Prediction of monthly transition of the composition stock price index using recurrent back-propagation

Recurrent-type neural networks are trained to predict the transition of monthly composition stock price index. Eleven economic indicators of eight seven years are used for training and performance evaluation. Three prediction models are designed and compared. The percentage of correct prediction of rise or fall is between 67 to 78%.