Combination Method between Fuzzy Logic and Neural Network Models to Predict Amman Stock Exchange

Abstract

The purpose of this paper is to consider the potential in the projection of Fuzzy logic and Neural networks, also to make some combination between models to address implementation issues in the prediction of index and prices for Amman stock exchange in different models, where the previous researchers have to demonstrate the differences between these measures. We have used in this research Amman stock Exchange index prices data as a sample set to compare the different application models, where predicting the stock market was very difficult since it depends on nonstationary financial data, in addition to the most of the models are nonlinear systems. These papers draw an existing academic and practitioner in literature review as a combination of these models and compare them, the facilities of the development of conceptual methods and the research proposition are the basis for serving this combination. Hence, the present and recent papers can serve the further researchers into addressing contemporary barriers in the direction of these researchers. The authors show in this paper the Fuzzy logic and Neural networks, in addition to time series analysis through these models, utilized of RSI, OS, MACD, and OBV, then using MSE, MAPE, and RMSE. The research implication represents of too much data for the period of study, also this paper is conceptual in its nature, the paper highlights in finding that the implementation challenges, and how these challenges can facilitate the trader decision in the stock market. The results of the analysis show that the ANFIS is the better model to achieve prediction of stock market more than others. When are MAPE and RMSE the best more than simulating the errors in other methods? Also the fuzzy-neural models as the results of table show that more prominent in fuzzy-neural models ,while it appears that in MSE as medium, MAD posses less amount than other models in all table testing fuzzy-neural models, therefore, it becomes superior ...