Publicação
Forecasting financial markets with artificial neural networks
| Resumo: | Artificial Neural Networks are exible nonlinear mathematical models widely used in forecasting. This work is intended to investigate the support these models can give to nancial economists predicting prices movements of oil and gas companies listed in stock exchanges. Multilayer Perceptron models with logistic activation functions achieved better results predicting the direction of stocks returns than traditional linear regressions and better performances in companies with lower market capitalization. Furthermore, multilayer perceptron with eight hidden units in the hidden layer had better predictive ability than a neural network with four hidden neurons. |
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| Autores principais: | Vieira, Cristiano Ribeiro |
| Assunto: | Artificial Neural Networks Multilayer Perceptron Backpropagation Financial markets Oil & Gas Industry Forecasting. |
| Ano: | 2013 |
| País: | Portugal |
| Tipo de documento: | dissertação de mestrado |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório da Universidade de Lisboa |
| Resumo: | Artificial Neural Networks are exible nonlinear mathematical models widely used in forecasting. This work is intended to investigate the support these models can give to nancial economists predicting prices movements of oil and gas companies listed in stock exchanges. Multilayer Perceptron models with logistic activation functions achieved better results predicting the direction of stocks returns than traditional linear regressions and better performances in companies with lower market capitalization. Furthermore, multilayer perceptron with eight hidden units in the hidden layer had better predictive ability than a neural network with four hidden neurons. |
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