Market efficiency analysis using AI models based on Investors’ Mood

Palabras clave: big data, IBEX, redes bayesianas, humor do investidor, trading systems, eficiência de mercado

Resumen

La hipótesis del mercado eficiente asume que los precios de las acciones en los mercados financieros incorporan toda la información histórica en cualquiera de sus formas (débil, semifuerte y fuerte). El objetivo de este estudio es validar esta hipótesis. Este estudio utiliza modelos de inteligencia artificial diseñados para predecir las tendencias del IBEX con base en el estado de ánimo de los inversores, extrayendo información del big data y utilizando algoritmos de procesamiento del lenguaje natural. Los resultados del estudio muestran que la tasa de éxito de un sistema que se prepara para solo 6 meses es mayor que la de un sistema que utiliza toda la información histórica disponible. Las estrategias de inversión también pueden basarse en las previsiones de los modelos de inteligencia artificial, que pueden superar el mercado, estableciendo diferentes sistemas de negociación para distintos grados de riesgo en función del umbral de probabilidad que proporcione el modelo considerado. Estos resultados implican que el mercado financiero español tiene una memoria de corto plazo y no incluye información más antigua, por lo que no cumple los supuestos de la hipótesis de mercado eficiente.

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Biografía del autor/a

Gómez Martínez Raúl

PhD in Business Economics and Finance. Universidad Rey Juan Carlos, Madrid, Spain

Paola Plaza Casado

PhD in Financial Economics and Accounting. Universidad Rey Juan Carlos, Madrid, Spain

Miguel Prado Román

PhD in Financial Economics and Accounting. Universidad Rey Juan Carlos, Madrid, Spain

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Publicado
2021-04-26
Cómo citar
Raúl, G. M., Plaza Casado, P., & Prado Román, M. (2021). Market efficiency analysis using AI models based on Investors’ Mood. Revista Perspectiva Empresarial, 7(2), 10-23. https://doi.org/10.16967/23898186.649
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