Selective-Learning-Rate Approach for Stock Market Prediction by Simple Recurrent Neural Networks

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.

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