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Recently, a large attention has been given to the problem of financial data modeling and decision making (Xidonas & Psarras, 2008; Davalos et al., 2009; Sun, 2010; Joseph & Mazouz, 2010; Hammami & Boujelbene, 2012; Lai & Joseph, 2012; Strang, 2012). One of the most attractive fields of study is the stock returns prediction. Indeed, stock market forecasting is a difficult and challenging task since stock data are noisy non-stationary, and chaotic. In particular, several factors affect the behaviour of the stock market including macroeconomic conditions, political events, psychology of investors, and traders’ expectations. Nevertheless, there have been many studies in the field of stock market forecasting using soft computing techniques in the last decade (refer to the survey by Atsalakis and Valavanis (2009)). More recent studies include bacterial foraging optimization, adaptive bacterial foraging optimization, genetic algorithms and particle swarm optimization (Majhi et al., 2009), bacterial chemotaxis optimization and artificial neural networks (Zhang and Wu, 2009), probabilistic neural network, rough set and C4.5 (Cheng et al., 2010b), Markov chain and fuzzy logic (Wang et al., 2010), rough sets theory and genetic algorithms (Cheng et al., 2010a), artificial neural networks (Wang et al., 2011), support vector machines (Yeh et al., 2011), fuzzy logic and artificial neural networks (Chakravarty & Dash, 2012; Kumar, 2012), and partially connected networks (Chang et al., 2012). The advantage of using soft computing techniques is that they can handle the uncertain, chaotic, and nonlinear structure of the stock markets (Majhi et al., 2009; Chakravarty & Dash, 2012). Furthermore, they are not based on the assumption of linearity of the underlying model and normality distribution of the variables. Indeed, these assumptions may not be satisfied in modeling the stock price movements (Wang et al., 2011). Support vector machines are the most successfully used soft computing technique for modeling and forecasting financial time series due to the outstanding performance in classification and regression problems (Wen et al., 2010; Ismael et al., 2010; Kara et al., 2011; Ni et al., 2011). Indeed, SVM which is introduced by Vapnik (1995) is based on the structural risk minimization principle that considers both the training error and the capacity of optimal generalization, has a global optimum, and is effective and powerful as a discriminant function (Vapnik, 1995; Cristianini & Shawe-Taylor, 2000; Kecman, 2001; Sun, 2010). To predict stock market movements, most of works used as predictive inputs to soft computing systems either macroeconomic variables, technical analysis indicators or simply historical values (Atsalakis & Valavanis, 2009). However, recent studies have given attention to the wavelet analysis (Daubechies, 1992; Chui, 2002) to transform stock market data to a time-frequency feature space suitable for financial modeling and forecasting using soft computing systems (Huang & Wu, 2010; Wang et al., 2011; Huang, 2011, Hsieh et al., 2011). The wavelet analysis which is widely used in pure science and engineering problems is a powerful tool to decompose a time series into series of different timescales. In particular, it decomposes a given data into high and low frequency components. At high frequency (shorter time intervals), the wavelets are able to capture discontinuities, ruptures and singularities in the original data. At low frequency (longer time intervals), the wavelet characterizes the coarse structure of data to identify the long-run trend. Because of these attractive features, it was concluded that the wavelet analysis is effective in forecasting stock market future price index (Huang & Wu, 2010; Wang et al., 2011; Huang, 2011, Hsieh et al., 2011).