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A Novel Approach for Portfolio Optimization Using Fuzzy AHP Based on Gustafson Kessel Clustering Algorithm

Year 2024, Volume: 11 Issue: 4, 1436 - 1456, 31.12.2024
https://doi.org/10.30798/makuiibf.1469103

Abstract

Portfolio management involves modeling risk-return relationships. However, the diverse factors impacting financial markets introduce uncertainty into future portfolio selection. The aim of this study is to propose a portfolio selection model to assist investors in creating the most suitable investment plan in the financial market uncertainty. In this context, a preliminary reduction step is applied to the stocks using the Gustafson-Kessel (GK) algorithm, a fuzzy clustering method, to select portfolio stocks. Later, trapezoidal fuzzy numbers (TrFNs) were defined instead of triangular fuzzy numbers (TFNs) used in the Constrained Fuzzy Analytic Hierarchy Process (AHP) for portfolio selection problems. By using new fuzzy numbers, the weights of the criteria were obtained as TrFNs. Then, a linear programming problem was modeled using the weights of the obtained criteria as a TrFN. For this purpose, a method available in the literature was used that uses price variables in the objective function as TFNs. In this study, a linear programming model that uses these variables as TrFNs is proposed as an alternative to the method that uses the price variables in the objective function as TFNs. In this proposed model, the weights obtained from the Constrained Fuzzy AHP using TrFNs are used as price variables in the objective function of the created linear programming problem. Proposed model then applied to the 48-month return data set of stocks in the Istanbul Stock Exchange 100 (ISE-100) index to determine which stocks the investor should choose and the investment rates investor should make in these stocks. In addition, in order to examine the effectiveness of the proposed model within the scope of the study, portfolio distributions were obtained with different portfolio optimization methods using the same data set and the results were compared.

Ethical Statement

The study does not necessitate Ethics Committee permission. The study has been crafted in adherence to the principles of research and publication ethics. The authors declare that there exists no financial conflict of interest involving any institution, organization, or individual(s) associated with the article. Furthermore, there are no conflicts of interest among the authors themselves. The authors declare that they all equally contributed to all processes of the research.

References

  • Abdullah, A., Banmongkol, C., Hoonchareon, N., andHidaka, K. (2017). A study on the gustafson-kessel clustering algorithm in power system fault identification. Journal of Electrical Engineering and Technology, 12(5), 1798–1804. https://doi.org/10.5370/JEET.2017.12.5.1798
  • Akbaş, S., and Erbay Dalkılıç, T. (2021). A hybrid algorithm for portfolio selection: An application on the Dow Jones Index (DJI). Journal of Computational and Applied Mathematics, 398, 113678. https://doi.org/10.1016/j.cam.2021.113678
  • Bezdek, J.C., Ehrlich, R., and Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2-3), 191–203. https://doi.org/10.1016/0098-3004(84)90020-7
  • 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
  • Cardone, B., and Di Martino, F. (2020). A novel fuzzy entropy-based method to improve the performance of the fuzzy C-means algorithm. Electronics, 9(4), 554. https://doi.org/10.3390/electronics9040554
  • Chang, D.-Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649–655. https://doi.org/10.1016/0377-2217(95)00300-2
  • Chen, L.H., and 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
  • Cheng, C.H., Yang, K.L., and Hwang, C.L. (1999). Evaluating attack helicopters by AHP based on linguistic variable weight. European Journal of Operational Research, 116(2), 423–435. https://doi.org/10.1016/S0377- 2217(98)00156-8
  • Cox, E. (1994). The fuzzy systems handbook: a practitioner’s guide to building, using, and maintaining fuzzy systems. Academic Press Professional.
  • Enea, M., and Piazza, T. (2004). Project Selection by Constrained Fuzzy AHP. Fuzzy Optimization and Decision Making, 3, 39–62. https://doi.org/10.1023/B:FODM.0000013071.63614.3d
  • Gath, I. and Geva, A. B. (1989). Unsupervised optimal fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 773–781. https://doi.org/10.1109/34.192473
  • Ghosh, A., Mishra, N.S., and Ghosh, S. (2011). Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Information Sciences, 181(4), 699–715. https://doi.org/10.1016/j.ins.2010.10.016
  • Gustafson, D., and Kessel, W. (1979, January 10-12). Fuzzy clustering with a fuzzy covariance matrix. [Conference presentation]. 1978 IEEE Conference on Decision and Control Including the 17th Symposium on Adaptive Processes, San Diego, CA, USA.
  • Hadiloo, S., Mirzaei, S., Hashemi, H., and Beiranvand, B. (2018). Comparison between unsupervised and supervised fuzzy clustering method in interactive mode to obtain the best result for extract subtle patterns from seismic facies maps. Geopersia, 8(1), 27–34. https://doi.org/10.22059/GEOPE.2017.240099.648346
  • Huang, K.Y. (2009). Application of VPRS model with enhanced threshold parameter selection mechanism to automatic stock market forecasting and portfolio selection. Expert Systems with Applications, 36(9), 11652– 11661. https://doi.org/10.1016/j.eswa.2009.03.028
  • Jang, J.S.R. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685. https://doi.org/10.1109/21.256541
  • Jang, J.S.R., Sun, C.T., and Mizutani, E. (1997). Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]. IEEE Transactions on Automatic Control, 42(10), 1482–1484.
  • Khedmati, M., and Azin, P. (2020). An online portfolio selection algorithm using clustering approaches and considering transaction costs. Expert Systems with Applications, 159, 113546. https://doi.org/10.1016/j.eswa.2020.113546
  • Lai, Y.J., and Hwang, C.-L. (1992). A new approach to some possibilistic linear programming problems. Fuzzy Sets and Systems, 49(2), 121–133. https://doi.org/10.1016/0165-0114(92)90318-X
  • Mamdani, E.H. (1974). Application of fuzzy algorithms for control of simple dynamic plant. Proceedings of the institution of electrical engineers, 121(12), 1585-1588. https://doi.org/10.1049/piee.1974.0328
  • Mamdani, E.H., and Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1–13. https://doi.org/10.1016/S0020-7373(75)80002-2
  • Markowitz, H. (1952). Portfolio Selection. The Journal of Finance ,7(1), 77–91. https://doi.org/10.1111/j.1540- 6261.1952.tb01525.x
  • Miller, D.J., Nelson, C.A., Cannon, M.B., and Cannon, K.P. (2009). Comparison of Fuzzy Clustering Methods and Their Applications to Geophysics Data. Applied Computational Intelligence and Soft Computing, 2009(7), 1–9. https://doi.org/10.1155/2009/876361
  • Rockafellar, R.T., and Uryasev, S. (2000). Optimization of conditional value-at-risk. Journal of Risk, 2(3), 21–41. https://doi.org/10.21314/JOR.2000.038
  • Serir, L., Ramasso, E., and Zerhouni, N. (2012). Evidential evolving Gustafson–Kessel algorithm for online data streams partitioning using belief function theory. International Journal of Approximate Reasoning, 53(5), 747–768. https://doi.org/10.1016/j.ijar.2012.01.009
  • Shapiro, A.F., and Koissi, M.-C. (2017). Fuzzy logic modifications of the Analytic Hierarchy Process. Insurance: Mathematics and Economics, 75, 189–202. https://doi.org/10.1016/j.insmatheco.2017.05.003
  • Stam, A., Sun, M., and Haines, M. (1996). Artificial neural network representations for hierarchical preference structures. Computers & Operations Research, 23(12), 1191–1201. https://doi.org/10.1016/S0305-0548(96)00021- 4
  • Sugeno, M., and Kang, G. (1988). Structure identification of fuzzy model. Fuzzy Sets and Systems, 28(1), 15–33. https://doi.org/10.1016/0165-0114(88)90113-3
  • Takagi, T., and Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, SMC-15(1), 116–132. https://doi.org/10.1109/TSMC.1985.6313399
  • van Laarhoven, P.J.M., and Pedrycz, W. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems, 11(1-3), 229–241. https://doi.org/10.1016/S0165-0114(83)80082-7
  • Weck, M., Klocke, F., Schell, H., and Rüenauver, E. (1997). Evaluating alternative production cycles using the extended fuzzy AHP method. European Journal of Operational Research, 100(2), 351–366.
  • https://doi.org/10.1016/S0377-2217(96)00295-0
  • Zadeh, L. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.
  • Zadeh, L.A. (1975). The concept of a linguistic variable and its application to approximate reasoning-I. Information Sciences, 8(3), 199–249. https://doi.org/10.1016/0020-0255(75)90036-5
  • Zhang, D.Q., Chen, S.C. (2003). Clustering Incomplete Data Using Kernel-Based Fuzzy C-means Algorithm. Neural Processing Letters, 18, 155–162. https://doi.org/10.1023/B:NEPL.0000011135.19145.1b
  • Zimmermann, H.-J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets Systems, 1(1), 45–55. https://doi.org/10.1016/0165-0114(78)90031-3

A Novel Approach for Portfolio Optimization Using Fuzzy AHP Based on Gustafson Kessel Clustering Algorithm

Year 2024, Volume: 11 Issue: 4, 1436 - 1456, 31.12.2024
https://doi.org/10.30798/makuiibf.1469103

Abstract

Portfolio management involves modeling risk-return relationships. However, the diverse factors impacting financial markets introduce uncertainty into future portfolio selection. The aim of this study is to propose a portfolio selection model to assist investors in creating the most suitable investment plan in the financial market uncertainty. In this context, a preliminary reduction step is applied to the stocks using the Gustafson-Kessel (GK) algorithm, a fuzzy clustering method, to select portfolio stocks. Later, trapezoidal fuzzy numbers (TrFNs) were defined instead of triangular fuzzy numbers (TFNs) used in the Constrained Fuzzy Analytic Hierarchy Process (AHP) for portfolio selection problems. By using new fuzzy numbers, the weights of the criteria were obtained as TrFNs. Then, a linear programming problem was modeled using the weights of the obtained criteria as a TrFN. For this purpose, a method available in the literature was used that uses price variables in the objective function as TFNs. In this study, a linear programming model that uses these variables as TrFNs is proposed as an alternative to the method that uses the price variables in the objective function as TFNs. In this proposed model, the weights obtained from the Constrained Fuzzy AHP using TrFNs are used as price variables in the objective function of the created linear programming problem. Proposed model then applied to the 48-month return data set of stocks in the Istanbul Stock Exchange 100 (ISE-100) index to determine which stocks the investor should choose and the investment rates investor should make in these stocks. In addition, in order to examine the effectiveness of the proposed model within the scope of the study, portfolio distributions were obtained with different portfolio optimization methods using the same data set and the results were compared.

References

  • Abdullah, A., Banmongkol, C., Hoonchareon, N., andHidaka, K. (2017). A study on the gustafson-kessel clustering algorithm in power system fault identification. Journal of Electrical Engineering and Technology, 12(5), 1798–1804. https://doi.org/10.5370/JEET.2017.12.5.1798
  • Akbaş, S., and Erbay Dalkılıç, T. (2021). A hybrid algorithm for portfolio selection: An application on the Dow Jones Index (DJI). Journal of Computational and Applied Mathematics, 398, 113678. https://doi.org/10.1016/j.cam.2021.113678
  • Bezdek, J.C., Ehrlich, R., and Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2-3), 191–203. https://doi.org/10.1016/0098-3004(84)90020-7
  • 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
  • Cardone, B., and Di Martino, F. (2020). A novel fuzzy entropy-based method to improve the performance of the fuzzy C-means algorithm. Electronics, 9(4), 554. https://doi.org/10.3390/electronics9040554
  • Chang, D.-Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649–655. https://doi.org/10.1016/0377-2217(95)00300-2
  • Chen, L.H., and 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
  • Cheng, C.H., Yang, K.L., and Hwang, C.L. (1999). Evaluating attack helicopters by AHP based on linguistic variable weight. European Journal of Operational Research, 116(2), 423–435. https://doi.org/10.1016/S0377- 2217(98)00156-8
  • Cox, E. (1994). The fuzzy systems handbook: a practitioner’s guide to building, using, and maintaining fuzzy systems. Academic Press Professional.
  • Enea, M., and Piazza, T. (2004). Project Selection by Constrained Fuzzy AHP. Fuzzy Optimization and Decision Making, 3, 39–62. https://doi.org/10.1023/B:FODM.0000013071.63614.3d
  • Gath, I. and Geva, A. B. (1989). Unsupervised optimal fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 773–781. https://doi.org/10.1109/34.192473
  • Ghosh, A., Mishra, N.S., and Ghosh, S. (2011). Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Information Sciences, 181(4), 699–715. https://doi.org/10.1016/j.ins.2010.10.016
  • Gustafson, D., and Kessel, W. (1979, January 10-12). Fuzzy clustering with a fuzzy covariance matrix. [Conference presentation]. 1978 IEEE Conference on Decision and Control Including the 17th Symposium on Adaptive Processes, San Diego, CA, USA.
  • Hadiloo, S., Mirzaei, S., Hashemi, H., and Beiranvand, B. (2018). Comparison between unsupervised and supervised fuzzy clustering method in interactive mode to obtain the best result for extract subtle patterns from seismic facies maps. Geopersia, 8(1), 27–34. https://doi.org/10.22059/GEOPE.2017.240099.648346
  • Huang, K.Y. (2009). Application of VPRS model with enhanced threshold parameter selection mechanism to automatic stock market forecasting and portfolio selection. Expert Systems with Applications, 36(9), 11652– 11661. https://doi.org/10.1016/j.eswa.2009.03.028
  • Jang, J.S.R. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665–685. https://doi.org/10.1109/21.256541
  • Jang, J.S.R., Sun, C.T., and Mizutani, E. (1997). Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]. IEEE Transactions on Automatic Control, 42(10), 1482–1484.
  • Khedmati, M., and Azin, P. (2020). An online portfolio selection algorithm using clustering approaches and considering transaction costs. Expert Systems with Applications, 159, 113546. https://doi.org/10.1016/j.eswa.2020.113546
  • Lai, Y.J., and Hwang, C.-L. (1992). A new approach to some possibilistic linear programming problems. Fuzzy Sets and Systems, 49(2), 121–133. https://doi.org/10.1016/0165-0114(92)90318-X
  • Mamdani, E.H. (1974). Application of fuzzy algorithms for control of simple dynamic plant. Proceedings of the institution of electrical engineers, 121(12), 1585-1588. https://doi.org/10.1049/piee.1974.0328
  • Mamdani, E.H., and Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1–13. https://doi.org/10.1016/S0020-7373(75)80002-2
  • Markowitz, H. (1952). Portfolio Selection. The Journal of Finance ,7(1), 77–91. https://doi.org/10.1111/j.1540- 6261.1952.tb01525.x
  • Miller, D.J., Nelson, C.A., Cannon, M.B., and Cannon, K.P. (2009). Comparison of Fuzzy Clustering Methods and Their Applications to Geophysics Data. Applied Computational Intelligence and Soft Computing, 2009(7), 1–9. https://doi.org/10.1155/2009/876361
  • Rockafellar, R.T., and Uryasev, S. (2000). Optimization of conditional value-at-risk. Journal of Risk, 2(3), 21–41. https://doi.org/10.21314/JOR.2000.038
  • Serir, L., Ramasso, E., and Zerhouni, N. (2012). Evidential evolving Gustafson–Kessel algorithm for online data streams partitioning using belief function theory. International Journal of Approximate Reasoning, 53(5), 747–768. https://doi.org/10.1016/j.ijar.2012.01.009
  • Shapiro, A.F., and Koissi, M.-C. (2017). Fuzzy logic modifications of the Analytic Hierarchy Process. Insurance: Mathematics and Economics, 75, 189–202. https://doi.org/10.1016/j.insmatheco.2017.05.003
  • Stam, A., Sun, M., and Haines, M. (1996). Artificial neural network representations for hierarchical preference structures. Computers & Operations Research, 23(12), 1191–1201. https://doi.org/10.1016/S0305-0548(96)00021- 4
  • Sugeno, M., and Kang, G. (1988). Structure identification of fuzzy model. Fuzzy Sets and Systems, 28(1), 15–33. https://doi.org/10.1016/0165-0114(88)90113-3
  • Takagi, T., and Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics, SMC-15(1), 116–132. https://doi.org/10.1109/TSMC.1985.6313399
  • van Laarhoven, P.J.M., and Pedrycz, W. (1983). A fuzzy extension of Saaty’s priority theory. Fuzzy Sets and Systems, 11(1-3), 229–241. https://doi.org/10.1016/S0165-0114(83)80082-7
  • Weck, M., Klocke, F., Schell, H., and Rüenauver, E. (1997). Evaluating alternative production cycles using the extended fuzzy AHP method. European Journal of Operational Research, 100(2), 351–366.
  • https://doi.org/10.1016/S0377-2217(96)00295-0
  • Zadeh, L. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.
  • Zadeh, L.A. (1975). The concept of a linguistic variable and its application to approximate reasoning-I. Information Sciences, 8(3), 199–249. https://doi.org/10.1016/0020-0255(75)90036-5
  • Zhang, D.Q., Chen, S.C. (2003). Clustering Incomplete Data Using Kernel-Based Fuzzy C-means Algorithm. Neural Processing Letters, 18, 155–162. https://doi.org/10.1023/B:NEPL.0000011135.19145.1b
  • Zimmermann, H.-J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets Systems, 1(1), 45–55. https://doi.org/10.1016/0165-0114(78)90031-3
There are 36 citations in total.

Details

Primary Language English
Subjects Operations Research
Journal Section Research Articles
Authors

Türkan Erbay Dalkılıç 0000-0003-2923-599X

Yeşim Akbaş 0000-0001-7590-6139

Serkan Akbaş 0000-0001-5220-7458

Publication Date December 31, 2024
Submission Date April 16, 2024
Acceptance Date November 21, 2024
Published in Issue Year 2024 Volume: 11 Issue: 4

Cite

APA Erbay Dalkılıç, T., Akbaş, Y., & Akbaş, S. (2024). A Novel Approach for Portfolio Optimization Using Fuzzy AHP Based on Gustafson Kessel Clustering Algorithm. Journal of Mehmet Akif Ersoy University Economics and Administrative Sciences Faculty, 11(4), 1436-1456. https://doi.org/10.30798/makuiibf.1469103

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