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The existing literature provides evidence that limit order book data can beused to predict short-term price movements in stock markets. This paperproposes a new neural network architecture for predicting return jump arrivalsin equity markets with high-frequency limit order book data. This newarchitecture, based on Convolutional Long Short-Term Memory with Attention, isintroduced to apply time series representation learning with memory and tofocus the prediction attention on the most important features to improveperformance. The data set consists of order book data on five liquid U.S.stocks. The use of the attention mechanism makes it possible to analyze theimportance of the inclusion limit order book data and other input variables. Byusing this mechanism, we provide evidence that the use of limit order book datawas found to improve the performance of the proposed model in jump prediction,either clearly or marginally, depending on the underlying stock. This suggeststhat path-dependence in limit order book markets is a stock specific feature.Moreover, we find that the proposed approach with an attention mechanismoutperforms the multi-layer perceptron network as well as the convolutionalneural network and Long Short-Term memory model.

Forecasting of Jump Arrivals in Stock Prices: New Attention-based Network Architecture using Limit Order Book Data