We have investigated selective learning techniques for improving the ability of back-propagation neural networks to predict large changes. The prediction of daily stock prices was taken as an example of a noisy real-world problem. We previously proposed the selective-presentation and selective-learning-rate approaches and applied them into feed-forward neural networks. This paper applies the selective-learning-rate approach into three types of simple recurrent neural networks. We evaluated their performances through experimental stock-price prediction. Using selective-learning-rate approach, the network can learn the large changes well and profit per trade was improved in all of simple recurrent neural networks.
CITATION STYLE
Kohara, K. (2003). Selective-learning-rate approach for stock market prediction by simple recurrent neural networks. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 2773 PART 1, pp. 141–147). Springer Verlag. https://doi.org/10.1007/978-3-540-45224-9_21
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