An integrated decision support system for stock investment based on spherical fuzzy PT-EDAS method and MEREC
Abstract
The stock investment selection could be deemed as a classic multiple attribute group decision making (MAGDM) problem involving multiple conflicts and interleaved qualitative and quantitative attributes. Spherical fuzzy sets (SFSs) can excavate the potential vagueness and intricacy in MAGDM more effectively and deeply. This article we propose an integrated decision support system (IDSS) based on SFSs, prospect theory (PT), distance from average solution (EDAS) method and the MEthod based on the Removal Effects of Criteria (MEREC). The proposed IDSS, called SF-PT-EDAS-MEREC model, uses SFSs to describe the uncertain and obscure assessment information of DMs. The combination of PT and EDAS (PT-EDAS) method adequately captures DMs’ psychological behavior characteristics to execute more reasonable alternative evaluation. The MEREC is utilized to efficaciously obtain unknown attribute weights. In addition, this paper also presents a novel score function to compare spherical fuzzy numbers (SFNs) more directly and efficiently. Eventually, in order to illustrate the practicability of the proposed IDSS, two numerical examples of stock investment selection are employed to achieve this. Meanwhile, the comparative study with existing approach further demonstrates the effectiveness and superiority of SF-PT-EDAS-MEREC model.
Keyword : spherical fuzzy sets, EDAS method, multiple attribute group decision making, prospect theory, MEREC, stock investment selection
How to Cite
Zhang, H., Wang, H., Wei, G., & Chen, X. (2023). An integrated decision support system for stock investment based on spherical fuzzy PT-EDAS method and MEREC. Technological and Economic Development of Economy, 29(4), 1353–1381. https://doi.org/10.3846/tede.2023.19123
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Chen, T., Wang, Y.-T., Wang, J.-Q., Li, L., & Cheng, P.-F. (2020). Multistage decision framework for the selection of renewable energy sources based on prospect theory and PROMETHEE. International Journal of Fuzzy Systems, 22, 1535–1551. https://doi.org/10.1007/s40815-020-00858-1
Chen, T. Y. (2018). Remoteness index-based Pythagorean fuzzy VIKOR methods with a generalized distance measure for multiple criteria decision analysis. Information Fusion, 41, 129–150. https://doi.org/10.1016/j.inffus.2017.09.003
Deng, H., Yeh, C. H., & Willis, R. J. (2000). Inter-company comparison using modified TOPSIS with objective weights. Computers & Operations Research, 27, 963–973. https://doi.org/10.1016/S0305-0548(99)00069-6
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Fan, J., Zhai, S., & Wu, M. (2022). PT-MARCOS multi-attribute decision-making method under neutrosophic cubic environment. Journal of Intelligent & Fuzzy Systems, 42, 1737–1748. https://doi.org/10.3233/JIFS-211189
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Gundogdu, F. K. (2020). A spherical fuzzy extension of MULTIMOORA method. Journal of Intelligent & Fuzzy Systems, 38, 963–978. https://doi.org/10.3233/JIFS-179462
Gundogdu, F. K., & Kahraman, C. (2019). Spherical fuzzy sets and spherical fuzzy TOPSIS method. Journal of Intelligent & Fuzzy Systems, 36, 337–352. https://doi.org/10.3233/JIFS-181401
He, Y., Lei, F., Wei, G., Wang, R., Wu, J., & Wei, C. (2019). EDAS method for multiple attribute group decision making with probabilistic uncertain linguistic information and its application to green supplier selection. International Journal of Computational Intelligence Systems, 12, 1361–1370. https://doi.org/10.2991/ijcis.d.191028.001
Huang, W., Goto, S., & Nakamura, M. (2004). Decision-making for stock trading based on trading probability by considering whole market movement. European Journal of Operational Research, 157, 227–241. https://doi.org/10.1016/S0377-2217(03)00144-9
Huang, Y., Lin, R., & Chen, X. (2021). An enhancement EDAS method based on prospect theory. Technological and Economic Development of Economy, 27, 1019–1038. https://doi.org/10.3846/tede.2021.15038
Jia, F., & Wang, X. (2020). Rough-Number-Based Multiple-Criteria Group Decision-Making Method by combining the BWM and prospect theory. Mathematical Problems in Engineering, 2020, 8738327. https://doi.org/10.1155/2020/8738327
Jiang, Z., Wei, G., & Guo, Y. (2022). Picture fuzzy MABAC method based on prospect theory for multiple attribute group decision making and its application to suppliers selection. Journal of Intelligent & Fuzzy Systems, 42, 3405–3415. https://doi.org/10.3233/JIFS-211359
Ju, Y., Liang, Y., Luo, C., Dong, P., Gonzalez, E. D. R. S., & Wang, A. (2021). T-spherical fuzzy TODIM method for multi-criteria group decision-making problem with incomplete weight information. Soft Computing, 25, 2981–3001. https://doi.org/10.1007/s00500-020-05357-x
Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47, 263–291. https://doi.org/10.2307/1914185
Kahraman, C., Keshavarz Ghorabaee, M., Zavadskas, E. K., Onar, S. C., Yazdani, M., & Oztaysi, B. (2017). Intuitionistic fuzzy EDAS method: An application to solid waste disposal site selection. Journal of Environmental Engineering and Landscape Management, 25, 1–12. https://doi.org/10.3846/16486897.2017.1281139
Kahraman, C., Onar, S. C., & Oztaysi, B. (2022). A novel spherical fuzzy CRITIC method and its application to prioritization of supplier selection criteria. Journal of Intelligent & Fuzzy Systems, 42, 29–36. https://doi.org/10.3233/JIFS-219172
Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2021). Determination of objective weights using a new method based on the removal effects of criteria (MEREC). Symmetry-Basel, 13, 525. https://doi.org/10.3390/sym13040525
Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2017). A new multi-criteria model based on interval type-2 fuzzy sets and EDAS method for supplier evaluation and order allocation with environmental considerations. Computers & Industrial Engineering, 112, 156–174. https://doi.org/10.1016/j.cie.2017.08.017
Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., & Turskis, Z. (2015). Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica, 26, 435–451. https://doi.org/10.15388/Informatica.2015.57
Kutlu Gündoğdu, F., & Kahraman, C. (2021). Optimal site selection of electric vehicle charging station by using spherical fuzzy TOPSIS method. In C. Kahraman & F. Kutlu Gündoğdu (Eds.), Decision making with spherical fuzzy sets: Theory and applications (pp. 201–216). Springer International Publishing. https://doi.org/10.1007/978-3-030-45461-6_8
Li, P., Liu, J., Wei, C., & Liu, J. (2022). A new EDAS method based on prospect theory for Pythagorean fuzzy set and its application in selecting investment projects for highway. Kybernetes, 51, 2636–2651. https://doi.org/10.1108/K-01-2021-0066
Li, X., Ju, Y. B., Ju, D. W., Zhang, W. K., Dong, P. W., & Wang, A. H. (2019). Multi-attribute group decision making method based on EDAS under picture fuzzy environment. IEEE Access, 7, 141179–141192. https://doi.org/10.1109/ACCESS.2019.2943348
Liu, P., & Zhang, P. (2021). A normal wiggly hesitant fuzzy MABAC method based on CCSD and prospect theory for multiple attribute decision making. International Journal of Intelligent Systems, 36, 447–477. https://doi.org/10.1002/int.22306
Mahmood, T., Ullah, K., Khan, Q., & Jan, N. (2019). An approach toward decision-making and medical diagnosis problems using the concept of spherical fuzzy sets. Neural Computing & Applications, 31, 7041–7053. https://doi.org/10.1007/s00521-018-3521-2
Menekse, A., & Akdag, H. C. (2022). Distance education tool selection using novel spherical fuzzy AHP EDAS. Soft Computing, 26, 1617–1635. https://doi.org/10.1007/s00500-022-06763-z
Nguyen, P. H., Dang, T., Nguyen, K., & Pham, H. A. (2022). Spherical fuzzy WASPAS-based entropy objective weighting for international payment method selection. Computers Materials & Continua, 72, 2055–2075. https://doi.org/10.32604/cmc.2022.025532
Opricovic, S., & Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 2, 445–455. https://doi.org/10.1016/S0377-2217(03)00020-1
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