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Feature Engineering for Mid-Price Prediction with Deep Learning
2019
IEEE Access
Mid-price movement prediction based on the limit order book data is a challenging task due to the complexity and dynamics of the limit order book. So far, there have been very limited attempts for extracting relevant features based on the limit order book data. In this paper, we address this problem by designing a new set of handcrafted features and performing an extensive experimental evaluation on both liquid and illiquid stocks. More specifically, we present an extensive set of econometric
doi:10.1109/access.2019.2924353
fatcat:wraujuag5nhovmxvru7hfz37ni