An ANFIS model for stock price prediction: The case of Tehran stock exchange

Abstract

The main purpose of forecasting in financial markets is to estimate future trends and to reduce risks of decision making. This research suggests an ANFIS model to improve prediction accuracy in stock price forecasting. For doing so, we applied fuzzy subtractive clustering for structure identification of our ANFIS model. We implemented the proposed model for predicting Tehran Stock Exchange Price Index (TEPIX) using a dataset including TEPIX data from 25 March 2001 until 25 September 2010. To demonstrate the advantages of this model, first we compared our results with an Artificial Neural Network (ANN) model of type Multi Layer Perceptron (MLP). Then, we compared our results with ANFIS models using grid partitioning and Fuzzy C-Mean (FCM) clustering. The comparative results show the superiority of our proposed ANFIS model against ANN model and ANFIS models with no clustering and FCM clustering.