Summary
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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Azzini, A., da Costa Pereira, C., Tettamanzi, A.G.B. (2010). Modeling Turning Points in Financial Markets with Soft Computing Techniques. In: Brabazon, A., O’Neill, M., Maringer, D.G. (eds) Natural Computing in Computational Finance. Studies in Computational Intelligence, vol 293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13950-5_9
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DOI: https://doi.org/10.1007/978-3-642-13950-5_9
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