Modeling Turning Points in Financial Markets with Soft Computing Techniques

Two independent evolutionary modeling methods, based on fuzzy logic and neural networks respectively, are applied to predicting trend reversals in financial time series of the financial instruments S&P 500, crude oil and gold, 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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