Stock market prediction is an important area of financial forecasting, which attracts great interest to stock buyers and sellers, stock investors, policy makers, applied researchers, and many others who are involved in the capital market. In this paper, a comparative study has been conducted to predict stock index values using soft computing models and time series model. Paying attention to the applied econometric noises because our considered series are time series, we predict Chittagong stock indices for the period from January 1, 2005 to May 5, 2011. We have used well-known models such as, the genetic algorithm (GA) model and the adaptive network fuzzy integrated system (ANFIS) model as soft computing forecasting models. Very widely used forecasting models in applied time series econometrics, namely, the generalized autoregressive conditional heteroscedastic (GARCH) model is considered as time series model. Our findings have revealed that the use of soft computing models is more successful than the considered time series model. 1. Introduction The stock index values play an important role in controlling dynamics of the capital market. As a result, the appropriate prediction of stock index values is a crucial factor for domestic/foreign stock investors, buyers and/or sellers, fund managers, policy makers, applied researchers (who want to improve the model specifications of this index), and many others. Many researchers, for example, [1–4] and others have found that the empirical distribution of stock is significantly nonnormal and nonlinear. Stock market data are also observed in practice chaotic and volatile by nature (e.g., see [5–8]). That is why stock values are hard to predict. Traditionally, the fundamental Box-Jenkins analysis has been the mainstream methodology that is used to predict stock values in applied literature. Due to continual studies of stock market experts, the use of soft computing models (such as artificial neural networks, fuzzy set, evolutionary algorithms, and rough set theory.) have been widely established to forecast stock market. Evidence [9, 10] suggests that the Box-Jenkins approach often fails to predict time series when the behavior of series is chaotic and nonlinear. Thus, soft computing systems have emerged to increase the accuracy of chaotic time series predictions. The reason is that these systems have the potential to provide a viable solution through the versatile approach to self-organization. Thus, in forecasting literatures [11–14], it has been found that soft computing systems yield better results compared to
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