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En M xico, la aplicaci n de Redes Neuronales Artificiales en finanzas, se ha enfocado en el estudio del an lisis del riesgo de cr dito; emple ndolas para ajustar los resultados de indicadores burs tiles que ofrecen informaci n til a los inversionistas que desean obtener niveles ptimos de inversi n. Sin embargo, esta investigaci n en particular, usa esta herramienta para establecer un ajuste a la medici n y clasificaci n del riesgo de mercado mexicano; mostrando los resultados obtenidos en la fase experimental de los procesos de entrenamiento y prueba en la segunda etapa de simulaci n de la red; los cuales han alcanzado un nivel de categorizaci n arriba del 70%, y de acuerdo con stos, las variables que contribuyen significativamente a la medici n y clasificaci n del riesgo son: la tasa de rendimiento requerida, los Cetes a 91 d as y los rendimientos accionarios, en comparaci n con otras ya utilizadas anteriormente en la primera etapa de la simulaci n. In Mexico, the Artificial Neuronal Network applicate to the finances has focused in the study of the analysis of the credit risk; and to fit the results of stock-exchange indicators that offer useful information to the investors who wishes to obtain optimal returns. Nevertheless, in this case in particular, this tool it s used to measure and classified the Mexican market risk; showing the results obtained in the experimental phase of the training and test in the second simulation stage of the network; reaching a classification rate of over 70%. According to this, the variables that significantly contribute to the measurement and classification of the risk are: the required rate of return, the Cetes to 91 days and shareholding yields, in comparison with others previously used in the first stage of the simulation.
