Trading ebook Finance - Neural Prediction of Weekly Stock Market Index

The authors conclude that the neural network shows a

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Unformatted text preview: r the short term module. The authors conclude that the neural network shows a much better response than multiple linear regression. Neural sequential associator In this paper [10], the author uses a feedforward neural network with the last n stock index values as inputs and the next N-n values as the outputs. This is a N-n step ahead prediction. Thus, if index for the nth day is denoted by Xn, then, the inputs are X1, X2, ... Xn and the outputs are Xn+1, Xn+2, ..., XN. If such a network is trained, any correlation between the index values for the n+1 through Nth day will be neglected. To ensure that this does not happen, the network is trained with errors between the desired and actual outputs in addition to the n inputs. These errors will then be (Xn+1 - Yn+1) ...., where Y is the output of the network. As training proceeds this error will tend to zero and these additional inputs are not required in the testing phase. This work also uses two neural networks, one to learn the global features and another to learn the local feature...
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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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