A novel feature engineering approach for high-frequency financial data
Mantilla, Pablo y Dormido Canto, Sebastián . (2023) A novel feature engineering approach for high-frequency financial data. Engineering Applications of Artificial Intelligence
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Feature engineering for high-frequency financial data based on constructing dynamic data subsets, defined by time intervals in which high-frequency trends occur, is proposed. These intervals are obtained through time series segmentation. This methodology allows us to extract and analyze variables by intraday trends as well as to feed artificial intelligence models to forecast response variables in future trends. Furthermore, to show how to use this feature engineering, this methodology is applied to estimate high-frequency volatility, duration and direction linked to future intraday trends, developing multiclass classification models based on the machine learning method extreme gradient boosting. Experimentation was conducted using high-frequency financial data from the Brazil Stock Exchange, corresponding to 206 trading days related to 20 listed assets from this financial market.
The registered version of this article, first published in Engineering Applications of Artificial Intelligence , is available online at the publisher's website: Elsevier https://doi.org/10.1016/j.engappai.2023.106705
Notas adicionales
La versión registrada de este artículo, publicado por primera vez en Engineering Applications of Artificial Intelligence, está disponible en línea en el sitio web del editor: Elsevier https://doi.org/10.1016/j.engappai.2023.106705
