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Ajuste a La Calificación Del Riesgo De Mercado De Las Emisoras Más Activas Que Cotizan En La Bolsa Mexicana De Valores, Con La Implementación De Una Red Neuronal Artificial Clasificadora

Adjustment to the Market Risk Rating of the Most Active Issuers Lis

Aura María González Garzón

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Paperback / softback
22 March 2019
$39.00
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En México, la aplicación de Redes Neuronales Artificiales en finanzas, se ha enfocado en el estudio del análisis del riesgo de crédito; empleándolas para ajustar los resultados de indicadores bursátiles que ofrecen información útil a los inversionistas que desean obtener niveles óptimos de inversión. Sin embargo, esta investigación en particular, usa esta herramienta para establecer un ajuste a la medición y clasificación 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 simulación de la red; los cuales han alcanzado un nivel de categorización arriba del 70%, y de acuerdo con éstos, las variables que contribuyen significativamente a la medición y clasificación del riesgo son: la tasa de rendimiento requerida, los Cetes a 91 días y los rendimientos accionarios, en comparación con otras ya utilizadas anteriormente en la primera etapa de la simulación. 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.

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$39.00
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Ajuste a La Calificación Del Riesgo De Mercado De Las Emisoras Más Activas Que Cotizan En La Bolsa Mexicana De Valores, Con La Implementación De Una Red Neuronal Artificial Clasificadora

$39.00

Description

En México, la aplicación de Redes Neuronales Artificiales en finanzas, se ha enfocado en el estudio del análisis del riesgo de crédito; empleándolas para ajustar los resultados de indicadores bursátiles que ofrecen información útil a los inversionistas que desean obtener niveles óptimos de inversión. Sin embargo, esta investigación en particular, usa esta herramienta para establecer un ajuste a la medición y clasificación 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 simulación de la red; los cuales han alcanzado un nivel de categorización arriba del 70%, y de acuerdo con éstos, las variables que contribuyen significativamente a la medición y clasificación del riesgo son: la tasa de rendimiento requerida, los Cetes a 91 días y los rendimientos accionarios, en comparación con otras ya utilizadas anteriormente en la primera etapa de la simulación. 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.

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