英文标题：
《Sequence Classification of the Limit Order Book using Recurrent Neural
  Networks》
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作者：
Matthew F Dixon
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最新提交年份：
2017
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英文摘要：
  Recurrent neural networks (RNNs) are types of artificial neural networks (ANNs) that are well suited to forecasting and sequence classification. They have been applied extensively to forecasting univariate financial time series, however their application to high frequency trading has not been previously considered. This paper solves a sequence classification problem in which a short sequence of observations of limit order book depths and market orders is used to predict a next event price-flip. The capability to adjust quotes according to this prediction reduces the likelihood of adverse price selection. Our results demonstrate the ability of the RNN to capture the non-linear relationship between the near-term price-flips and a spatio-temporal representation of the limit order book. The RNN compares favorably with other classifiers, including a linear Kalman filter, using S&P500 E-mini futures level II data over the month of August 2016. Further results assess the effect of retraining the RNN daily and the sensitivity of the performance to trade latency.
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中文摘要：
递归神经网络（RNN）是一种非常适合预测和序列分类的人工神经网络（ANN）。它们已被广泛应用于预测单变量金融时间序列，但其在高频交易中的应用尚未被考虑。本文解决了一个序列分类问题，在该问题中，使用对限价指令簿深度和市场订单的短序列观察来预测下一事件的价格翻转。根据该预测调整报价的能力降低了不利价格选择的可能性。我们的结果表明，RNN能够捕捉短期价格波动与限价订单簿时空表示之间的非线性关系。RNN在2016年8月使用S&P500 E-mini期货二级数据，与其他分类器（包括线性卡尔曼滤波器）相比，具有优势。进一步的结果评估了每天重新训练RNN的效果以及性能对交易延迟的敏感性。
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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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