Stock price prediction using a fusion model of wavelet, fuzzy logic and ANN

Stock price prediction is vital for making informed investment decisions and is receiving increasing attention especially because of its practical applications. Predicting the stock market is very difficult since it depends on several unknown factors. The traditional prediction models are not able to achieve a satisfying prediction effect in the problem of a non-linear system and non stationary financial signal. On the other hand data preparation is an important step for complex data analysis and it has a huge impact on the success of prediction. In this paper we propose a fusion model of forecasting by combining wavelet as a data preparation tool, fuzzy logic and neural network. We have used Tehran stock market prices as a sample dataset to compare simulation error of stock market returns between the proposed models. The experimental results show that this fusion model achieves better forecasting accuracy than either of the models used separately.