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

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 t...

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Bibliographic Details
Published in:IEEE transactions on human-machine systems 2000-11, Vol.30 (4), p.568-572
Main Authors: Sitte, R., Sitte, J.
Format: Article
Language:English
Subjects:
Australia
Correlation analysis
Cybernetics
Delay effects
Fluctuations
Information technology
Networks
Neural networks
Prediction methods
Random walk
Statistical analysis
Stochastic processes
Testing
Time delay
Time series
Time series analysis
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Summary: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.
ISSN:1094-6977
2168-2291
1558-2442
2168-2305
DOI:10.1109/5326.897083