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An evaluation of equity premium prediction using multiple kernel learning with financial features
dc.contributor.author | Arratia Quesada, Argimiro Alejandro |
dc.contributor.author | Belanche Muñoz, Luis Antonio |
dc.contributor.author | Fábregues de los Santos, Luis |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.date.accessioned | 2019-02-13T16:03:10Z |
dc.date.available | 2020-01-09T01:26:22Z |
dc.date.issued | 2020-08 |
dc.identifier.citation | Arratia, A.; Belanche, L.; Fábregues, L. An evaluation of equity premium prediction using multiple kernel learning with financial features. "Neural processing letters (Online)", vol. 52, no 1, Agost 2020, p. 117–134. |
dc.identifier.issn | 1573-773X |
dc.identifier.uri | http://hdl.handle.net/2117/129068 |
dc.description.abstract | This paper introduces and extensively explores a forecasting procedure based on multivariate dynamic kernels to re-examine –under a non-linearframework– the experimental tests reported by Welch and Goyal (Review of Financial Studies 21(4),1455-1508, 2008) showing that several variables proposed in the finance literature are of no use as exogenous information to predict the equity premium under linear regressions. For this non-linear approach to equity premium forecasting, kernel functions for time series are used with multiple kernel learning(MKL) in order to represent the relative importance of each of the variables. We find that, in general, the predictive capabilities of the MKL models do not improve consistently with the use of some or all of the variables, nor does the predictability by single kernels, as determined by different resampling procedures that we implement and compare. This fact tends to corroborate the instability already observed by Welch and Goyal for the predictive power of exogenous variables, now in a non-linear modelling framework |
dc.format.extent | 18 p. |
dc.language.iso | eng |
dc.subject | Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica |
dc.subject.lcsh | Finance -- Econometric models |
dc.subject.other | Support vector classification |
dc.subject.other | Support vector regression |
dc.subject.other | Financial time series |
dc.subject.other | Multiple kernel learning |
dc.subject.other | Kernel functions for time series |
dc.title | An evaluation of equity premium prediction using multiple kernel learning with financial features |
dc.type | Article |
dc.subject.lemac | Finances -- Models economètrics |
dc.contributor.group | Universitat Politècnica de Catalunya. LARCA - Laboratori d'Algorísmia Relacional, Complexitat i Aprenentatge |
dc.identifier.doi | 10.1007/s11063-018-09971-7 |
dc.description.peerreviewed | Peer Reviewed |
dc.subject.ams | Classificació AMS::62 Statistics::62P Applications |
dc.relation.publisherversion | https://link.springer.com/article/10.1007%2Fs11063-018-09971-7 |
dc.rights.access | Open Access |
local.identifier.drac | 23637118 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-89244-R/ES/GESTION Y ANALISIS DE DATOS COMPLEJOS/ |
local.citation.author | Arratia, A.; Belanche, Ll.; Fábregues, L. |
local.citation.publicationName | Neural processing letters (Online) |
local.citation.volume | 52 |
local.citation.number | 1 |
local.citation.startingPage | 117 |
local.citation.endingPage | 134 |
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