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dc.contributor.authorMartín, Carlos
dc.contributor.authorQuintana, David
dc.contributor.authorIsasi, Pedro
dc.date2021-06
dc.date.accessioned2022-04-29T09:27:23Z
dc.date.available2022-04-29T09:27:23Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/12981
dc.description.abstractThe attainment of trading rules using Grammatical Evolution traditionally follows a static approach. A single rule is obtained and then used to generate investment recommendations over time. The main disadvantage of this approach is that it does not consider the need to adapt to the structural changes that are often associated with financial time series. We improve the canonical approach introducing an alternative that involves a dynamic selection mechanism that switches between an active rule and a candidate one optimized for the most recent market data available. The proposed solution seeks the flexibility required by structural changes while limiting the transaction costs commonly associated with constant model updates. The performance of the algorithm is compared with four alternatives: the standard static approach; a sliding window-based generation of trading rules that are used for a single time period, and two ensemble-based strategies. The experimental results, based on market data, show that the suggested approach beats the rest.es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligence (IJIMAI)es_ES
dc.relation.ispartofseries;vol. 6, nº 6
dc.relation.urihttps://www.ijimai.org/journal/bibcite/reference/2937es_ES
dc.rightsopenAccesses_ES
dc.subjectdynamic strategyes_ES
dc.subjectevolutionary computationes_ES
dc.subjectfinancees_ES
dc.subjectgrammatical evolutiones_ES
dc.subjectstructural changees_ES
dc.subjecttradinges_ES
dc.subjectIJIMAIes_ES
dc.titleDynamic Generation of Investment Recommendations Using Grammatical Evolutiones_ES
dc.typearticlees_ES
reunir.tag~IJIMAIes_ES
dc.identifier.doihttps://doi.org/10.9781/ijimai.2021.04.007


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