Abstract
Portfolio Selection (PS) is to allocate a given amount of investment fund across a set of assets in such a way that the return is maximized and the risk is minimized. PS is a challenging financial engineering problem and optimization problem. GA is well known for its effectiveness in solving optimization problems. However it may experience slow convergence especially when dealing with constrained optimization problems. To address this issue, we propose a variation of genetic algorithm (GA), which utilizes dual populations to solve PS problems. The first population is responsible for exploration in the search space, whilst the second one is for exploitation to speed up the convergence process. These two populations share individuals periodically. The proposed algorithm has been tested on the standard PS benchmark instances. The results reveal that our method can obtain very good results compared to the state of the art methods. More importantly, this dual population method is much faster than other methods.
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References
Markowitz, H.: Portfolio selection. The Journal of Finance 7(1), 77–91 (1952)
Chang, T.-J., Meade, N., Beasley, J.E., Sharaiha, Y.M.: Heuristics for cardinality constrained portfolio optimisation. Computers & Operations Research 27(13), 1271–1302 (2000)
Varian, H.: A portfolio of Nobel laureates: Markowitz, Miller and Sharpe. The Journal of Economic Perspectives 7(1), 159–169 (1993)
Markowitz, H.: Portfolio selection: efficient diversification of investments. John Wiley and Sons, New York (1959)
Gendreau, M., Potvin, J.-Y.: Handbook of metaheuristics. Springer (2010)
Deng, G.-F., Lin, W.-T., Lo, C.-C.: Markowitz-based portfolio selection with cardinality constraints using improved particle swarm optimization. Expert Systems with Applications 39(4), 4558–4566 (2012)
Sabar, N.R., Kendall, G.: Using Harmony Search with Multiple Pitch Adjustment Operators for the Portfolio Selection Problem. Paper presented at the In Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC 2014) (2014)
Kendall, G., Su, Y.: Imperfect evolutionary systems. IEEE Transactions on Evolutionary Computation 11(3), 294–307 (2007)
Fernández, A., Gómez, S.: Portfolio selection using neural networks. Computers & Operations Research 34(4), 1177–1191 (2007)
Holland, J.: Adaptation in Natural and Artificial Systems. MIT Press, Cambridge (1992)
Neri, F., Cotta, C.: Memetic algorithms and memetic computing optimization: A literature review. Swarm and Evolutionary Computation 2, 1–14 (2012)
Moral-Escudero, R., Ruiz-Torrubiano, R., Suarez, A.: Selection of optimal investment portfolios with cardinality constraints. Paper presented at the IEEE Congress on Evolutionary Computation, CEC 2006 (2006)
Beasley, J.E.: OR-Library: distributing test problems by electronic mail. Journal of the Operational Research Society 41(11), 1069–1072 (1990)
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Sabar, N.R., Song, A. (2014). Dual Population Genetic Algorithm for the Cardinality Constrained Portfolio Selection Problem. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_59
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DOI: https://doi.org/10.1007/978-3-319-13563-2_59
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13562-5
Online ISBN: 978-3-319-13563-2
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