Stock market indices in Santiago de Chile: forecasting using neural networks

Artificial neural networks (ANN) were used to predict the general index of share prices at the Santiago de Chile stock market. Time series with daily values of the index and of total amount of transactions were used to train the ANN. Input data was standardized and normalized shifting mean value to zero, variance to one and maximum values to one. A combined ANN produced better results than simple architectured ANN. The network managed TIS and I-delay memories in parallel. A time delay of ten labor days were sufficient to forecast. Results shows adequate performance of ANN in comparison with other methods used at INDECSA.