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dc.date.accessioned 2021-09-21T13:18:36Z
dc.date.available 2021-09-21T13:18:36Z
dc.date.issued 2017
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/125231
dc.description.abstract The validity of the Efficient Market Hypothesis has been under severe scrutiny since several decades. However, the evidence against it is not conclusive. Artificial Neural Networks provide a model-free means to analize the prediction power of past returns on current returns. This chapter analizes the predictability in the intraday Brazilian stock market using a backpropagation Artificial Neural Network. We selected 20 stocks from Bovespa index, according to different market capitalization, as a proxy for stock size. We find that predictability is related to capitalization. In particular, larger stocks are less predictable than smaller ones. en
dc.format.extent 399-410 es
dc.language en es
dc.subject Efficient Market Hypothesis es
dc.subject Artificial Neural Networks es
dc.subject Bovespa index es
dc.title Stock Returns Forecast en
dc.type Articulo es
sedici.identifier.other arXiv:1801.07960 es
sedici.identifier.other doi:10.1007/978-3-319-69989-9_23 es
sedici.identifier.issn 2198-4182 es
sedici.identifier.issn 2198-4190 es
sedici.title.subtitle An Examination By Means of Artificial Neural Networks en
sedici.creator.person Caride, Martín Iglesias es
sedici.creator.person Bariviera, Aurelio F. es
sedici.creator.person Lanzarini, Laura Cristina es
sedici.description.note Parte de Berger-Vachon, C.; Gil Lafuente, A. M.; Kacprzyk, J.; Kondratenko, Y.; Merigó, J. M.; Morabito, F. C. (eds.) (2018). Complex Systems: Solutions and Challenges in Economics, Management and Engineering. Cham: Springer. es
sedici.subject.materias Ciencias Informáticas es
sedici.subject.materias Informática es
sedici.description.fulltext true es
mods.originInfo.place Instituto de Investigación en Informática es
sedici.subtype Preprint es
sedici.rights.license Creative Commons Attribution 4.0 International (CC BY 4.0)
sedici.rights.uri http://creativecommons.org/licenses/by/4.0/
sedici.description.peerReview peer-review es
sedici.relation.isRelatedWith https://doi.org/10.1007/978-3-319-69989-9 es


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Creative Commons Attribution 4.0 International (CC BY 4.0) Excepto donde se diga explícitamente, este item se publica bajo la siguiente licencia Creative Commons Attribution 4.0 International (CC BY 4.0)