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dc.contributor.authorFry, John
dc.contributor.authorGriguta, V.
dc.contributor.authorGerber, L.
dc.contributor.authorSlater-Petty, H.
dc.contributor.authorCrockett, K.
dc.date.accessioned2021-05-24T17:40:55Z
dc.date.accessioned2021-06-03T12:02:41Z
dc.date.available2021-05-24T17:40:55Z
dc.date.available2021-06-03T12:02:41Z
dc.date.issued2021
dc.identifier.citationFry John, Griguta V, Gerber L, Slater-Petty H and Crockett K (2021) Modelling corporate bank accounts. Economics Letters. 205: 109924en_US
dc.identifier.urihttp://hdl.handle.net/10454/18503
dc.descriptionyesen_US
dc.description.abstractWe discuss the modelling of corporate bank accounts using a proprietary dataset. We thus offer a principled treatment of a genuine industrial problem. The corporate bank accounts in our study constitute spare, irregularly-spaced time series that may take both positive and negative values. We thus builds on previous models where the underlying is real-valued. We describe an intra-monthly effect identified by practitioners whereby account uncertainty is typically lowest at the beginning and end of each month and highest in the middle. However, our theory also allows for the opposite effect to occur. In-sample applications demonstrate the statistical significance of the hypothesised monthly effect. Out-of-sample forecasting applications offer a 9% improvement compared to a standard SARIMA approach.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1016/j.econlet.2021.109924en_US
dc.rights© 2021 Elsevier. Reproduced in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license.en_US
dc.subjectCorporate bank accountsen_US
dc.subjectFin techen_US
dc.subjectForecasting applicationsen_US
dc.subjectMachine learningen_US
dc.titleModelling corporate bank accountsen_US
dc.status.refereedyesen_US
dc.typeArticleen_US
dc.date.EndofEmbargo2023-05-24
dc.type.versionAccepted manuscripten_US
dc.description.publicnotesThe full text will be available at the end of the publisher's embargo: 24th May 2023en
dc.date.updated2021-05-24T16:41:02Z
refterms.dateFOA2021-06-03T12:03:42Z
dc.openaccess.statusGreenen_US


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