Data-driven decision-making and its application to the corporate cash management problem

Tesis llevada a cabo para conseguir el grado de Doctor por la Universidad Politécnica de Valencia--15-12-2017--Sobresaliente cum laude

Detalles Bibliográficos
Autor: Salas-Molina, Francisco
Tipo de recurso: tesis doctoral
Fecha de publicación:2017
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/197558
Acceso en línea:http://hdl.handle.net/10261/197558
Access Level:acceso abierto
Palabra clave:Cash management
Forecasting
Multiobjective
Data-driven decision-making.
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spelling Data-driven decision-making and its application to the corporate cash management problemSalas-Molina, FranciscoCash managementForecastingMultiobjectiveData-driven decision-making.Tesis llevada a cabo para conseguir el grado de Doctor por la Universidad Politécnica de Valencia--15-12-2017--Sobresaliente cum laudeThis thesis investigates the cash management problem from a multidimensional perspective. Cash management focuses on finding the balance between cash holdings and short-term investments. Typically, cash managers make decisions based usually on a firm’s optimal cash balance for operational and precautionary purposes. We here explore the opportunities for improved decision-making derived from modeling cash flow uncertainty with the help of data-driven procedures within a multiobjective context. On the one hand, cash managers may achieve cost savings by forecasting future cash flows. To this end, we perform an empirical analysis of daily cash flow time-series to take advantage of modern machine learning techniques as a key step to connect data analysis and optimization methods in cash management. On the other hand, cash managers may be interested not only in the cost but also in the risk associated to decision-making. Thus, we address the cash management problem from a multiobjective perspective focusing on both cost and risk. In addition, under the current situation of time-varying financial circumstances, the selection of cash management models according to operating conditions and its robustness are worth considering questions. We also show the utility of forecasts through a new cash management model which outperforms the state-of-the-art by guaranteeing optimal solutions. Since most firms usually deal with cash management systems with multiple accounts, we develop a framework to formulate and solve the multiple bank accounts cash management problem. Finally, in an attempt to fill the gap between theory and practice, we also provide a software library in Python for practitioners interested in building decision support systems for cash management.Peer reviewedUniversidad Politécnica de ValenciaCSIC - Instituto de Investigación en Inteligencia Artificial (IIIA)Rodríguez-Aguilar, Juan AntonioMartin, Francisco J.Díaz García, PabloConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202020202017info:eu-repo/semantics/doctoralThesishttp://purl.org/coar/resource_type/c_db06http://hdl.handle.net/10261/197558reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://dx.doi.org/10.4995/Thesis/10251/95408Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/1975582026-05-22T06:33:51Z
dc.title.none.fl_str_mv Data-driven decision-making and its application to the corporate cash management problem
title Data-driven decision-making and its application to the corporate cash management problem
spellingShingle Data-driven decision-making and its application to the corporate cash management problem
Salas-Molina, Francisco
Cash management
Forecasting
Multiobjective
Data-driven decision-making.
title_short Data-driven decision-making and its application to the corporate cash management problem
title_full Data-driven decision-making and its application to the corporate cash management problem
title_fullStr Data-driven decision-making and its application to the corporate cash management problem
title_full_unstemmed Data-driven decision-making and its application to the corporate cash management problem
title_sort Data-driven decision-making and its application to the corporate cash management problem
dc.creator.none.fl_str_mv Salas-Molina, Francisco
author Salas-Molina, Francisco
author_facet Salas-Molina, Francisco
author_role author
dc.contributor.none.fl_str_mv Rodríguez-Aguilar, Juan Antonio
Martin, Francisco J.
Díaz García, Pablo
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Cash management
Forecasting
Multiobjective
Data-driven decision-making.
topic Cash management
Forecasting
Multiobjective
Data-driven decision-making.
description Tesis llevada a cabo para conseguir el grado de Doctor por la Universidad Politécnica de Valencia--15-12-2017--Sobresaliente cum laude
publishDate 2017
dc.date.none.fl_str_mv 2017
2020
2020
dc.type.none.fl_str_mv info:eu-repo/semantics/doctoralThesis
http://purl.org/coar/resource_type/c_db06
format doctoralThesis
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/197558
url http://hdl.handle.net/10261/197558
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.4995/Thesis/10251/95408
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Universidad Politécnica de Valencia
CSIC - Instituto de Investigación en Inteligencia Artificial (IIIA)
publisher.none.fl_str_mv Universidad Politécnica de Valencia
CSIC - Instituto de Investigación en Inteligencia Artificial (IIIA)
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.81155

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