신경회로망을 이용한 종합주가지수의 변화율 예측

Prediction of Monthly Transition of the Composition Stock Price Index Using Error Back-propagation Method

This paper presents the neural network method to predict the Korea composition stock price index. The error back-propagation method is used to train the multi-layer perceptron network. Ten of the various economic indices of the past 7 Nears are used as train data and the monthly transition of the composition stock price index is represented by five output neurons. Test results of this method using the data of the last 18 months are very encouraging.