Stock returns forecasting for financial, food, industrial and services companies using neural networks and ARIMA-GARCH models Previsão de retornos de ações dos setores financeiro, de alimentos, industrial e de serviços, por meio de RNA e modelos arima-garch

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dc.contributor.authorDe Oliveira M.A.
dc.contributor.authorDe Avila Montini A.
dc.contributor.authorBergmann D.R.
dc.date.accessioned2024-03-13T01:37:50Z
dc.date.available2024-03-13T01:37:50Z
dc.date.issued2008
dc.description.abstract© 2008 Mackenzie Presbyterian University. All rights reserved.The main purpose of this work is realize stock returns forecasting for financial, food, industrial and services companies using feedforward neural networks trained with Levenberg-Marquardt algorithm and Arima-Garch models. In each area two time series was selected from Economatica. To the financial area, Bradesco and Itaú was analyzed, Perdigão and Sadia in the food sector, Marcopolo and Gerdau in the industrial area, finally Pão de Açúcar and Lojas Americanas in the services. The forecasting generated by the two techniques had similar performance implying no significant differences between them.
dc.description.firstpage130
dc.description.issuenumber1
dc.description.lastpage156
dc.description.volume9
dc.identifier.doi10.1590/S1678-69712008000100007
dc.identifier.issn1678-6971
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/37536
dc.relation.ispartofRevista de Administracao Mackenzie
dc.rightsAcesso Aberto
dc.subject.otherlanguageArima-Garch
dc.subject.otherlanguageForecasting
dc.subject.otherlanguageLevenberg-Marquardt algorithm
dc.subject.otherlanguageNeural networks
dc.subject.otherlanguageTime series
dc.titleStock returns forecasting for financial, food, industrial and services companies using neural networks and ARIMA-GARCH models Previsão de retornos de ações dos setores financeiro, de alimentos, industrial e de serviços, por meio de RNA e modelos arima-garch
dc.typeArtigo
local.scopus.citations0
local.scopus.eid2-s2.0-85099322954
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85099322954&origin=inward
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