Stock Returns Forecast : An Examination By Means of Artificial Neural Networks

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 th...

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Detalles Bibliográficos
Autores principales: Caride, Martín Iglesias, Bariviera, Aurelio F., Lanzarini, Laura Cristina
Formato: Articulo Preprint
Lenguaje:Inglés
Publicado: 2017
Materias:
Ciencias Informáticas
Informática
Efficient Market Hypothesis
Artificial Neural Networks
Bovespa index
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/125231
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-125231
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Ciencias Informáticas
Informática
Efficient Market Hypothesis
Artificial Neural Networks
Bovespa index
spellingShingle Ciencias Informáticas
Informática
Efficient Market Hypothesis
Artificial Neural Networks
Bovespa index
Caride, Martín Iglesias
Bariviera, Aurelio F.
Lanzarini, Laura Cristina
Stock Returns Forecast : An Examination By Means of Artificial Neural Networks
topic_facet Ciencias Informáticas
Informática
Efficient Market Hypothesis
Artificial Neural Networks
Bovespa index
description 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.
format Articulo
Preprint
author Caride, Martín Iglesias
Bariviera, Aurelio F.
Lanzarini, Laura Cristina
author_facet Caride, Martín Iglesias
Bariviera, Aurelio F.
Lanzarini, Laura Cristina
author_sort Caride, Martín Iglesias
title Stock Returns Forecast : An Examination By Means of Artificial Neural Networks
title_short Stock Returns Forecast : An Examination By Means of Artificial Neural Networks
title_full Stock Returns Forecast : An Examination By Means of Artificial Neural Networks
title_fullStr Stock Returns Forecast : An Examination By Means of Artificial Neural Networks
title_full_unstemmed Stock Returns Forecast : An Examination By Means of Artificial Neural Networks
title_sort stock returns forecast : an examination by means of artificial neural networks
publishDate 2017
url http://sedici.unlp.edu.ar/handle/10915/125231
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AT lanzarinilauracristina stockreturnsforecastanexaminationbymeansofartificialneuralnetworks
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