In this paper we study methods for predicting the German stock index DAX. T
he idea is to use the information provided by several different information
sources. We consider two different types of information sources: (1) human
experts who formulate their knowledge in the form of rules, and (2) databa
ses of objective measurable time series of financial values. It is shown ho
w to fuse these different types of knowledge by using neuro-fuzzy methods.
We present experimental results that demonstrate the usefulness of these ne
w concepts. In the second part of the paper we present methods for the eval
uation and combination of different methods for DAX prediction by using a p
robabilistic assessment methodology. (C) 2001 John Wiley & Sons, Inc.