Forecasting stock market with fuzzy neural networks

Neural networks have been widely used to forecast indices and prices of stock market due to the significant properties of treating non-linear data with self-learning capability. However, neural networks suffer from the difficulty to deal with qualitative information and the "black box" syndrome that more or less limited their applications in practice. To overcome the drawbacks of neural networks, in this study we proposed a fuzzy neural network that is a class of adaptive networks and functionally equivalent to a fuzzy inference system. The experiment results based on the comprehensive index of Shanghai stock market indicate that the suggested fuzzy neural network could be an efficient system to forecast financial time series. To make this clearer, an empirical analysis is given for illustration.