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Andrés Felipe Rodríguez Pérez Robert Romero

Resumen

Diariamente se generan grandes volúmenes de información, especialmente en las redes sociales. El uso de esta información como insumo para el estudio del comportamiento de los agentes en el mercado de valores ha venido cobrando fuerza, especialmente en el campo del aprendizaje de máquina. Es por ello que, en este artículo se presenta un estudio de la capacidad predictiva de la información que generan los agentes del mercado en la red social StockTwits sobre la variación de la dirección del precio de una activo transado en la Bolsa de Valores de Nueva York, valiéndose de herramientas de minería de datos y algoritmos de aprendizaje de máquina.

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Palabras Clave

Bolsa de Valores de Nueva York, Precio del Activo, Minería de Datos, Aprendizaje Automático, Procesamiento del Lenguaje Natural, Análisis de Redes Sociales

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Cómo citar
Rodríguez Pérez, A. F., & Romero, R. (2020). Exploración del poder predictivo de datos extraídos de StockTwits respecto a la dirección de variación futura del precio de un activo transado en la Bolsa de Valores de Nueva York, . Comunicaciones En Estadística, 13(2), 51-61. Recuperado a partir de https://revistas.usantotomas.edu.co/index.php/estadistica/article/view/6285
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