Publicación: A novel feature engineering approach for high-frequency financial data
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Fecha
2023-10
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Atribución-NoComercial-SinDerivadas 4.0 Internacional
info:eu-repo/semantics/embargoedAccess
info:eu-repo/semantics/embargoedAccess
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Elsevier
Resumen
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.
Descripción
Categorías UNESCO
Palabras clave
Artificial intelligence in finance, High-frequency financial data, Time series segmentation, Intraday volatility, Directional forecasting
Citación
Centro
E.T.S. de Ingeniería Informática
Departamento
Informática y Automática