Abstract
Portfolio optimization is a complex real-world problem where assets are selected such that profit is maximized while risk is simultaneously minimized. In recent years, nature-inspired algorithms have become a popular choice for efficiently identifying optimal portfolios. This paper introduces such an algorithm that, unlike previous algorithms, uses a set-based approach to reduce the dimensionality of the problem and to determine the appropriate budget al.location for each asset. The results show that the proposed approach is capable of obtaining good quality solutions, while being relatively fast.
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Chang, T., Meade, N., Beasley, J., Sharaiha, Y.: Heuristics for cardinality constrained portfolio optimisation. Comput. Oper. Res. 27(13), 1271–1302 (2000). https://doi.org/10.1016/S0305-0548(99)00074-X
Coello Coello, C.A., Reyes Sierra, M.: A study of the parallelization of a coevolutionary multi-objective evolutionary algorithm. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds.) MICAI 2004. LNCS (LNAI), vol. 2972, pp. 688–697. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24694-7_71
Eberhart, R., Kennedy, J.: A new optimizer using particle swarm theory. In: MHS 1995. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp. 39–43, October 1995. https://doi.org/10.1109/MHS.1995.494215
Franken, N.: Visual exploration of algorithm parameter space. In: 2009 IEEE Congress on Evolutionary Computation, pp. 389–398 (2009)
Kalayci, C.B., Ertenlice, O., Akbay, M.A.: A comprehensive review of deterministic models and applications for mean-variance portfolio optimization. Expert Syst. Appl. 125, 345–368 (2019). https://doi.org/10.1016/j.eswa.2019.02.011
Langeveld, J., Engelbrecht, A.P.: Set-based particle swarm optimization applied to the multidimensional knapsack problem. Swarm Intell. 6(4), 297–342 (2012). https://doi.org/10.1007/s11721-012-0073-4
Markowitz, H.: Portfolio selection. J. Financ. 7(1), 77–91 (1952). https://doi.org/10.1111/j.1540-6261.1952.tb01525.x
Shi, Y., Eberhart, R.: A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 69–73 (1998). https://doi.org/10.1109/ICEC.1998.699146
Veldhuizen, D.A.V., Lamont, G.B.: Multiobjective evolutionary algorithm research: a history and analysis. Technical reports, Department of Electrical and Computer Engineering. Graduate School of Engineering, Air Force Inst Technol, Wright Patterson, Technical Report TR-98-03 (1998)
Woodside-Oriakhi, M., Lucas, C., Beasley, J.: Heuristic algorithms for the cardinality constrained efficient frontier. Eur. J. Oper. Res. 213, 538–550 (2011). https://doi.org/10.1016/j.ejor.2011.03.030
Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C.M., da Fonseca, V.G.: Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans. Evol. Comput. 7(2), 117–132 (2003). https://doi.org/10.1109/TEVC.2003.810758
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The authors acknowledge the Centre for High Performance Computing (CHPC), South Africa, for providing computational resources to this research project.
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Erwin, K., Engelbrecht, A.P. (2020). Set-Based Particle Swarm Optimization for Portfolio Optimization. In: Dorigo, M., et al. Swarm Intelligence. ANTS 2020. Lecture Notes in Computer Science(), vol 12421. Springer, Cham. https://doi.org/10.1007/978-3-030-60376-2_28
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DOI: https://doi.org/10.1007/978-3-030-60376-2_28
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