Trading ebook Finance - Neural Prediction of Weekly Stock Market Index

This augurs well for a good performance in the future

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Unformatted text preview: tput range. This augurs well for a good performance in the future, provided that care is taken to normalize the data so that a sudden increase in the index value will not saturate the normalized value. Fig. 11: Predicted and desired S&P 500 index 660 Max. % error = 4.044% 640 Avg. % error = 0.95% S&P 500 index 620 600 580 560 540 Desired value P redicted value 520 Correct trend - 43 out of 50 times 500 0 10 20 30 Data number 40 50 13 It can be argued that the network can be trained every week rather than keeping it based on the training, which will be very old near the end of the 50th week. Moving the training window every week and retraining the network is a valid approach, which might be necessary in practice. However, there is a danger of the network training on the noise, inherent in the weekly changes and hence, performing worse than this network. In any case, this procedure can be modified suitably and the prediction window can also be reduced to suit the requirements. Case 2: Crash of October, 19...
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