Neural Network Model vs. Sarima Model in Forecasting Korean Stock Price Index (Kospi)

The purpose of this study is to compare the forecasting performance of a neural network (NN) model and a time-series (SARIMA) model in Korean Stock Exchange. In particular, we investigate whether the back-propagation neural network (BPNN)model outperforms the seasonal autoregressive integrated moving average (SARIMA) model in forecasting the Korea Composite Stock Price Index (KOSPI) and its return. Forecasting performance is ev1auated by the forecasting accuracy of each model. KOSPI data and its return data over the 390 week (89 month) period extending from January 1999 to May 2006 are analyzed. We find the followings: first, the SARIMA model generally provides more accurate forecasts for the KOSPI than the BPNN model does. This relative superiority of the SARIMA model over the BPNN model is pronounced for the mid-range forecasting horizons. Second, the BPNN model is generally better than the SARIMA model in forecasting the KOSPI returns. However, the difference in forecasting accuracies of the two models is not statistically significant. These results hold for both weekly and monthly data, and are robust across different measures of forecasting accuracy.