Unlocking the Potential of Machine Learning in Portfolio Selection: A Hybrid Approach with Genetic Optimization

[EN] In the field of financial market predictions, machine learning has been widely used to identify patterns and gain valuable insights. However, for success in portfolio selection, it is crucial to optimize factors that impact accuracy. This study focuses on combining machine learning and optimiza...

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Detalles Bibliográficos
Autor: Alzaman, Chaher
Tipo de recurso: capítulo de libro
Fecha de publicación:2024
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/208398
Acceso en línea:https://riunet.upv.es/handle/10251/208398
Access Level:acceso abierto
Palabra clave:Artificial Intelligence
Machine Learning
Optimization
Financial Markets
Predictive Analytics
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spelling Unlocking the Potential of Machine Learning in Portfolio Selection: A Hybrid Approach with Genetic OptimizationAlzaman, ChaherArtificial IntelligenceMachine LearningOptimizationFinancial MarketsPredictive Analytics[EN] In the field of financial market predictions, machine learning has been widely used to identify patterns and gain valuable insights. However, for success in portfolio selection, it is crucial to optimize factors that impact accuracy. This study focuses on combining machine learning and optimization to enhance stock selection and prediction capabilities. The work starts with hyperparameter optimization and utilizes three different machine learning algorithms: XGBoost, LSTM, and Deep RankNet. Our findings show a 40% improvement in results through the use of a genetic-based optimization technique, as well as a promising daily average return of 0.47% through a novel feature engineering approach. The study provides a framework for optimizing and learning in financial portfolio selection, with promising results for medium and small-sized traders.Editorial Universitat Politècnica de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20242024-07-16book parthttp://purl.org/coar/resource_type/c_3248VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/bookPartapplication/pdfhttps://riunet.upv.es/handle/10251/208398reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Compartir igual (by-nc-sa) http://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2083982026-06-13T07:49:27Z
dc.title.none.fl_str_mv Unlocking the Potential of Machine Learning in Portfolio Selection: A Hybrid Approach with Genetic Optimization
title Unlocking the Potential of Machine Learning in Portfolio Selection: A Hybrid Approach with Genetic Optimization
spellingShingle Unlocking the Potential of Machine Learning in Portfolio Selection: A Hybrid Approach with Genetic Optimization
Alzaman, Chaher
Artificial Intelligence
Machine Learning
Optimization
Financial Markets
Predictive Analytics
title_short Unlocking the Potential of Machine Learning in Portfolio Selection: A Hybrid Approach with Genetic Optimization
title_full Unlocking the Potential of Machine Learning in Portfolio Selection: A Hybrid Approach with Genetic Optimization
title_fullStr Unlocking the Potential of Machine Learning in Portfolio Selection: A Hybrid Approach with Genetic Optimization
title_full_unstemmed Unlocking the Potential of Machine Learning in Portfolio Selection: A Hybrid Approach with Genetic Optimization
title_sort Unlocking the Potential of Machine Learning in Portfolio Selection: A Hybrid Approach with Genetic Optimization
dc.creator.none.fl_str_mv Alzaman, Chaher
author Alzaman, Chaher
author_facet Alzaman, Chaher
author_role author
dc.contributor.none.fl_str_mv Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Artificial Intelligence
Machine Learning
Optimization
Financial Markets
Predictive Analytics
topic Artificial Intelligence
Machine Learning
Optimization
Financial Markets
Predictive Analytics
description [EN] In the field of financial market predictions, machine learning has been widely used to identify patterns and gain valuable insights. However, for success in portfolio selection, it is crucial to optimize factors that impact accuracy. This study focuses on combining machine learning and optimization to enhance stock selection and prediction capabilities. The work starts with hyperparameter optimization and utilizes three different machine learning algorithms: XGBoost, LSTM, and Deep RankNet. Our findings show a 40% improvement in results through the use of a genetic-based optimization technique, as well as a promising daily average return of 0.47% through a novel feature engineering approach. The study provides a framework for optimizing and learning in financial portfolio selection, with promising results for medium and small-sized traders.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-07-16
dc.type.none.fl_str_mv book part
http://purl.org/coar/resource_type/c_3248
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/bookPart
format bookPart
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/208398
url https://riunet.upv.es/handle/10251/208398
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Compartir igual (by-nc-sa)
http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Compartir igual (by-nc-sa)
http://creativecommons.org/licenses/by-nc-sa/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Editorial Universitat Politècnica de València
publisher.none.fl_str_mv Editorial Universitat Politècnica de València
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
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