Predicting Turning Points in Financial Markets with Fuzzy-Evolutionary and Neuro-Evolutionary Modeling

Two independent evolutionary modeling methods, based on fuzzy logic and neural networks respectively, are applied to predicting trend reversals in financial time series, and their performances are compared. Both methods are found to give essentially the same results, indicating that trend reversals are partially predictable.

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