Forecasting the stock market is a challenging task because of its stochastic and complex nature. Various statistical models and data mining techniques have been developed in the recent years and applied to stock market forecasting. A review of the relevant literature shows that only a very few studies have applied high frequency data to forecast the stock market and among these studies, only one or two have applied data mining techniques. There are no studies on forecasting high frequency data of stock index using multivariate adaptive regression splines. In this paper we study the applicability of the following four data mining techniques: backpropagation neural network (BPNN), support vector regression (SVR), multivariate adaptive regression splines (MARS) and Markov chain incorporated into fuzzy stochastic (MF), for one-stepahead forecast of S&P CNX Nifty index of India and Nasdaq composite index of USA with every sixtieth minute data. The results of the study shows that SVR is better than the others for forecasting high frequency data of both indices with an accuracy of 99.7 %.
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