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.
CITATION STYLE
Yim, J. (2002). A comparison of neural networks with time series models for forecasting returns on a stock market index. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2358, pp. 25–35). Springer Verlag. https://doi.org/10.1007/3-540-48035-8_4
Mendeley helps you to discover research relevant for your work.