Abstract
Computational finance has become one of the emerging application fields of metaheuristic algorithms. In particular, these optimization methods are quickly becoming the solving approach alternative when dealing with realistic versions of financial problems, such as the popular portfolio optimization problem (POP). This paper reviews the scientific literature on the use of metaheuristics for solving rich versions of the POP and illustrates, with a numerical example, the capacity of these methods to provide high-quality solutions to complex POPs in short computing times, which might be a desirable property of solving methods that support real-time decision making.
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Acknowledgments
This work has been partially supported with doctoral grants from the UOC, the Spanish Ministry of Economy and Competitiveness (grants TRA2013-48180-C3-3-P, TRA2015-71883-REDT) and FEDER. Likewise we want to acknowledge the support received by the Department of Universities, Research & Information Society of the Catalan Government (Grant 2014-CTP-00001).
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Doering, J., Juan, A.A., Kizys, R., Fito, A., Calvet, L. (2016). Solving Realistic Portfolio Optimization Problems via Metaheuristics: A Survey and an Example. In: León, R., Muñoz-Torres, M., Moneva, J. (eds) Modeling and Simulation in Engineering, Economics and Management. MS 2016. Lecture Notes in Business Information Processing, vol 254. Springer, Cham. https://doi.org/10.1007/978-3-319-40506-3_3
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