Abstract
Two possible neural network architectures for stock market forecasting are the time-delay neural network and the recurrent neural network. In this paper we explore two effective techniques for the training of the above networks: the conjugate gradient algorithm and multi-stream extended Kalman filter. We are particularly interested in limiting false alarms, which correspond to actual investment losses. Encouraging results have been obtained when using the above techniques.
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