英文标题：
《Forecasting of Jump Arrivals in Stock Prices: New Attention-based
Network Architecture using Limit Order Book Data》
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作者：
Ymir M\\\"akinen, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis
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最新提交年份：
2018
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英文摘要：
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 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 data set consists of order book data on five liquid U.S. stocks. The use of the attention mechanism makes it possible to analyze the importance of the inclusion limit order book data and other input variables. By using this mechanism, we provide evidence that 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.
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中文摘要：
现有文献证明，限价指令簿数据可用于预测股票市场的短期价格变动。本文提出了一种新的神经网络结构，用于预测股票市场中高频极限订单数据的收益跳跃到达。这种新的体系结构基于带注意的卷积长短时记忆，用于应用带记忆的时间序列表示学习，并将预测注意力集中在最重要的特征上，以提高性能。该数据集包括五只流动美国股票的订单数据。使用注意机制可以分析包含限制订单簿数据和其他输入变量的重要性。通过使用这一机制，我们提供了证据，证明使用限额订单数据可以明显或轻微地改善所提出模型在跳跃预测中的性能，具体取决于基础股票。这表明限价订单市场的路径依赖是股票特有的特征。此外，我们发现，所提出的具有注意机制的方法优于多层感知器网络、卷积神经网络和长-短期记忆模型。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Trading and Market Microstructure 交易与市场微观结构
分类描述：Market microstructure, liquidity, exchange and auction design, automated trading, agent-based modeling and market-making
市场微观结构，流动性，交易和拍卖设计，自动化交易，基于代理的建模和做市
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