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. The problem of the black-box approach often arises, i.e., after having trained neural networks to a particular problem, it is almost impossible to analyse them for how they work. Fuzzy neuronal networks allow one to add rules to neural networks. This avoids the black-box-problem. Additionally they are supposed to have a higher prediction precision in different situations. 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.