Comparative Study Of Artificial Neural Network And Box-Jenkins Arima For Stock Price Indexes
Autor(a) principal: | |
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Data de Publicação: | 2008 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/1472 |
Resumo: | The accuracy in forecasting financial time series, such as stock price indexes, has focused a great deal of attention nowadays. Conventionally, the Box-Jenkins autoregressive integrated moving average (ARIMA) models have been one of the most widely used linear models in time series forecasting. Recent research suggests that artificial neural networks (ANN) can be a promising alternative to the traditional ARIMA structure in forecasting. This thesis aims to study the efficiency of ARIMA and ANN models for forecasting the value of four Stock Price Indexes, of four different countries (Germany, Italy, Greece and Portugal), during 2006 – 2007, using the data from preceding 15 years. In order to reach the goal of this study, it is used the Eviews software that allows to find an appropriate ARIMA specification, offered also a powerful evaluation, testing and forecasting tools. In order to predict the time series is used the Matlab software, which provides a package that allows generating a suitable ANN model. It is found that ANN provides forecasted results closest to the actual ones when used the logarithmic transformation. The first difference transformation is required in ARIMA but no one founding model is satisfactory. When this transformation is also used with ANN, the forecasted results are less satisfactory. In fact, it wasn’t possible to compare the efficiency of ARIMA and ANN models for forecasting the time series, due to the founding ARIMA models were not satisfactory. A possible solution would be to reduced the input period of 15 years. |
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Comparative Study Of Artificial Neural Network And Box-Jenkins Arima For Stock Price IndexesARIMA modelsArtificial neural networksBackpropagation algorithmStock price index forecastingModelos ARIMARedes Neuronais ArtificiaisAlgoritmo BackpropagationPrevisão de Índices AccionistasThe accuracy in forecasting financial time series, such as stock price indexes, has focused a great deal of attention nowadays. Conventionally, the Box-Jenkins autoregressive integrated moving average (ARIMA) models have been one of the most widely used linear models in time series forecasting. Recent research suggests that artificial neural networks (ANN) can be a promising alternative to the traditional ARIMA structure in forecasting. This thesis aims to study the efficiency of ARIMA and ANN models for forecasting the value of four Stock Price Indexes, of four different countries (Germany, Italy, Greece and Portugal), during 2006 – 2007, using the data from preceding 15 years. In order to reach the goal of this study, it is used the Eviews software that allows to find an appropriate ARIMA specification, offered also a powerful evaluation, testing and forecasting tools. In order to predict the time series is used the Matlab software, which provides a package that allows generating a suitable ANN model. It is found that ANN provides forecasted results closest to the actual ones when used the logarithmic transformation. The first difference transformation is required in ARIMA but no one founding model is satisfactory. When this transformation is also used with ANN, the forecasted results are less satisfactory. In fact, it wasn’t possible to compare the efficiency of ARIMA and ANN models for forecasting the time series, due to the founding ARIMA models were not satisfactory. A possible solution would be to reduced the input period of 15 years.Actualmente a precisão na previsão de séries financeiras, tais como Índices Accionistas, têm captado uma enorme atenção. Tradicionalmente, o modelo Box-Jenkins Autorregressivos Integrados de Médias Móveis (ARIMA) é um dos modelos lineares mais utilizados na previsão de séries temporais. Pesquisas recentes têm demonstrado que as Redes Neuronais Artificiais (RNA) podem constituir uma potencial alternativa à tradicional estrutura ARIMA, na previsão. Esta tese tem por objectivo o estudo da eficiência dos ARIMA e dos modelos de RNA na previsão de quarto índices accionistas de quatro diferentes países (Alemanha, Itália, Grécia e Portugal), desde 2006 a 2007, considerando os 15 anos antecedentes. De modo a atingir este objectivo, foram utilizados dois softwares. Para determinar uma especificação apropriada para os modelos ARIMA foi utilizado o software Eviews que dispõe, também, de ferramentas poderosas para avaliar e testar os modelos, possibilitando ainda a previsão através dos mesmos. De forma a encontrar modelos RNA apropriados, para prever as séries em estudo, foi utilizado o software Matlab. As RNA forneceram uma boa precisão na previsão das quatro séries logaritmizadas. Uma vez que os modelos ARIMA requerem estacionaridade das séries, foram utilizadas as séries das primeiras diferenças, no entanto não foi encontrado nenhum modelo que pudesse fornecer uma previsão aceitável. Considerando as séries temporais diferenciadas nas RNA, os resultados da previsão foram menos satisfatórios. De facto, não foi possível comparar a eficiência dos modelos na previsão dos índices, uma vez que os modelos ARIMA encontrados não foram satisfatórios. Uma hipótese, na tentativa de encontrar modelos satisfatórios seria reduzir o intervalo de 15 anos de input.2009-05-04T15:00:11Z2009-05-04T00:00:00Z2009-05-042008info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfapplication/octet-streamhttp://hdl.handle.net/10071/1472porCancela, Ângela Mar isa Roldãoinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-09T17:35:53Zoai:repositorio.iscte-iul.pt:10071/1472Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:16:15.667364Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Comparative Study Of Artificial Neural Network And Box-Jenkins Arima For Stock Price Indexes |
title |
Comparative Study Of Artificial Neural Network And Box-Jenkins Arima For Stock Price Indexes |
spellingShingle |
Comparative Study Of Artificial Neural Network And Box-Jenkins Arima For Stock Price Indexes Cancela, Ângela Mar isa Roldão ARIMA models Artificial neural networks Backpropagation algorithm Stock price index forecasting Modelos ARIMA Redes Neuronais Artificiais Algoritmo Backpropagation Previsão de Índices Accionistas |
title_short |
Comparative Study Of Artificial Neural Network And Box-Jenkins Arima For Stock Price Indexes |
title_full |
Comparative Study Of Artificial Neural Network And Box-Jenkins Arima For Stock Price Indexes |
title_fullStr |
Comparative Study Of Artificial Neural Network And Box-Jenkins Arima For Stock Price Indexes |
title_full_unstemmed |
Comparative Study Of Artificial Neural Network And Box-Jenkins Arima For Stock Price Indexes |
title_sort |
Comparative Study Of Artificial Neural Network And Box-Jenkins Arima For Stock Price Indexes |
author |
Cancela, Ângela Mar isa Roldão |
author_facet |
Cancela, Ângela Mar isa Roldão |
author_role |
author |
dc.contributor.author.fl_str_mv |
Cancela, Ângela Mar isa Roldão |
dc.subject.por.fl_str_mv |
ARIMA models Artificial neural networks Backpropagation algorithm Stock price index forecasting Modelos ARIMA Redes Neuronais Artificiais Algoritmo Backpropagation Previsão de Índices Accionistas |
topic |
ARIMA models Artificial neural networks Backpropagation algorithm Stock price index forecasting Modelos ARIMA Redes Neuronais Artificiais Algoritmo Backpropagation Previsão de Índices Accionistas |
description |
The accuracy in forecasting financial time series, such as stock price indexes, has focused a great deal of attention nowadays. Conventionally, the Box-Jenkins autoregressive integrated moving average (ARIMA) models have been one of the most widely used linear models in time series forecasting. Recent research suggests that artificial neural networks (ANN) can be a promising alternative to the traditional ARIMA structure in forecasting. This thesis aims to study the efficiency of ARIMA and ANN models for forecasting the value of four Stock Price Indexes, of four different countries (Germany, Italy, Greece and Portugal), during 2006 – 2007, using the data from preceding 15 years. In order to reach the goal of this study, it is used the Eviews software that allows to find an appropriate ARIMA specification, offered also a powerful evaluation, testing and forecasting tools. In order to predict the time series is used the Matlab software, which provides a package that allows generating a suitable ANN model. It is found that ANN provides forecasted results closest to the actual ones when used the logarithmic transformation. The first difference transformation is required in ARIMA but no one founding model is satisfactory. When this transformation is also used with ANN, the forecasted results are less satisfactory. In fact, it wasn’t possible to compare the efficiency of ARIMA and ANN models for forecasting the time series, due to the founding ARIMA models were not satisfactory. A possible solution would be to reduced the input period of 15 years. |
publishDate |
2008 |
dc.date.none.fl_str_mv |
2008 2009-05-04T15:00:11Z 2009-05-04T00:00:00Z 2009-05-04 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10071/1472 |
url |
http://hdl.handle.net/10071/1472 |
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por |
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por |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf application/octet-stream |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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