Abstract
This paper proposes a hybrid decision support system for financial time series analysis. It uses evolutionary algorithms to construct an artificial expert, consisting of some expert rules, which processes financial time series and provides an expertise. Such expert rules are either static coming from the domain knowledge or are in the form of multi-layer perceptrons representing expert rules extracted from data. Results of a large number of experiments on real-life data from the Paris Stock Exchange confirm that the model of the hybrid decision support system is reasonable and it is able to generate reasonable solutions, not only over a training period, used in the training process, but also over a test period, unknown during constructing artificial experts.
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