Information fusion in the context of stock index prediction

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Abstract
In this paper we study methods for predicting the German stock index DAX. The 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) databases of objective measurable time series of financial values. It is shown how to fuse these different types of knowledge by using neuro-fuzzy methods. We present experimental results that demonstrate the usefulness of these new concepts. In the second part of the paper we present methods for the evaluation and combination of different methods for DAX prediction by using a probabilistic assessment methodology. © 2001 John Wiley & Sons, Inc.

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