dc.contributor.author | Contreras Eitner, Alfredo | |
dc.date.accessioned | 2024-08-05T14:23:31Z | |
dc.date.available | 2024-08-05T14:23:31Z | |
dc.date.issued | 2023-03-01 | |
dc.identifier.issn | 1544-6123 | |
dc.identifier.issn | 1544-6131 | |
dc.identifier.uri | http://hdl.handle.net/10726/5530 | |
dc.language.iso | eng | |
dc.publisher | Elsevier Inc. | |
dc.title | Learning specialists and market resilience | eng |
dc.type | article | |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | |
dc.rights.local | Acceso Restringido | |
dc.type.version | info:eu-repo/semantics/acceptedVersion | |
dc.identifier.instname | instname:Colegio de Estudios Superiores de Administración – CESA | |
dc.identifier.reponame | reponame:Biblioteca Digital – CESA | |
dc.identifier.repourl | repourl:https://repository.cesa.edu.co/ | |
dc.description.abstractenglish | In this paper, I address the stochastic behavior of asset prices set by an imperfectly informed specialist who uses learning technology to refine her knowledge of the order flow. The specialist’s endogenous choice of an information structure is analyzed w | eng |
dc.type.coar | http://purl.org/coar/resource_type/c_2df8fbb1 | |
dc.relation.citationvolume | 52 | |
dc.contributor.orcid | Contreras Eitner, Alfredo [0000-0003-3041-3271] | |
dc.type.driver | info:eu-repo/semantics/article | |
dc.type.redcol | http://purl.org/redcol/resource_type/ART | |
dc.type.coarversion | http://purl.org/coar/version/c_71e4c1898caa6e32 | |
dc.contributor.scopus | Contreras Eitner, Alfredo [58080792300] | |
dc.description.orcid | https://orcid.org/0000-0003-3041-3271 | |
dc.description.scopus | https://www.scopus.com/authid/detail.uri?authorId=58080792300 | |
dc.relation.ispartofjournal | Finance Research Letters | |
dc.identifier.doi | https://doi-org.cvirtual.cesa.edu.co/10.1016/j.frl.2022.103516 | |
dc.subject.proposal | Market microstructure | |
dc.subject.proposal | Market resilience | |
dc.subject.proposal | Insider trading | |
dc.subject.proposal | Dynamic rational inattention | |
dc.subject.proposal | Entropy learning | |