Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Series Vol. 5 , 24 April 2023
* Author to whom correspondence should be addressed.
Cooperative game theory is concerned with exploring schemes for allocating payoffs among rational participants in coalitions and has produced several solution designs due to the different emphasis on criteria such as stability and fairness, but this theory has not been widely applied in the field of portfolio selection. In this paper, we explore further applications of the solution concepts of cooperative games based on the model of optimal portfolio selection developed in previous studies, which is modelled in a static form of a non-cooperative zero-sum game between investors and the market and a cooperative game between investors. We propose a risk modified Shapley value based on the tradeoff between return and risk in the financial market based on the Shapley value, and the performance of this solution shows an evident improvement. We also introduce some other solution concepts of cooperative games and give an approach to construct a nucleolus-based portfolio using Maschler's scheme to compute the nucleolus, and the results demonstrate that the allocation schemes based on the cooperative game theory perform well.
Optimal Portfolio Selection, Cooperative Game Theory, Risk Modified Shapley Value, Nucleolus, Stock Market
1. Markowits, H. M. (1952). Portfolio selection. Journal of finance, 7(1), 71-91.
2. Nash, J. F. (1951). Non-cooperative games.” Annals of Mathematics 54: 286-95.(1953). Two-person cooperative games." Econometrica, 21, 128-40.
3. Kocak, H. (2014). Canonical coalition game theory for optimal portfolio selection. Asian Economic and Financial Review, 4(9), 1254-1259.
4. Tataei, P., Roudposhti, F. R., Nikoumaram, H., and Hafezolkotob, A. (2018). Outperforming the market portfolio using coalitional game theory approach. Dama International Journal of Researchers, 3(5):145–155.
5. bin Ibrahim, M. A. R., Hee, P. C., Islam, M. A., & Bahaludin, H. (2020). Cooperative game theory approach for portfolio sectoral selection before and after Malaysia general elections: GE13 versus GE14.
6. Dai, J., & Xue, H. (2004). The strategy of profit allocation among partners in dynamic alliance based on the Shapley value. Chinese Journal of Management Science, 12(4), 33-36.
7. Fang, F., Yu, S., & Liu, M. (2020). An improved Shapley value-based profit allocation method for CHP-VPP. Energy, 213, 118805.
8. Xie, W., Yu, X., Zhang, Y., & Wang, H. (2020, July). An improved shapley value benefit distribution mechanism in cooperative game of cyber threat intelligence sharing. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) (pp. 810-815). IEEE.
9. Singal, V. (2004). Beyond the random walk: A guide to stock market anomalies and low-risk investing. Oxford University Press, USA.
10. Schneider, P., Wagner, C., & Zechner, J. (2020). Low‐Risk Anomalies?. The Journal of finance, 75(5), 2673-2718.
11. Auer, B. R., & Hiller, T. (2019). Can cooperative game theory solve the low‐risk puzzle?. International Journal of Finance & Economics, 24(2), 884-889.
12. Shapley, L. S., & Shubik, M. (1973). Game Theory in Economics: Characteristic function, core, and stable set (Vol. 6). Rand.
13. Young, H. P., Okada, N., & Hashimoto, T. (1982). Cost allocation in water resources development. Water resources research, 18(3), 463-475.
14. Curiel, I. (2013). Cooperative game theory and applications: cooperative games arising from combinatorial optimization problems (Vol. 16). Springer Science & Business Media.
15. Maschler, M., Peleg, B., & Shapley, L. S. (1979). Geometric properties of the kernel, nucleolus, and related solution concepts. Mathematics of operations research, 4(4), 303-338.
16. Ben-Porath, E. (2014). Game Theory, Michael Maschler, Eilon Solan, Shmuel Zamir, Cambridge University Press (2013).
17. Durga, M. V. (2016). An efficient algorithm for solving nucleolus of cooperative TU games using MATLAB. Int. J. Innov. Res. Dev, 5.
18. Khudaykulova, M., Yuanqiong, H., & Khudaykulov, A. (2022). Economic consequences and implications of the Ukraine-russia war. International Journal of Management Science and Business Administration, 8(4), 44-52.
19. Mbah, R. E., & Wasum, D. F. (2022). Russian-Ukraine 2022 War: A review of the economic impact of Russian-Ukraine crisis on the USA, UK, Canada, and Europe. Advances in Social Sciences Research Journal, 9(3), 144-153.
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open Access Instruction).