A survey on financial applications of metaheuristics
comunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/8648
comunitat-uji-handle3:10234/8649
comunitat-uji-handle4:
INVESTIGACIONMetadatos
Título
A survey on financial applications of metaheuristicsFecha de publicación
2017Editor
ACM (Association for Computing Machinery)ISSN
0360-0300Cita bibliográfica
SOLER-DOMINGUEZ, Amparo; JUAN, Angel A.; KIZYS, Renatas. A survey on financial applications of metaheuristics. ACM Computing Surveys (CSUR), 2017, vol. 50, no 1Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
http://dl.acm.org/citation.cfm?doid=3058791.3054133Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision-making in a number of fields, such as logistics and transportation, ... [+]
Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision-making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, and the like. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and real-life combinatorial optimization problems that arise in a number of financial and banking activities. This article reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. This article also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered. [-]
Publicado en
ACM Computing Surveys (CSUR), 2017, vol. 50, no 1Derechos de acceso
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