Forecasting trends of high-frequency KOSPI200 index data using learning classifiers

Recently many statistical learning techniques have been applied to the prediction of financial variables. The aim of this paper is to conduct a comprehensive study of the applications of statistical learning techniques to predict the trend of the return of high-frequency Korea composite stock price index (KOSPI) 200 index data using the information from the one-minute time series of spot index, futures index, and foreign exchange rate. Through experiments, it is observed that the spot index change is better predictable with high-frequency time series data and the futures index information significantly improves the prediction accuracy of the return trends of the spot index for high-frequency index data, while the information of exchange rate does not. Also, dimension reduction process before training helps to increase the accuracy and dramatically for some classifiers. In addition, the trained classifiers with which a virtual trading strategy is applied to, noticeable better profits can be achieved than just a buy-and-hold-like strategy.