Modeling chaotic behavior of chittagong stock indices

  • Authors:

  • Shipra Banik;Mohammed Anwer;A. F. M. Khodadad Khan

  • Affiliations:

  • School of Engineering and Computer Science, Independent University, Bangladesh, Dhaka, Bangladesh;School of Engineering and Computer Science, Independent University, Bangladesh, Dhaka, Bangladesh;School of Engineering and Computer Science, Independent University, Bangladesh, Dhaka, Bangladesh

  • Venue:
  • Applied Computational Intelligence and Soft Computing - Special issue on Applied Neural Intelligence to Modeling, Control, and Management of Human Systems and Environments
  • Year:
  • 2012

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Abstract

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.