Forecasting with fuzzy neural networks: A case study in stock market crash situations

Neural networks have been used for forecasting purposes for some years now. Often arises the problem of a black-box approach, i.e. after having trained neural networks to a particular problem, at is almost impossible to analyse them for how they work. Fuzzy Neuronal Networks allow to add rubs to neural networks, This avoids the black-box-problem. Additionally they are supposed to have a higher prediction precision in unlike situations. In this paper a case study describes a comparison of fuzzy neural networks and the classical approach during the stock market crashes of 1987 and 1998, It can be found that rules generate a more stable prediction quality, while the performance is not as good as when using classical neural networks.