Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Evolutionary neural network modeling for forecasting the field failure data of repairable systems
Expert Systems with Applications: An International Journal
A new computational method of input selection for stock market forecasting with neural networks
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
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This paper analyses whether artificial neural networks can outperform traditional time series models for forecasting stock market returns. Specifically, neural networks were used to predict Brazilian daily index returns and their results were compared with a time series model with GARCH effects and a structural time series model (STS). Further, using output from ARMA-GARCH model as an input to a neural network is explored. Several procedures were utilized to evaluate forecasts, RMSE, MAE and the Chong and Hendry encompassing test. The results suggest that artificial neural networks are superior to ARMA-GARCH models and STS models and volatility derived from the ARMA-GARCH model is useful as an input to a neural network.