Stock market prediction is an important area of financial forecasting, which attracts great interest to stock buyers and sellers, stock\r\ninvestors, policy makers, applied researchers, and many others who are involved in the capital market. In this paper, a comparative\r\nstudy has been conducted to predict stock index values using soft computing models and time series model. Paying attention to\r\nthe applied econometric noises because our considered series are time series, we predict Chittagong stock indices for the period\r\nfrom January 1, 2005 to May 5, 2011. We have used well-known models such as, the genetic algorithm (GA) model and the\r\nadaptive network fuzzy integrated system (ANFIS) model as soft computing forecasting models. Very widely used forecasting\r\nmodels in applied time series econometrics, namely, the generalized autoregressive conditional heteroscedastic (GARCH) model\r\nis considered as time series model. Our findings have revealed that the use of soft computing models is more successful than the\r\nconsidered time series model.
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