Forecasting jump arrivals in stock prices: new attention-based network architecture using limit order book data
Research output: Contribution to journal › Article › Scientific › peer-review
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. We provide evidence that (i) the new architecture with attention model outperforms existing architectures and (ii) the use of limit order book data was found to improve the performance of the proposed model in jump prediction, either clearly or marginally, depending on the underlying stock. This suggests that path-dependence in limit order book markets is a stock specific feature. Moreover, we find that the proposed approach with an attention mechanism outperforms the multi-layer perceptron network as well as the convolutional neural network and Long Short-Term memory model.