Modeling Chaotic Behavior of Dhaka Stock Market Index Values Using the Neuro-fuzzy Model

Shipra      Banik; Farah      Habib Chanchary; Rifat      Ara Rouf; AFM      Khodadad Khan

doi:10.2174/2213275911205010072

Abstract

Stock market prediction is an important area of financial forecasting, which attracts great interest of stock investors, stock buyers/sellers, policy makers, applied researchers and many others who are involved in the capital market. This paper aims to develop an efficient model to predict the Dhaka Stock Market Index (DSPI) values using the appropriate forecasting model. It is widely believed that stock data are nonlinear, dynamic and chaotic. In this paper, we propose an adaptive network based fuzzy inference system (ANFIS) to predict DSPI values. We used the daily general DSPI values for the period of March 2003 to October 10, 2006 for the learning and October 11, 2006 to May 31, 2007 for validation. Results obtained by this model are also compared to the back-propagation ANN model and the traditional ARIMA model to show advantages of the proposed ANFIS model. The findings suggest that the ANFIS model can be used as a better predictor for daily general DSPI values as compared to the ANN and the ARIMA models. The review also discussed relevant patents related to our research.

Keywords: ANFIS, back-propagation, fuzzy sets and logics, neural network, stock market, time series forecasting, ARCHITECTURES AND LEARNING, ARIMA, DSPI, ANN