Group drop of sustainability: Trade-off solutions between low returns and portfolio stability

Abstract

Portfolio design is the most difficult aspect of financial investment decisions. This paper presents a trade-off solution between low returns and portfolio stability by a fixed predetermined niveau of conservatism. A conservative model that combines both risk-free assets as agricultural land and risky assets is proposed. An experimental model with one-year historical data for four assets was built and tested to find a globally optimal solution using an evolutionary algorithm. The results showed that a positive return can be realized with a share of 13-14% in the assets of agricultural land.

Keywords

• Evolutionary algorithms
• Multi-criteria optimization problems
• Portfolio design

References

1. [1] Herfindal, O. C. Concentration in the steel industry, PhD dissertation, Columbia University, 1950.
2. [2] Bessler, W., & Wolff, D. Do commodities add value in multi-asset portfolios? An out-of-sample analysis for different investment strategies, Journal of Banking &Finance, 2015; 60; 1-20 DOI: 10.1016/j.jbankfin.2015.06.021,
3. [3] Bjornson, B., & Innes, R. Another look at returns to agricultural and nonagricultural assets, American Journal of Agricultural Economics, 1992; 74(1); 109-119.
4. [4] Franch, X. Research challenges in information science RCIS 2022. Lecture Notes in Business Information Processing, 2022; pp. 1-37, Springer, Cham. DOI: 10.1007/978-3-031-05760.
5. [5] Baker, M. Risk neglect in equity markets. Journal of Portfolio Management, 2016; 42(3); 12-25.
6. [6] Miettinen, K., Nonlinear Multi-Objective Optimization. 1999; Kluwer Academic Publishers, Boston, London, Dordrecht.
7. [7] Madhogarhia, P. K., & Lam, M. Dynamic asset allocation, Journal of Asset Management. 2015; 16(5,) 293-302.
8. [8] Petrov, P., Kostadinov, G., Zhivkov, P., Velichkova, V. & Balabanov, T. Approximated sequences reconstruction with genetic algorithms, In Proceedings of 28th Symposium with International Participation-Control of Energy, Industrial and Ecological Systems, 2020; 63-66.

9. [9] Bekkers, N., Doeswijk, R. Q., & Lam, T.W. Strategic asset allocation: Determining the optimal portfolio with ten asset classes”, Working Paper, October, 2009 https://doi.org/10.1016/j.jbankfin.2015.06.021
10. [10] H.M. Markowitz, Journal of finance (Portfolio selection), 1952; 7; 77-91. DOI: 10.1111/j.1540-6261.1952.tb01525.x.
11. [11] Stoyanova K., & Balabanov, T. A combination of Broyden-Fletcher-Goldfarb-Shanno (BFGS) and bisection method for solving portfolio optimization problems, International Conference on Engineering and Emerging Technologies (ICEET), Kuala Lumpur, Malaysia, 2022; 1-3, DOI: 10.1109/ICEET56468.2022.10007369
12. [12] Li, Y., Zhou, B. & Tan, Y., Portfolio optimization model with uncertain returns based on prospect theory. Complex Intell. Syst. 2022, DOI: 10.1007/s40747-021-00493-9.
13. [13] Golmakani H.R., & Fazel, M. Constrained portfolio selection using particle swarm optimization. Expert Systems with Applications, 2011; 38(7); 8327-8335. DOI: 10.22436/jmcs.010.02.01.
14. [14] Feng X., & Hayes, D. Vine-Copula based models for farmland portfolio management. Selected Paper Presented at the 2016 Agricultural and Applied Economics Association Annual Meeting, July 31-August 2, 2016, Boston, Massachusetts.
15. [15] Borissova D. & Dimitrova, Z. An integrated group decision-making approach considering uncertainty conditions, Business Information Systems, 2021; 1; 307–316. DOI: 10.52825/bis.v1i.52,
16. [16] Borissova, D., Dimitrova, Z., Naidenov, N., & Yoshinov, R. (2022). Integrated Approach to Assessing the Progress of Digital Transformation by Using Multiple Objective and Subjective Indicators. In: Guizzardi, R., Ralyté, J., Franch, X. (eds) Research Challenges in Information Science. RCIS 2022. Lecture Notes in Business Information Processing, vol 446. Springer, Cham. https://doi.org/10.1007/978-3-031-05760-1_37
17. [17] Chen, C., & Kwon, R. H. Robust portfolio selection for index tracking, Computers & operations research, 2012; 39; 829-837.
18. [18] Miettinen, K., Neittaanmäki, P., Mäkelä, M.M., & Périaux, J. Evolutionary Algorithms in Engineering and Computer Science, John Wiley & Sons, Ltd., Chichester, Weinheim, New York, Brisbane, Singapore, Toronto, 1999.
19. [19] Juszczuk, P., Kaliszewski, I., Miroforidis, J., & Podkopaev, D., Expected mean return-standard deviation efficient frontier approximation with low‐cardinality portfolios in the presence of the risk‐free asset, International Transactions in Operational Research. 2022; https://doi.org/10.1111/itor.13121.
20. [20] Stoyanova K., & Balabanov, T., Optimal selection of pharma stock portfolios using DEPSO, 24th International Carpathian Control Conference, Hungary, 2023; 419-422, DOI: 10.1109/ICCC57093.2023.10178900
21. [21] Bertsimas D. & Melvyn, S. The price of robustness. Operations Research, 2004; 52; 35–53. JSTOR, http://www.jstor.org/stable/30036559,
22. [22] Benotsmane, R., Dudás, L., & Kovács, G. Newly elaborated hybrid algorithm for optimization of robot arm’s trajectory in order to increase efficiency and provide sustainability in production, Sustainability, 2021; 13(15), 8193. DOI: 10.3390/su13158193.
23. [23] Balabanov, T. Fruit machine simulator with Excel input-output interface, Velbazhd Software LLC, https://github.com/VelbazhdSoftwareLLC/Fruit, 2021.