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The use of neural networks has been extended in all areas of knowledge due to the good results being obtained in the resolution of the di erent problems posed. The prediction of prices in general, and stock market prices in particular, represents one of the main objectives of the use of neural networks in finance. This paper presents the analysis of the e iciency of the hybrid fuzzy neural network against a backpropagation type neural network in the price prediction of the Spanish stock exchange index (IBEX-35). The paper is divided into two parts. In the first part, the main characteristics of neural networks such as hybrid fuzzy and backpropagation, their structures and learning rules are presented. In the second part, the prediction of the IBEX-35 stock exchange index with these networks is analyzed, measuring the Cómo citar este artículo: Oliver Muncharaz, J. (2020) Red neuronal fuzzy híbrida versus red neuronal backpropagation: Aplicación a la predicción del índice bursátil Ibex-35. Finance, Markets and Valuation 6(1), pp. 85–98. 85 Finance, Markets and Valuation Vol. 6, Num. 1 (Enero-Junio 2020), 85–98 e iciency of both as a function of the prediction errors committed. For this purpose, both networks have been constructed with the same inputs and for the same sample period. The results obtained suggest that the Hybrid fuzzy neural network is muchmore e icient than the widespread backpropagation neuronal network for the sample analysed.

Hybrid fuzzy neural network versus backpropagation neural network: An application to predict the Ibex-35 index stock