Forecasting jump arrivals in stock prices: new attention-based network architecture using limit order book data

The existing literature provides evidence that limit order book data can be used to predict short-term price movements in stock markets. This paper proposes a new neural network architecture for predicting return jump arrivals one minute ahead in equity markets with high-frequency limit order book data. This new architecture, based on Convolutional Long Short-Term Memory with Attention, is introduced to apply time series representation learning with memory and to focus the prediction attention on the most important features to improve performance. The use of the attention mechanism makes it possible to analyze the importance of the inclusion limit order book data and other input variables. Our architecture with this mechanism is used and compared to existing deep learning architectures with the data set that consists of order book data on five liquid U.S. stocks over 18 months.