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Linear and Non-linear time series analysis: forecasting financial markets

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Resumo:Time series analyses in financial area have been attract some special attention in the recent years. The stock markets are examples of systems with a complex behaviour and, sometimes, forecasting a financial time series can be a hard task. In this thesis we compare linear against non-linear models, ARIMA and Artificial Neural Networks. Using the log returns of nine countries we tried to demonstrate that neural networks can be used to uncover the non-linearity that exists in the financial field. First we followed a traditional approach by analysing the characteristics of the nine stock series and some typical features. We also produce a BDS test to investigate the nonlinearity, the results were as expected, and none of the markets exhibit a linear dependence. In consequence, traditional linear models may not produce reliable forecasts. However, this didn’t mean that neural networks can. We trained four types of neural networks for the nine stock markets and the results between them were quite similar varying most in their structure and suggesting that more studies about the hidden units between the input and output layer need to be done. This study stresses the importance of taking into account nonlinear effects that are quite evident in the stock market MODELS.
Autores principais:Barão, Sandra Maria Mestre
Assunto:Stock returns Neural networks ARIMA models Linear time series Non linear time series Redes neuronais Retornos bolsistas Séries temporais lineares Séries temporais não-lineares
Ano:2008
País:Portugal
Tipo de documento:dissertação de mestrado
Tipo de acesso:acesso aberto
Instituição associada:ISCTE
Idioma:inglês
Origem:Repositório ISCTE

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