Financial predictions based on bootstrap-neural networks
نویسندگان
چکیده
In this paper neural netw orks are applied to nancial data in order to predict the daily price of the nancial index LIFFE. Our atten tion is focused on the choice of the exogeneous variables and on the training of the netw ork itself. The rst problem is solved by using the pre-whitening method that provides information on which variables are the most relevan t for our prediction. The latter problem is due to the fact that data referring to a far past cannot be used because of the nonstationarit yof the nancial indicators. This implies that the training set is relativ ely small and it is necessary to extract as much information as possible from recent data. The bootstrap approach is applied to the training set of the neural netw ork to improve the predicition capabilities of the system. This results in better prediction performances even when a limited number of data is available.
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