Neural computing: theory and practice
Neural computing: theory and practice
Artificial neural system for gene classification using a domain database
CSC '90 Proceedings of the 1990 ACM annual conference on Cooperation
Neural nets for image restoration
CSC '90 Proceedings of the 1990 ACM annual conference on Cooperation
CSC '92 Proceedings of the 1992 ACM annual conference on Communications
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Construction of an artificial neural network for simple exponential smoothing in forecasting
SAC '94 Proceedings of the 1994 ACM symposium on Applied computing
Computational intelligence for evolving trading rules
IEEE Transactions on Evolutionary Computation - Special issue on computational finance and economics
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We have developed a stock-market forecasting system based on artificial neural networks. The system has been trained with the Standard & Poor 500 composite indexes of past twenty years. Meanwhile, the system produces the forecasts and adjusts itself by comparing its forecasts with the actual indexes. Since most of stock-market forecasting systems are based on some kind of statistical models, we have also implemented a statistical system based on Box-Jenkins ARIMA(p,d,q) model of time series. We compare the performance of the these systems. It shows that the artificial neural network's forecasting is generally superior to time series but it occasionally produces some very wild forecasting values. We then developed a transfer function model to forecast based on the indexes and the forecasts by the artificial neural networks.