Abstract
Earning profits when investing in a stock exchange and avoiding losses has always been a priority for every investor. This is the main reason why the portfolio selection problem has been of great importance to obtain reasonable compensation between the rate of return and the risk. However, the portfolio problem has been extended by introducing real-world constraints, such as cardinality restriction to limit the number of portfolio assets and quantity constraints to limit the proportion of each portfolio asset within the inferior and superior limits. In this work, nine state-of-the-art multiobjective algorithms were used to solve an instance of the portfolio selection problem for the Mexican Stock Exchange. A comparative experimental study of the efficient frontiers obtained and the behavior of these algorithms when solving the problem instance is reported. Two statistical hypothesis tests were used to support the conclusions in the analysis of the experimental results.
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Acknowledgements
The authors would like to acknowledge the Consejo Nacional de Ciencia y Tecnología (CONACYT). Besides, they acknowledge the Laboratorio Nacional de Tecnologías de la Información (LaNTI) of the Instituto Tecnológico de Ciudad Madero for the access to the cluster. Also, Javier Alberto Rangel González thanks the scholarship 429340 received from CONACYT in his Ph.D.
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Huacuja, H.J.F., González, J.A.R., Solís, J.F., Lam, M.A.A., Rodríguez, L.M., Valadez, J.M.C. (2021). Analysis of the Efficient Frontier of the Portfolio Selection Problem Instance of the Mexican Capital Market. In: Castillo, O., Melin, P. (eds) Fuzzy Logic Hybrid Extensions of Neural and Optimization Algorithms: Theory and Applications. Studies in Computational Intelligence, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-68776-2_16
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