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Analysis of the predictive ability of time delay neural networks applied to the S&P 500 time series

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2 Author(s)
R. Sitte ; Fac. of Eng. & Inf. Technol., Griffith Univ., Gold Coast, Qld., Australia ; J. Sitte

Reported work on financial time series prediction using neural networks often shows a characteristic one step shift relative to the original data. This seems to imply a failure of the neural network (NN), because a shift corresponds to a random walk prediction. Our systematic analysis of different time delay neural networks predictors applied to the detrended S&P 500 time series, indicates that this prediction behavior is not a limitation of the network, but may be a characteristic of the time series. This suggests that there are no short-term correlations in this stockmarket time series, which is consistent with conventional statistical analysis

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IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)  (Volume:30 ,  Issue: 4 )