STOCK MARKET PREDICTION USING A COMBINATION OF STEPWISE REGRESSION ANALYSIS, DIFFERENTIAL EVOLUTION-BASED FUZZY CLUSTERING, AND A FUZZY INFERENCE NEURAL NETWORK

This paper discusses a hybrid prediction model that combines differential evolution-based fuzzy clustering with a fuzzy inference neural network for performing an index level forecast. In the first phase of the proposed model, stepwise regression analysis is implemented to determine the combination of inputs that have the strongest forecasting ability. Next, the selected variables are grouped by means of a differential evolution-based fuzzy clustering method, allowing the extraction rules to be determined. For the final stage, a fuzzy inference neural network is implemented to predict the market prices by using the extraction rules from the previous stage.