Publicação

Comparative study of artificial neural network and Box-Jenkins Arima for Stock Price Indexes

Ver documento

Detalhes bibliográficos
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.
Autores principais:Cancela, Ângela Marisa Roldão
Assunto:ARIMA models Artificial neural networks Backpropagation algorithm Stock price index forecasting Modelos ARIMA Redes neuronais artificiais Algoritmo Backpropagation Previsão de índices acionistas
Ano:2009
País:Portugal
Tipo de documento:dissertação de mestrado
Tipo de acesso:acesso aberto
Instituição associada:ISCTE
Idioma:português
Origem:Repositório ISCTE

Registos relacionados

Registos relacionados