Neural Networks Approach to the Random Walk Dilemma of Financial Time Series
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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