Forecasting Time Series using Neural Network Model: Application for Korean Stock Market
Young Geun Shin, Sang Sung Park, Dong Sik Jang · 2007
This study examines the performance of forecasting model established by using neural network. The neural network model proposed in this study is implemented to forecast the stock price stream of Korean stock market. Factors for forecasting that influence Korean stock market are obtained by the way based on multiple regression analysis. As a result of the analysis, Korean stock market is greatly influenced by the stock market of the USA. It is enough to be called comovement phenomenon. In addition, the interest rate of Korea has a great effect on Korean stock market as well. So, we established the forecasting model of Korea Composite Stock Price Index (KOSPI) using neural network with Back-Propagation (BP) algorithm. Experimental results show that the proposed model has a good performance in forecasting KOSPI index.