- -

Selecting cash management models from a multiobjective perspective

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Selecting cash management models from a multiobjective perspective

Mostrar el registro sencillo del ítem

Ficheros en el ítem

Thumbnail
Nombre: Salas-Molina;Rodr ...
Tamaño: 386.8Kb
Formato: PDF
Descripción: Versión del Autor.
Abrir
[Cerrado]
Nombre: 10.1007/s10479-01 ...
Tamaño: 701.6Kb
Formato: PDF
Descripción: Versión editorial
Solicitar una copia al autor
dc.contributor.author Salas-Molina, Francisco es_ES
dc.contributor.author Rodríguez-Aguilar, Juan A. es_ES
dc.contributor.author Díaz-García, Pablo es_ES
dc.date.accessioned 2020-10-22T03:32:13Z
dc.date.available 2020-10-22T03:32:13Z
dc.date.issued 2018-02 es_ES
dc.identifier.issn 0254-5330 es_ES
dc.identifier.uri http://hdl.handle.net/10251/152806
dc.description.abstract [EN] This paper addresses the problem of selecting cash management models under different operating conditions from a multiobjective perspective considering not only cost but also risk. A number of models have been proposed to optimize corporate cash management policies. The impact on model performance of different operating conditions becomes an important issue. Here, we provide a range of visual and quantitative tools imported from Receiver Operating Characteristic (ROC) analysis. More precisely, we show the utility of ROC analysis from a triple perspective as a tool for: (1) showing model performance; (2) choosingmodels; and (3) assessing the impact of operating conditions on model performance. We illustrate the selection of cash management models by means of a numerical example. es_ES
dc.description.sponsorship Work partially funded by projects Collectiveware TIN2015-66863-C2-1-R (MINECO/FEDER) and 2014 SGR 118. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Annals of Operations Research es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Cash management models es_ES
dc.subject ROC analysis es_ES
dc.subject Multiobjective es_ES
dc.subject Operating condition.I es_ES
dc.subject.classification INGENIERIA TEXTIL Y PAPELERA es_ES
dc.title Selecting cash management models from a multiobjective perspective es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s10479-017-2634-9 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//TIN2015-66863-C2-1-R/ES/COLLECTIVEWARE: TECNOLOGIAS PARA POTENCIAR COLECTIVOS HUMANOS EN LA RED ELECTRICA INTELIGENTE/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Generalitat de Catalunya//2014 SGR 118/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Textil y Papelera - Departament d'Enginyeria Tèxtil i Paperera es_ES
dc.description.bibliographicCitation Salas-Molina, F.; Rodríguez-Aguilar, JA.; Díaz-García, P. (2018). Selecting cash management models from a multiobjective perspective. Annals of Operations Research. 261(1-2):275-288. https://doi.org/10.1007/s10479-017-2634-9 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s10479-017-2634-9 es_ES
dc.description.upvformatpinicio 275 es_ES
dc.description.upvformatpfin 288 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 261 es_ES
dc.description.issue 1-2 es_ES
dc.relation.pasarela S\342318 es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.contributor.funder Generalitat de Catalunya es_ES
dc.description.references Ballestero, E. (2007). Compromise programming: A utility-based linear-quadratic composite metric from the trade-off between achievement and balanced (non-corner) solutions. European Journal of Operational Research, 182(3), 1369–1382. es_ES
dc.description.references Ballestero, E., & Romero, C. (1998). Multiple criteria decision making and its applications to economic problems. Berlin: Springer. es_ES
dc.description.references Bi, J., & Bennett, K. P. (2003). Regression error characteristic curves. In Proceedings of the 20th international conference on machine learning (ICML-03), pp. 43–50. es_ES
dc.description.references Bradley, A. P. (1997). The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145–1159. es_ES
dc.description.references da Costa Moraes, M. B., Nagano, M. S., & Sobreiro, V. A. (2015). Stochastic cash flow management models: A literature review since the 1980s. In Decision models in engineering and management (pp. 11–28). New York: Springer. es_ES
dc.description.references Doumpos, M., & Zopounidis, C. (2007). Model combination for credit risk assessment: A stacked generalization approach. Annals of Operations Research, 151(1), 289–306. es_ES
dc.description.references Drummond, C., & Holte, R. C. (2000). Explicitly representing expected cost: An alternative to roc representation. In Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 98–207). New York: ACM. es_ES
dc.description.references Drummond, C., & Holte, R. C. (2006). Cost curves: An improved method for visualizing classifier performance. Machine Learning, 65(1), 95–130. es_ES
dc.description.references Elkan, C. (2001). The foundations of cost-sensitive learning. In International joint conference on artificial intelligence (Vol. 17, pp. 973–978). Lawrence Erlbaum associates Ltd. es_ES
dc.description.references Fawcett, T. (2006). An introduction to roc analysis. Pattern Recognition Letters, 27(8), 861–874. es_ES
dc.description.references Flach, P. A. (2003). The geometry of roc space: understanding machine learning metrics through roc isometrics. In Proceedings of the 20th international conference on machine learning (ICML-03), pp. 194–201. es_ES
dc.description.references Garcia-Bernabeu, A., Benito, A., Bravo, M., & Pla-Santamaria, D. (2016). Photovoltaic power plants: a multicriteria approach to investment decisions and a case study in western spain. Annals of Operations Research, 245(1–2), 163–175. es_ES
dc.description.references Glasserman, P. (2003). Monte Carlo methods in financial engineering (Vol. 53). New York: Springer. es_ES
dc.description.references Gregory, G. (1976). Cash flow models: a review. Omega, 4(6), 643–656. es_ES
dc.description.references Hernández-Orallo, J. (2013). Roc curves for regression. Pattern Recognition, 46(12), 3395–3411. es_ES
dc.description.references Hernández-Orallo, J., Flach, P., & Ferri, C. (2013). Roc curves in cost space. Machine Learning, 93(1), 71–91. es_ES
dc.description.references Hernández-Orallo, J., Lachiche, N., & Martınez-Usó, A. (2014). Predictive models for multidimensional data when the resolution context changes. In Workshop on learning over multiple contexts at ECML, volume 2014. es_ES
dc.description.references Metz, C. E. (1978). Basic principles of roc analysis. In Seminars in nuclear medicine (Vol. 8, pp. 283–298). Amsterdam: Elsevier. es_ES
dc.description.references Miettinen, K. (2012). Nonlinear multiobjective optimization (Vol. 12). Berlin: Springer. es_ES
dc.description.references Ringuest, J. L. (2012). Multiobjective optimization: Behavioral and computational considerations. Berlin: Springer. es_ES
dc.description.references Ross, S. A., Westerfield, R., & Jordan, B. D. (2002). Fundamentals of corporate finance (sixth ed.). New York: McGraw-Hill. es_ES
dc.description.references Salas-Molina, F., Pla-Santamaria, D., & Rodriguez-Aguilar, J. A. (2016). A multi-objective approach to the cash management problem. Annals of Operations Research, pp. 1–15. es_ES
dc.description.references Srinivasan, V., & Kim, Y. H. (1986). Deterministic cash flow management: State of the art and research directions. Omega, 14(2), 145–166. es_ES
dc.description.references Steuer, R. E., Qi, Y., & Hirschberger, M. (2007). Suitable-portfolio investors, nondominated frontier sensitivity, and the effect of multiple objectives on standard portfolio selection. Annals of Operations Research, 152(1), 297–317. es_ES
dc.description.references Stone, B. K. (1972). The use of forecasts and smoothing in control limit models for cash management. Financial Management, 1(1), 72. es_ES
dc.description.references Torgo, L. (2005). Regression error characteristic surfaces. In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining (pp. 697–702). ACM. es_ES
dc.description.references Yu, P.-L. (1985). Multiple criteria decision making: concepts, techniques and extensions. New York: Plenum Press. es_ES
dc.description.references Zeleny, M. (1982). Multiple criteria decision making. New York: McGraw-Hill. es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem