Trading ebook Finance - Neural Prediction of Weekly Stock Market Index

The feature extraction is not explained in 12 except

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Unformatted text preview: of rt for some weeks. The feature extraction is not explained in [12] except for the fact that some irregularity is removed and logarithm function is used before normalization. The authors claim that the use of the weighted sum of the outputs of many neural networks reduces the error, especially since the returns are predicted for a few weeks. A buy/sell system is setup based on the predicted returns and this system is shown to perform much better than a buy-hold strategy. However, the teaching data uses future returns, so that this method cannot be used for actual stock trading. (The authors do mention this as part of their proposed future work). One week ahead prediction using feedforward networks This work [7] uses a simple feedforward neural network trained using past and present data to predict the value of the FAZ-Index which is the German equivalent of the DJIA. Input data includes the moving average of past 5 and 10 weeks of the FAZ-Index, a first order difference of the FAZ-Index and its moving averages, the present bond market index and its first order difference and the Dollar-Mark exchange rate along with its f...
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This note was uploaded on 07/20/2012 for the course ECON 203 taught by Professor Girishdev during the Spring '12 term at Indian Institute of Technology, Kharagpur.

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