Fitting random cash management models to data

[EN] Organizations use cash management models to control balances to both avoid overdrafts and obtain a profit from short-term investments. Most management models are based on control bounds which are derived from the assumption of a particular cash flow probability distribution. In this paper, we r...

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Detalhes bibliográficos
Autor: Salas-Molina, Francisco|||0000-0002-1168-7931
Tipo de documento: artigo
Data de publicação:2019
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositório:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglês
OAI Identifier:oai:riunet.upv.es:10251/201125
Acesso em linha:https://riunet.upv.es/handle/10251/201125
Access Level:Acceso aberto
Palavra-chave:Machine learning
Stochastic programming
Data-driven models
Ensembles
Control bounds
ECONOMIA FINANCIERA Y CONTABILIDAD
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spelling Fitting random cash management models to dataSalas-Molina, Francisco|||0000-0002-1168-7931Machine learningStochastic programmingData-driven modelsEnsemblesControl boundsECONOMIA FINANCIERA Y CONTABILIDAD[EN] Organizations use cash management models to control balances to both avoid overdrafts and obtain a profit from short-term investments. Most management models are based on control bounds which are derived from the assumption of a particular cash flow probability distribution. In this paper, we relax this strong assumption to fit cash management models to data by means of stochastic and linear programming. We also introduce ensembles of random cash management models which are built by randomly selecting a subsequence of the original cash flow data set. We illustrate our approach by means of a real case study showing that a small random sample of data is enough to fit sufficiently good bound-based models.ElsevierCentro de Investigación en Gestión de Empresas (CEGEA)Departamento de Economía y Ciencias SocialesEscuela Politécnica Superior de AlcoyRepositorio Institucional de la Universitat Politècnica de València Riunet20192019-06-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/201125reponame: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 - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2011252026-06-13T07:49:27Z
dc.title.none.fl_str_mv Fitting random cash management models to data
title Fitting random cash management models to data
spellingShingle Fitting random cash management models to data
Salas-Molina, Francisco|||0000-0002-1168-7931
Machine learning
Stochastic programming
Data-driven models
Ensembles
Control bounds
ECONOMIA FINANCIERA Y CONTABILIDAD
title_short Fitting random cash management models to data
title_full Fitting random cash management models to data
title_fullStr Fitting random cash management models to data
title_full_unstemmed Fitting random cash management models to data
title_sort Fitting random cash management models to data
dc.creator.none.fl_str_mv Salas-Molina, Francisco|||0000-0002-1168-7931
author Salas-Molina, Francisco|||0000-0002-1168-7931
author_facet Salas-Molina, Francisco|||0000-0002-1168-7931
author_role author
dc.contributor.none.fl_str_mv Centro de Investigación en Gestión de Empresas (CEGEA)
Departamento de Economía y Ciencias Sociales
Escuela Politécnica Superior de Alcoy
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Machine learning
Stochastic programming
Data-driven models
Ensembles
Control bounds
ECONOMIA FINANCIERA Y CONTABILIDAD
topic Machine learning
Stochastic programming
Data-driven models
Ensembles
Control bounds
ECONOMIA FINANCIERA Y CONTABILIDAD
description [EN] Organizations use cash management models to control balances to both avoid overdrafts and obtain a profit from short-term investments. Most management models are based on control bounds which are derived from the assumption of a particular cash flow probability distribution. In this paper, we relax this strong assumption to fit cash management models to data by means of stochastic and linear programming. We also introduce ensembles of random cash management models which are built by randomly selecting a subsequence of the original cash flow data set. We illustrate our approach by means of a real case study showing that a small random sample of data is enough to fit sufficiently good bound-based models.
publishDate 2019
dc.date.none.fl_str_mv 2019
2019-06-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/201125
url https://riunet.upv.es/handle/10251/201125
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 - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/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 - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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
repository.mail.fl_str_mv
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score 15,300719

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