Information fusion in the context of stock index prediction

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.