Ability to predict direction of stock/index price accurately is crucial for market dealers or investors to maximize their profits. Data mining techniques have been successfully shown to generate high forecasting accuracy of stock price movement. Nowadays, in stead of a single method, traders need to use various forecasting techniques to gain multiple signals and more information about the future of the markets. In this paper, ten different techniques of data mining are discussed and applied to predict price movement of Hang Seng index of Hong Kong stock market. The approaches include Linear discriminant analysis (LDA), Quadratic discriminant analysis (QDA), K-nearest neighbor classification, Naïve Bayes based on kernel estimation, Logit model, Tree based classification, neural network, Bayesian classification with Gaussian process, Support vector machine (SVM) and Least squares support vector machine (LS-SVM). Experimental results show that the SVM and LS-SVM generate superior predictive performances among the other models. Specifically, SVM is better than LS-SVM for in-sample prediction but LS-SVM is, in turn, better than the SVM for the out-of-sample forecasts in term of hit rate and error rate criteria.

In this paper, we aim at comparing semi-parametric method, LSSVM (Least square support vector machine), with the classical GARCH(1,1), EGARCH(1,1) and GJR(1,1) models to forecast financial volatilities of three major ASEAN stock markets. More precisely, the experimental results suggest that using hybrid models, GARCH-LSSVM, EGARCH-LSSVM and GJR-LSSVM provides improved performances in forecasting the leverage effect volatilities, especially during the recently global financial market crashes in 2008.

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