Advanced Neural Network Training Methods for Low False Alarm Stock Trend Prediction
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 training of the above networks, i.e. conjugate gradient algorithm and multi-stream extended Kalman filter. We are particularly interested in limiting false alarms, which corresponds to actual investment losses. Encouraging results have been obtained when using the above techniques.
E. W. Saad et al., "Advanced Neural Network Training Methods for Low False Alarm Stock Trend Prediction," Neural Networks, 1996. IEEE International Conference on Neural Networks, vol. 4, pp. 2021 - 2026, Institute of Electrical and Electronics Engineers (IEEE), Jan 1996.
The definitive version is available at https://doi.org/10.1109/ICNN.1996.549212
1996 IEEE International Conference on Neural Networks, ICNN (1996: Jun. 3-6, Washington, DC)
Electrical and Computer Engineering
International Standard Book Number (ISBN)
Article - Conference proceedings
© 1996 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Jan 1996