A New Fuzzy Grach Model to forecast Stock Market Technical Analysis

Abstract

Decision making process in stock trading is a complex one. Stock market is a key factor of monetary markets and signs of economic growth. In some circumstances, traditional forecasting methods cannot contract with determining and sometimes data consist of uncertain and imprecise properties which are not handled by quantitative models. In order to achieve the main objective, accuracy and efficiency of time series forecasting, we move towards the fuzzy time series modeling. Fuzzy time series is different from other time series as it is represented in linguistics values rather than a numeric value. The Fuzzy set theory includes many types of membership functions. In this study, we will utilize the Fuzzy approach and trapezoidal membership function to develop the fuzzy generalized auto regression conditional heteroscedasticity (FGARCH) model by using the fuzzy least square techniques to forecasting stock exchange market prices. The experimental results show that the proposed forecasting system can accurately forecast stock prices. The accuracy measures RMSE, MAD, MAPE, MSE, and Theil-U-Statistics have values of 18.17, 15.65, 2.339, 301.998, and 0.003212, respectively, which confirmed that the proposed system is considered to be useful for forecasting the stock index prices, which outperforms conventional GARCH models.