EVALUATION AND PRIORITIZATION OF MUTUAL FUNDS USING INTUITIONISTIC FUZZY ANALYTIC HIERARCHY PROCESS
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Published
Mar 4, 2025
Soheila Senfi
Seyed Mojtaba Mirlohi Reza Sheikh
https://orcid.org/0000-0002-3505-4725
Seyed Mojtaba Mirlohi Reza Sheikh
Abstract
Investment has become increasingly important for individuals in today’s economic landscape, particularly regarding the growth of the capital market. A primary concern for investors is selecting the best investment options. When faced with complex decisions, such as choosing a mutual fund, multi-criteria decision-making (MCDM) methods can be used to prioritize options and make optimal choices. This study focuses on the Intuitionistic Fuzzy Analysis Hierarchy Process (IFAHP) technique. Given the significance of investment in the current economy, this study aims to apply and describe the IFAHP technique for ranking mutual funds listed on the Tehran Stock Exchange. A total of 20 mutual funds were selected based on their size and superior performance compared to other mutual funds, utilizing statistics published by the Financial Information Processing Center of Iran. The mutual funds were evaluated and prioritized using the IFAHP technique, resulting in the following cumulative weights and final weights: Moshtarek Agah (0.6360, ranked 1), Sepehr Aval Bazar (0.6248, ranked 2), and Ofoghe Melat (0.6234, ranked 3) holding the highest priorities. Conversely, the mutual funds Firozeh Movafaghiat (0.4550, ranked 20), Toseh Atlas Mofid (0.4584, ranked 19), and Moshtarek Kargozari Bank Meli Iran (0.4803, ranked 18) have the lowest priorities for investment. Therefore, according to these results and their desired criteria, investors can identify the most suitable options for their investments to maximize their profits.
How to Cite
Senfi, S., Mirlohi, S. M., & Sheikh, R. (2025). EVALUATION AND PRIORITIZATION OF MUTUAL FUNDS USING INTUITIONISTIC FUZZY ANALYTIC HIERARCHY PROCESS . International Journal of the Analytic Hierarchy Process, 16(3). https://doi.org/10.13033/ijahp.v16i3.1237
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Keywords
Mutual fund, Fuzzy Set, Intuitionistic Fuzzy Analytic Hierarchy Process, MCDM, investment
References
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Ayyildiz, E., & Taskin Gumus, A. (2021). Interval-valued Pythagorean fuzzy AHP method-based supply chain performance evaluation by a new extension of SCOR model: SCOR 4.0. Complex & Intelligent Systems, 7, 559-576. https://doi.org/10.1007/s40747-020-00221-9
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Bhojraj, S., Cho, Y., & Yehuda, N. (2012). Mutual fund family size and mutual fund performance: The role of regulatory changes. Journal of Accounting Research, 50(3), 647-684. https://doi.org/10.1111/j.1475-679x.2011.00436.x
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Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3), 233-247. https://doi.org/10.1016/0165-0114(85)90090-9
Chakraborty, S., Chatterjee, P., & Das, P. P. (2023). Multi-Criteria Decision-Making methods in manufacturing environments: models and applications. CRC Press. https://doi.org/10.1201/9781003377030
Chang, C. H., Lin, J. J., Lin, J. H., & Chiang, M. C. (2010). Domestic open-end equity mutual fund performance evaluation using extended TOPSIS method with different distance approaches. Expert Systems with Applications, 37(6), 4642-4649. https://doi.org/10.1016/j.eswa.2009.12.044
Chen, L. H., & Huang, L. (2009). Portfolio optimization of equity mutual funds with fuzzy return rates and risks. Expert Systems with Applications, 36(2), 3720-3727. https://doi.org/10.1016/j.eswa.2008.02.027
Dabiri, M., & Safari, B. (2016). Application of the inventive fuzzy hierarchy process (IFAHP) integration methodology in prioritizing new fields in each region at the Technical and Vocational University of Iran. Karafan Quarterly Scientific Journal, 13(2), 63-75.
Das, L. (2022). Evaluation of mutual funds using TOPSIS method. Rest Publisher.
de Souza, D. G. B., dos Santos, E. A., Soma, N. Y., & da Silva, C. E. S. (2021). MCDM-based R&D project selection: A systematic literature review. Sustainability, 13(21), 11626. https://doi.org/10.3390/su132111626
Erensal, Y. C., & Albayrak, E. (2007). The impact of micro‐and macroergonomics considerations on appropriate technology transfer decisions in developing countries: The case of Turkey. Human Factors and Ergonomics in Manufacturing & Service Industries, 17(1), 1-19. https://doi.org/10.1002/hfm.20063
Finnegan, S., Anderson, S. C., Harnik, P. G., Simpson, C., Tittensor, D. P., Byrnes, J. E., ... & Pandolfi, J. M. (2015). Paleontological baselines for evaluating extinction risk in the modern oceans. Science, 348(6234), 567-570.
Ghorbanzadeh, O., Moslem, S., Blaschke, T., & Duleba, S. (2018). Sustainable urban transport planning considering different stakeholder groups by an interval-AHP decision support model. Sustainability, 11(1), 9. https://doi.org/10.3390/su11010009
Gregoriou, G. N. (2007). Efficiency of US mutual funds using data envelopment analysis. In Performance of Mutual Funds: An International Perspective (pp. 152-167). London: Palgrave Macmillan UK.
Gursoy, Cudi Tuncer and Erzurumlu, Yaman O. (2001). Evaluation of portfolio performance of Turkish mutual funds. Doğuş University Journal, 4, 43-58. https://ssrn.com/abstract=2322097
Hernández-Pacheco, A., & Flores, R. C. (2021). A new approach to Markowitz portfolio optimization using ruin-based outcomes. IAA Joint Sections Colloquium.
Hsieh, H. P., Tebourbi, I., Lu, W. M., & Liu, N. Y. (2020). Mutual fund performance: The decision quality and capital magnet efficiencies. Managerial and Decision Economics, 41(5), 861-872. https://doi.org/10.1002/mde.3143
Humphrey-Murto, S., Wood, T. J., Gonsalves, C., Mascioli, K., & Varpio, L. (2020). The delphi method. Academic Medicine, 95(1), 168. https://doi.org/10.1097/acm.0000000000002887
Indro, D. C., Jiang, C. X., Hu, M. Y., & Lee, W. Y. (1999). Mutual fund performance: does fund size matter?. Financial Analysts Journal, 55(3), 74-87. https://doi.org/10.2469/faj.v55.n3.2274
Irawati, R., & Diyah, A. A. (2022). Asset allocation policy analysis, share selection, and level of mutual fund risk in Indonesia. Indikator, 6(1), 411762. https://doi.org/10.22441/indikator.v6i1.14110
Jensen, M. C. (1968). The performance of mutual funds in the period 1945-1964. Journal of Finance, 23(2), 389-416.
Jin, B., & Xu, X. (2024a). Carbon emission allowance price forecasting for China Guangdong carbon emission exchange via the neural network. Global Finance Review, 6(1), 3491-3491. https://doi.org/10.18282/gfr.v6i1.3491
Jin, B., & Xu, X. (2024b). Wholesale price forecasts of green grams using the neural network. Asian Journal of Economics and Banking, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/ajeb-01-2024-0007
Kahraman, C., Öztayşi, B., & Çevik Onar, S. (2018). An integrated intuitionistic fuzzy AHP and TOPSIS approach to evaluation of outsource manufacturers. Journal of Intelligent Systems, 29(1), 283-297. https://doi.org/10.1515/jisys-2017-0363
Kan, L., Mukhamedyarova-Levina, T., Abdullayeva, B., & Mukhiyayeva, D. (2023). External Factors Influencing on the Development of Mutual Funds in Kazakhstan. Eurasian Journal of Economic and Business Studies, 67(3), 85-103. https://doi.org/10.47703/ejebs.v3i67.277
Laks, I., Walczak, Z., & Walczak, N. (2023). Fuzzy analytical hierarchy process methods in changing the damming level of a small hydropower plant: Case study of Rosko SHP in Poland. Water Resources and Industry, 29, 100204. https://doi.org/10.1016/j.wri.2023.100204
Levišauskait, K. (2010). Investment analysis and portfolio management. Leonardo da Vinci Programme Project. Kaunas, Lithuania: Vytautus Magnus University
Lhabitant, F. S. (2017). Portfolio diversification. Elsevier.
Lin, K. H., & Neely, M. T. (2020). Divested: Inequality in financialized America. Oxford University Press. https://doi.org/10.1093/oso/9780190638313.003.0001
Ling, L. (2011). Life cycle analysis of US mutual funds. International Review of Accounting, Banking & Finance, 3(4), 1-24.
Makridakis, S., Andersen, A., Carbone, R., Fildes, R., Hibon, M., Lewandowski, R., ... & Winkler, R. (1982). The accuracy of extrapolation (time series) methods: Results of a forecasting competition. Journal of Forecasting, 1(2), 111-153. https://doi.org/10.1002/for.3980010202
Matallín-Sáez, J. C., Soler-Domínguez, A., Tortosa-Ausina, E., & de Mingo-López, D. V. (2019). Ethical strategy focus and mutual fund management: Performance and persistence. Journal of Cleaner Production, 213, 618-633. https://doi.org/10.1016/j.jclepro.2018.12.130
Mert, A. (2023). Defuzzification of non-linear pentagonal intuitionistic fuzzy numbers and application in the minimum spanning tree problem. Symmetry, 15(10), 1853. https://doi.org/10.3390/sym15101853
Mimovic, P., Jakšić, M., & Leković, M. (2017). A Multicriteria Decision Making approach to performance evaluation of mutual funds: A case study in Serbia. Yugoslav Journal of Operations Research, 28(3), 385-414. https://doi.org/10.2298/yjor170217023j
Mohd Padil, H., Kasim, E. S., Muda, S., Ismail, N., & Md Zin, N. (2022). Financial literacy and awareness of investment scams among university students. Journal of Financial Crime, 29(1), 355-367. https://doi.org/10.1108/jfc-01-2021-0012
Moradpour, N. (2021). Ranking mutual funds performance based on post-modern portfolio theory indicators using Multi-Criteria Decision-Making methods. International Journal of Management, Accounting & Economics, 8(1), 1-24.
Murthi, B. P. S., Choi, Y. K., & Desai, P. (1997). Efficiency of mutual funds and portfolio performance measurement: A non-parametric approach. European Journal of Operational Research, 98(2), 408-418. https://doi.org/10.1016/s0377-2217(96)00356-6
Muruganandan, S., & Sharma, A. M. (2024). Does the technique for order of preference by similarity to ideal solution (TOPSIS) help to find winners in mutual funds?. OPSEARCH, 1-24. https://doi.org/10.1007/s12597-024-00881-y
Otten, R., & Bams, D. (2002). European mutual fund performance. European Financial Management, 8(1), 75-101. https://doi.org/10.1111/1468-036x.00177
Pan, L., & Mishra, V. (2018). Stock market development and economic growth: Empirical evidence from China. Economic Modelling, 68, 661-673. https://doi.org/10.1016/j.econmod.2017.07.005
Pendaraki, K., & Zopounidis, C. (2003). Evaluation of equity mutual funds’ performance using a multicriteria methodology. Operational Research, 3, 69-90. https://doi.org/10.1007/bf02940279
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57. https://doi.org/10.1016/j.omega.2014.11.009
Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126-130. https://doi.org/10.1016/j.omega.2015.12.001
Saaty, T. L. (1986). Axiomatic foundation of the analytic hierarchy process. Management Science, 32(7), 841-855. https://doi.org/10.1287/mnsc.32.7.841
Senfi, S., Sheikh, R., & Sana, S. S. (2024). A portfolio selection using the intuitionistic fuzzy analytic hierarchy process: A case study of the Tehran Stock Exchange. Green Finance, 6(2), 219-248. https://doi.org/10.3934/gf.2024009
Sepehrian, Z., Khoshfetrat, S., & Ebadi, S. (2021). An approach for generating weights using the pairwise comparison matrix. Journal of Mathematics, 2021, 1-13. https://doi.org/10.1155/2021/3217120
Sharpe, W. F. (1966). Mutual fund performance. Journal of Business, 39(1), 119-138.
Swinkels, L., & Rzezniczak, P. (2009). Performance evaluation of Polish mutual fund managers. International Journal of Emerging Markets, 4(1), 26-42. https://doi.org/10.1108/17468800910931652
Tachiwou, A. M. (2010). Stock market development and economic growth: the case of West African monetary union. International Journal of Economics and Finance, 2(3), 97-103. https://doi.org/10.5539/ijef.v2n3p97
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