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

• Andrés Felipe Rodríguez Pérez ^[1] ; Robert Romero
  1. [1] Universidad Santo Tomás

     Universidad Santo Tomás

     Santiago, Chile

     
• Localización: Comunicaciones en Estadística, ISSN 2027-3355, ISSN-e 2339-3076, Vol. 13, Nº. 2, 2020, págs. 51-61
• Idioma: español
• DOI: 10.15332/2422474x.6285
• Títulos paralelos:
  • Predictive power exploration of the data extracted from StockTwits over the future price variation direction of a stock traded in the New York Stock Market
• Enlaces
  • Texto completo (pdf)
• Resumen
  • español

    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

  • English

    High volume of data is generated daily, especially on social networks. The usage of this data as a source in the study of the agent’s behavior in the stock market have been gaining interest, specifically in the machine learning field. Hence, in this article; a study about the predictive power of this kind of data over the future price variation direction of a stock is made, using the texts published in the StockTwits social network and machine learning techniques.

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