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

摘要: We have investigated selective learning techniques for improving the ability of back-propagation neural networks to predict large changes. The prediction daily stock prices was taken as an example a noisy real-world problem. previously proposed selective-presentation and selective-learning-rate approaches applied them into feed-forward networks. This paper applies approach three types simple recurrent evaluated their performances through experimental stock-price prediction. Using approach, network can learn changes well profit per trade improved in all