Detalhes bibliográficos
| Ano de defesa: |
2011 |
| Autor(a) principal: |
Santos, Augusto Felippe Caramico dos
 |
| Orientador(a): |
Famá, Rubens |
| Banca de defesa: |
Não Informado pela instituição |
| Tipo de documento: |
Dissertação
|
| Tipo de acesso: |
Acesso aberto |
| Idioma: |
por |
| Instituição de defesa: |
Pontifícia Universidade Católica de São Paulo
|
| Programa de Pós-Graduação: |
Programa de Estudos Pós-Graduados em Administração
|
| Departamento: |
Faculdade de Economia, Administração, Contábeis e Atuariais
|
| País: |
BR
|
| Palavras-chave em Português: |
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| Palavras-chave em Inglês: |
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| Área do conhecimento CNPq: |
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| Link de acesso: |
https://tede2.pucsp.br/handle/handle/985
|
Resumo: |
A model was developed with the purpose to estimate a potential anticipation of the reversal of the short-term trend for Ibovespa, reducing the investor s risk exposure and seeking to increase its return through statistical techniques, like the Multiple Regression Analysis. Besides, the Artificial Neural Networks have been used to build an algorithm able to anticipate trends and forecast its reversal. The study was limited to the São Paulo Stock Exchange in its main index (Ibovespa), within the period between July 1994 and December 2009, taking into consideration only its value in points. In order to build the artificial model, historical information have been collected from the Brazilian, American, European and Asian markets. It was found that the error percentage of the model built through the Neural Network was 21.76%, which allows us to conclude that in 78.24% of the cases, the model proposed through the use of neural networks could accurately determine the existing relationship between the input variables. When a fictitious application was performed based on the market conditions above mentioned, a gross return of 65.37% was found for responses with unknown data, in comparison with 53.51% of Ibovespa for the same period. Therefore, it can be concluded that the developed model presented conditions to treat the unknown data in a satisfactory manner and reach an additional gain in relation to the market in the analyzed period |
