A Study on Neural Network Architecture Applied to the Prediction of Brazilian Stock Returns

Leonardo Felizardo and Afonso Pinto

Papers from arXiv.org

Abstract: In this paper we present a statistical analysis about the characteristics that we intend to influence in the performance of the neural networks in terms of assertiveness in the prediction of Brazilian stock returns. We created a population of architectures for analysis and extracted the sample that had the best assertive performance. It was verified how the characteristics of this sample stand out and affect the neural networks. In addition, we make inferences about what kind of influence the different architectures have on the performance of neural networks. In the study, the prediction of the return of a Brazilian stock traded on the stock exchange of S\~ao Paulo to measure the error committed by the different architectures of constructed neural networks. The results are promising and indicate that some aspects of the neural network architecture have a significant impact on the assertiveness of the model.

Date: 2019-01
New Economics Papers: this item is included in nep-big, nep-cmp and nep-fmk
References: Add references at CitEc
Citations:

Published in EnANPAD - http://www.anpad.org.br/~anpad/diversos/2017/enanpad/posters/2017_EnANPAD_POSTER_FIN2093.pdf

Downloads: (external link)
http://arxiv.org/pdf/1901.09143 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1901.09143

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().