial objectives such as retirement savings. There are various types of investments, including stocks, bonds, mutual funds, and real estate, each with a risk and return profile. Effective investment strategies often involve diversification to mitigate risk and maximize returns. Within retirement planning, the Individual Retirement System (IRS) plays a crucial role. The IRS allows individuals to contribute a portion of their income into retirement accounts, which are then invested in various financial instruments. This system provides a structured approach to retirement savings, offering flexibility and significant tax advantages. The selection of appropriate funds within these accounts is vital to ensuring optimal growth and security of retirement savings. This study aims to evaluate the criteria affecting fund selection in BES using two important multi-criteria decision-making methods: Analytical Hierarchy Process (AHP) and Analytical Network Process (ANP). By applying AHP and ANP, the study defines the effects of basic factors such as risk tolerance, liquidity, fund performance, and diversification on fund selection in BES and evaluates their impact levels. The study aims to increase investment decisions' soundness and reliability and help individuals make informed choices compatible with their financial goals and risk preferences. At the end of the study, the impact levels of the criteria affecting fund selection were calculated, and the results were interpreted.

References

  • Abdel-aziem, A. H., Mohamed, H. K. and Abdelhafeez, A. (2023). Neutrosophic decision making model for investment portfolios selection and optimizing based on wide variety of investment opportunities and many criteria in market. Neutrosophic systems with applications, 6, 32-38.
  • Akinwale, A., Olajubu, E. and Aderounmu, G. (2023). A prey-predator defence mechanism for ad hoc on-demand distance vector routing protocol. Journal of Computing and Information Technology,30(2), 67-83.
  • Antara, G. M. B., Linawati, L. and Wirastuti, N. (2019). Evaluasi infrastruktur jaringan lan opd pemprov bali dengan cobit 5.0 studi kasus: pemprov bali. Jurnal Teknologi Informasi dan Ilmu Komputer,6(1).
  • Belousova, E. P. and Bulgakova, I. (2023). Inventory management in supply chains based on a linear discrete system with a quadratic quality criterion. Science and Education of the Bauman MSTU, (26), 3, 47-62.
  • Bingley, P., Datta Gupta, N. and Pedersen, P. J. (2001). The effects of pension program incentives on retirement behavior in Denmark. CLS Working Paper No. 01-08.
  • Budiono, D. ve Martens, M. (2010). Mutual funds selection based on funds characteristics. Journal of Financial Research, 33(3), 313-330.
  • Chengar, O., Shevchenko, V., Maschenko, E., Moiseev, D. and Soina, A. S. (2020). Strategy for primary processing of social networks data using hierarchy analysis method. Journal of Physics: Conference Series, 1679(2), 022082.
  • Coetzee, N. (2013). Choosing your retirement income: Retirement planning. Personal Finance, 2013(390), 4-5.
  • Consigli, G., Iaquinta, G., Moriggia, V., Tria, M. and Musitelli, D. (2012). Retirement planning in individual asset-liability management. International Journal of Management,23(4), 365-396.
  • Damigos, G., Lindgren, T., Sandberg, S., & Nikolakopoulos, G. (2023). Performance of sensor data process offloading on 5G-Enabled UAVs. Sensors, 23(2), 864.
  • Dyumaeva, I. V. and Kurochkin, A. V. (2023). Application of the analytic hierarchy process in selecting laboratory ınformation management systems. Analytical Review, (6), 462-466.
  • Ferreira, R., Almeida Filho, A. ve Souza, F. D. (2009). A decision model for portfolio selection. Production, 19(2), 340-355.
  • Fiala, P. and Borovička, A. (2015). Investment decision-making by a two-step multi-criteria procedure. Technology for Business and Economics.
  • Gale, W. ve John, D. C. (2018). State-sponsored retirement savings plans: New Approaches to Boost Retirement Plan Coverage. In How Persistent Low Returns Will Shape Saving and Retirement, 201-215. Oxford University Press.
  • Guananga, G. P. T., Leon, J. C. R., Falconi, A. F. I., Salazar, Á. G. C. And Sanipatin, E. L. R. (2019). La gestión por procesos un sistema de control eficiente en las empresas. Ciencia digital, 3(2.6), 495-514.
  • Ivanovych Kuznietsov, V., Yevtushenko, H. and Andriukhina, M. V. (2019). Solving system problems of a complex structure using multi-criteria analysis methods in the DSS NooTron. Science and Technology Journal, 3(122), 140-152.
  • Kantarcı, T. and van Soest, A. (2013). Stated preference analysis of full and partial retirement in the United States. CESR-Schaeffer Working Paper, (2013-011).
  • Kuznietsov, V. I., Yevtushenko, H. and Andriukhina, M. V. (2019). Solving system problems of a complex structure using multi-criteria analysis methods in the DSS NooTron. Системні технології,3(122), 140-152.
  • Liu, Q. (2022). Identifying and Correcting the Defects of the Saaty Analytic Hierarchy/Network Process: A Comparative Study of the Saaty Analytic Hierarchy/Network Process and the Markov Chain-based Analytic Network Process. Operations Research Perspectives, 9, 100244.
  • Miranda-Pinto, J. (2012). Does personalized pension projection affect the retirement decision? Superintendencia de Pensiones, Working Paper No.53.
  • Narayanamoorthy, V. and Aravanan, S. (2003). Multiplier approach - as a tool for investment decision. The Management Accountant, 39( 3), 208- 210.
  • Olson, R. and Phillips, D. (2015). Let's save retirement. Social Science Research Network, Paper No. 2450471.
  • Pavlov, A. and Kyselov, M. (2023). Mathematical models and methods of coordinated planning. Management of Development of Complex Systems, (2), 1-10.
  • Palanisamy, B. and Selvam, M. (2011). Stock Selection Ability of Indian Mutual Fund Managers Under Conditional Models. Social Science Research Network, Paper No. 1875885.
  • Roon, F., Guo, J. and ter Horst, J. (2010). A Random Walk by Fund of Funds Managers? Social Science Research Network, Paper No. 1571673.
  • Romadona, D. (2019). Perancangan Sistem Pendukung Keputusan Pemilihan Kepala Sekolah Terbaik Tingkat Kabupaten Labuahan Batu Utara Menggunakan Metode Analytic Hierarchy Process (Ahp)(Studi Kasus Dinas Pendidikan Aek Natas). Informasi dan Teknologi Ilmiah (INTI), 7(1), 29-31.
  • Saaty, T. L. (1980). The analytic hierarchy process: planning, priority setting, resource allocation. New York: McGraw-Hill.
  • Saaty, T. L. (1996). Decisions with the analytic network process (ANP). University of Pittsburgh (USA), ISAHP, 96.
  • Sánchez, M. A., Milanesi, G. S. and Rivitti, M. B. (2010). Evaluación de alternativas de inversión utilizando el proceso jerárquico analítico. Escritos Contables y de Administración, 1(2), 21-34.
  • Saraoglu, H. and Detzler, M. (2002). A sensible mutual fund selection model. Financial Analysts Journal, 58(3), 60-72.
  • Söylemez, E. Y., Kayabaşı, A. and Doğan, S. (2021). Seramik sektörü tedarikçi seçim sürecinde gri ilişkisel analiz (GİA) ile entegre edilmiş analitik hiyerarşi prosesi (AHP) modeli. İzmir İktisat Dergisi, 36(3), 497-516.
  • Shvedov, A. V., Gadasin, D. V., Klygina, O. G. and Tremasova, L. A. (2023, March). Optimization of Network Routing Using the Markov Decision Process and Hamiltonian Cycle. In 2023 Systems of Signals Generating and Processing in the Field of on Board Communications (pp. 1-4). IEEE.
  • Sivapriya, N., Mohandas, R., Kumar, P. K., & Vaigandla, K. K. (2023). Dissection of Mobility Model Routing Protocols in MANET on QoS Criterion. International Journal of Research in Information Technology and Computing.
  • Sheikh-Mohamed, Y. B., Jakobsen, S. H., Bødal, E. F., Haugseth, F. M., Kiel, E. S. and Riemer-SØrensen, S. (2023, June). Graph Convolutional Networks for probabilistic power system operational planning. In 2023 IEEE Belgrade PowerTech (pp. 1-6). IEEE.
  • Şahin, O., & Başarır, Ç. (2019). Bireysel emeklilik şirketlerinin finansal performanslarının değerlendirilmesi: Türkiye örneği. Yönetim Bilimleri Dergisi, 17(33), 211-229.
  • Tan, M., Yazıcı, E. and Alakaş, H. M. (2023). Active Wheelchair Selection for the Physically Disabled with Analytical Network Process, TOPSIS, and PROMETHEE Methods. International Journal of Pure and Applied Sciences, 9(1), 76-87.
  • Trihapningsari, D., Agushinta, R. D. and Banowosari, L. Y. (2021). Pengukuran Kapabilitas Tata Kelola TI Sistem Informasi Tiras dan Transaksi Bahan Ajar Universitas Terbuka Menggunakan Cobit 5. Jurnal Teknologi Informasi dan Ilmu Komputer,8(5),965-976.
  • Van der Aalst, W. M. P. (2022). European leadership in process management. ACM Transactions on Management Information Systems,65(4),80-83.
  • Wang, Z., Liu, L., Dong, X. and Liu, J. (2023). Evaluation of neural network models and quality forecasting based on process time-series data. Canadian Journal of Chemical Engineering. 102(4), 1522-1537.
  • Cheng, W., Wang, J. Y. and Ma, J. T. (2008). Optimal investment decision of security investment fund based on the experiment design. In 2008 International Conference on Risk Management & Engineering Management (pp. 527-530). IEEE.Wilton, M. (1974). What you should know about pensions. Managerial Finance, 1(1), 56-67.
  • Wu, X., Lin, Y. and Lin, T. (2023). Collaborative Production Task Allocation Decision for Multi Smart Factory. Proceedings of the IEEE International Conference on Data Science and Advanced Analytics (DSAA), 1-6.
  • Zakharov, A., Vakhitov, G. and Enikeeva, Z. (2019). Neural Network Architecture Development for Time Series Forecasting. Journal of Applied Computer Science & Mathematics, 13(6), 5615-5620.
  • Zyubin, V., Ivanishkin, D. S. and Anureev, I. (2023). Towards Process-Oriented Programming Distributed Control Systems. Proceedings of the IEEE International Conference on Data Science and Advanced Analytics (DSAA), 1-6.

Çok Kriterli Karar Verme Yöntemleri İle Bireysel Emeklilik Sisteminde (BES) Fon Seçim Kriterlerinin Değerlendirilmesi Üzerine Bir Uygulama

Year 2025, Volume: 40 Issue: 3, 752 - 769, 16.07.2025
https://doi.org/10.24988/ije.1519486

Abstract

Yatırım, mevcut finansal kaynakların gelecekte getiri elde etme beklentisiyle tahsis edilmesini anlamına gelir. Yatırımın temel amacı serveti artırmak, finansal güvenliği sağlamak ve emeklilik tasarrufları gibi belirli finansal hedeflere ulaşmaktır. Hisse senetleri, tahviller, yatırım fonları ve gayrimenkul dahil olmak üzere her birinin kendi risk ve getiri profili olan çeşitli yatırım türleri vardır. Etkili yatırım stratejileri genellikle riski azaltmak ve getirileri en üst düzeye çıkarmak için çeşitlendirmeyi içerir. Emeklilik planlaması alanında Bireysel Emeklilik Sistemi (BES) önemli bir rol oynamaktadır. BES, bireylerin gelirlerinin bir kısmını emeklilik hesaplarına yatırmalarına ve daha sonra bu hesapları çeşitli finansal araçlara yatırmalarına olanak tanır. Bu sistem emeklilik tasarruflarına yapısal bir yaklaşım sunarak esneklik ve önemli vergi avantajları sunar. Bu hesaplardaki uygun fonların seçimi, optimal büyümenin ve emeklilik tasarruflarının güvenliğinin sağlanması açısından hayati öneme sahiptir. Bu çalışma, BES'de fon seçimini etkileyen kriterleri iki önemli çok kriterli karar verme yöntemini kullanarak değerlendirmeyi amaçlamaktadır: Analitik Hiyerarşi Süreci (AHP) ve Analitik Ağ Süreci (ANP). Çalışmada, AHP ve ANP'yi uygulayarak, BES'de fon seçimini etkileyen risk toleransı, fon performansı, likidite ve çeşitlilik gibi temel faktörlerin fon seçimi üzerindeki etkileri tanımlamakta ve etki düzeyleri değerlendirmektedir. Çalışmada amaç ise, yatırım kararlarının sağlamlığını ve güvenilirliğini artırmak, bireylerin finansal hedefleri ve risk tercihleriyle uyumlu bilinçli seçimler yapmalarına yardımcı olmaktır. Çalışma sonunda fon seçimini etkileyen kriterlerin etki düzeyleri hesaplanmış ve sonuçlar yorumlanmıştır.

References

  • Abdel-aziem, A. H., Mohamed, H. K. and Abdelhafeez, A. (2023). Neutrosophic decision making model for investment portfolios selection and optimizing based on wide variety of investment opportunities and many criteria in market. Neutrosophic systems with applications, 6, 32-38.
  • Akinwale, A., Olajubu, E. and Aderounmu, G. (2023). A prey-predator defence mechanism for ad hoc on-demand distance vector routing protocol. Journal of Computing and Information Technology,30(2), 67-83.
  • Antara, G. M. B., Linawati, L. and Wirastuti, N. (2019). Evaluasi infrastruktur jaringan lan opd pemprov bali dengan cobit 5.0 studi kasus: pemprov bali. Jurnal Teknologi Informasi dan Ilmu Komputer,6(1).
  • Belousova, E. P. and Bulgakova, I. (2023). Inventory management in supply chains based on a linear discrete system with a quadratic quality criterion. Science and Education of the Bauman MSTU, (26), 3, 47-62.
  • Bingley, P., Datta Gupta, N. and Pedersen, P. J. (2001). The effects of pension program incentives on retirement behavior in Denmark. CLS Working Paper No. 01-08.
  • Budiono, D. ve Martens, M. (2010). Mutual funds selection based on funds characteristics. Journal of Financial Research, 33(3), 313-330.
  • Chengar, O., Shevchenko, V., Maschenko, E., Moiseev, D. and Soina, A. S. (2020). Strategy for primary processing of social networks data using hierarchy analysis method. Journal of Physics: Conference Series, 1679(2), 022082.
  • Coetzee, N. (2013). Choosing your retirement income: Retirement planning. Personal Finance, 2013(390), 4-5.
  • Consigli, G., Iaquinta, G., Moriggia, V., Tria, M. and Musitelli, D. (2012). Retirement planning in individual asset-liability management. International Journal of Management,23(4), 365-396.
  • Damigos, G., Lindgren, T., Sandberg, S., & Nikolakopoulos, G. (2023). Performance of sensor data process offloading on 5G-Enabled UAVs. Sensors, 23(2), 864.
  • Dyumaeva, I. V. and Kurochkin, A. V. (2023). Application of the analytic hierarchy process in selecting laboratory ınformation management systems. Analytical Review, (6), 462-466.
  • Ferreira, R., Almeida Filho, A. ve Souza, F. D. (2009). A decision model for portfolio selection. Production, 19(2), 340-355.
  • Fiala, P. and Borovička, A. (2015). Investment decision-making by a two-step multi-criteria procedure. Technology for Business and Economics.
  • Gale, W. ve John, D. C. (2018). State-sponsored retirement savings plans: New Approaches to Boost Retirement Plan Coverage. In How Persistent Low Returns Will Shape Saving and Retirement, 201-215. Oxford University Press.
  • Guananga, G. P. T., Leon, J. C. R., Falconi, A. F. I., Salazar, Á. G. C. And Sanipatin, E. L. R. (2019). La gestión por procesos un sistema de control eficiente en las empresas. Ciencia digital, 3(2.6), 495-514.
  • Ivanovych Kuznietsov, V., Yevtushenko, H. and Andriukhina, M. V. (2019). Solving system problems of a complex structure using multi-criteria analysis methods in the DSS NooTron. Science and Technology Journal, 3(122), 140-152.
  • Kantarcı, T. and van Soest, A. (2013). Stated preference analysis of full and partial retirement in the United States. CESR-Schaeffer Working Paper, (2013-011).
  • Kuznietsov, V. I., Yevtushenko, H. and Andriukhina, M. V. (2019). Solving system problems of a complex structure using multi-criteria analysis methods in the DSS NooTron. Системні технології,3(122), 140-152.
  • Liu, Q. (2022). Identifying and Correcting the Defects of the Saaty Analytic Hierarchy/Network Process: A Comparative Study of the Saaty Analytic Hierarchy/Network Process and the Markov Chain-based Analytic Network Process. Operations Research Perspectives, 9, 100244.
  • Miranda-Pinto, J. (2012). Does personalized pension projection affect the retirement decision? Superintendencia de Pensiones, Working Paper No.53.
  • Narayanamoorthy, V. and Aravanan, S. (2003). Multiplier approach - as a tool for investment decision. The Management Accountant, 39( 3), 208- 210.
  • Olson, R. and Phillips, D. (2015). Let's save retirement. Social Science Research Network, Paper No. 2450471.
  • Pavlov, A. and Kyselov, M. (2023). Mathematical models and methods of coordinated planning. Management of Development of Complex Systems, (2), 1-10.
  • Palanisamy, B. and Selvam, M. (2011). Stock Selection Ability of Indian Mutual Fund Managers Under Conditional Models. Social Science Research Network, Paper No. 1875885.
  • Roon, F., Guo, J. and ter Horst, J. (2010). A Random Walk by Fund of Funds Managers? Social Science Research Network, Paper No. 1571673.
  • Romadona, D. (2019). Perancangan Sistem Pendukung Keputusan Pemilihan Kepala Sekolah Terbaik Tingkat Kabupaten Labuahan Batu Utara Menggunakan Metode Analytic Hierarchy Process (Ahp)(Studi Kasus Dinas Pendidikan Aek Natas). Informasi dan Teknologi Ilmiah (INTI), 7(1), 29-31.
  • Saaty, T. L. (1980). The analytic hierarchy process: planning, priority setting, resource allocation. New York: McGraw-Hill.
  • Saaty, T. L. (1996). Decisions with the analytic network process (ANP). University of Pittsburgh (USA), ISAHP, 96.
  • Sánchez, M. A., Milanesi, G. S. and Rivitti, M. B. (2010). Evaluación de alternativas de inversión utilizando el proceso jerárquico analítico. Escritos Contables y de Administración, 1(2), 21-34.
  • Saraoglu, H. and Detzler, M. (2002). A sensible mutual fund selection model. Financial Analysts Journal, 58(3), 60-72.
  • Söylemez, E. Y., Kayabaşı, A. and Doğan, S. (2021). Seramik sektörü tedarikçi seçim sürecinde gri ilişkisel analiz (GİA) ile entegre edilmiş analitik hiyerarşi prosesi (AHP) modeli. İzmir İktisat Dergisi, 36(3), 497-516.
  • Shvedov, A. V., Gadasin, D. V., Klygina, O. G. and Tremasova, L. A. (2023, March). Optimization of Network Routing Using the Markov Decision Process and Hamiltonian Cycle. In 2023 Systems of Signals Generating and Processing in the Field of on Board Communications (pp. 1-4). IEEE.
  • Sivapriya, N., Mohandas, R., Kumar, P. K., & Vaigandla, K. K. (2023). Dissection of Mobility Model Routing Protocols in MANET on QoS Criterion. International Journal of Research in Information Technology and Computing.
  • Sheikh-Mohamed, Y. B., Jakobsen, S. H., Bødal, E. F., Haugseth, F. M., Kiel, E. S. and Riemer-SØrensen, S. (2023, June). Graph Convolutional Networks for probabilistic power system operational planning. In 2023 IEEE Belgrade PowerTech (pp. 1-6). IEEE.
  • Şahin, O., & Başarır, Ç. (2019). Bireysel emeklilik şirketlerinin finansal performanslarının değerlendirilmesi: Türkiye örneği. Yönetim Bilimleri Dergisi, 17(33), 211-229.
  • Tan, M., Yazıcı, E. and Alakaş, H. M. (2023). Active Wheelchair Selection for the Physically Disabled with Analytical Network Process, TOPSIS, and PROMETHEE Methods. International Journal of Pure and Applied Sciences, 9(1), 76-87.
  • Trihapningsari, D., Agushinta, R. D. and Banowosari, L. Y. (2021). Pengukuran Kapabilitas Tata Kelola TI Sistem Informasi Tiras dan Transaksi Bahan Ajar Universitas Terbuka Menggunakan Cobit 5. Jurnal Teknologi Informasi dan Ilmu Komputer,8(5),965-976.
  • Van der Aalst, W. M. P. (2022). European leadership in process management. ACM Transactions on Management Information Systems,65(4),80-83.
  • Wang, Z., Liu, L., Dong, X. and Liu, J. (2023). Evaluation of neural network models and quality forecasting based on process time-series data. Canadian Journal of Chemical Engineering. 102(4), 1522-1537.
  • Cheng, W., Wang, J. Y. and Ma, J. T. (2008). Optimal investment decision of security investment fund based on the experiment design. In 2008 International Conference on Risk Management & Engineering Management (pp. 527-530). IEEE.Wilton, M. (1974). What you should know about pensions. Managerial Finance, 1(1), 56-67.
  • Wu, X., Lin, Y. and Lin, T. (2023). Collaborative Production Task Allocation Decision for Multi Smart Factory. Proceedings of the IEEE International Conference on Data Science and Advanced Analytics (DSAA), 1-6.
  • Zakharov, A., Vakhitov, G. and Enikeeva, Z. (2019). Neural Network Architecture Development for Time Series Forecasting. Journal of Applied Computer Science & Mathematics, 13(6), 5615-5620.
  • Zyubin, V., Ivanishkin, D. S. and Anureev, I. (2023). Towards Process-Oriented Programming Distributed Control Systems. Proceedings of the IEEE International Conference on Data Science and Advanced Analytics (DSAA), 1-6.
There are 43 citations in total.

Details

Primary Language English
Subjects Econometric and Statistical Methods
Journal Section Articles
Authors

Huriye Akpınar 0000-0003-2460-942X

Early Pub Date July 14, 2025
Publication Date July 16, 2025
Submission Date July 20, 2024
Acceptance Date February 19, 2025
Published in Issue Year 2025 Volume: 40 Issue: 3

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APA Akpınar, H. (2025). An Application On Evaluation Of Fund Selection Criteria In The Individual Retirement System (IRS) With Multi-Criteria Decision-Making Methods. İzmir İktisat Dergisi, 40(3), 752-769. https://doi.org/10.24988/ije.1519486
İzmir Journal of Economics
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