英文标题：
《DeepLOB: Deep Convolutional Neural Networks for Limit Order Books》
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作者：
Zihao Zhang, Stefan Zohren, Stephen Roberts
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最新提交年份：
2020
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英文摘要：
We develop a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities. The architecture utilises convolutional filters to capture the spatial structure of the limit order books as well as LSTM modules to capture longer time dependencies. The proposed network outperforms all existing state-of-the-art algorithms on the benchmark LOB dataset [1]. In a more realistic setting, we test our model by using one year market quotes from the London Stock Exchange and the model delivers a remarkably stable out-of-sample prediction accuracy for a variety of instruments. Importantly, our model translates well to instruments which were not part of the training set, indicating the model\'s ability to extract universal features. In order to better understand these features and to go beyond a \"black box\" model, we perform a sensitivity analysis to understand the rationale behind the model predictions and reveal the components of LOBs that are most relevant. The ability to extract robust features which translate well to other instruments is an important property of our model which has many other applications.
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中文摘要：
我们开发了一个大规模的深度学习模型，从现金股票的限额订单簿（LOB）数据预测价格变动。该体系结构使用卷积滤波器来捕获限额订单簿的空间结构，并使用LSTM模块来捕获更长的时间依赖关系。所提出的网络在基准LOB数据集上的性能优于所有现有的最先进算法【1】。在更现实的环境中，我们使用伦敦证券交易所的一年市场报价来测试我们的模型，该模型为各种工具提供了非常稳定的样本外预测精度。重要的是，我们的模型可以很好地转换为不属于训练集的工具，这表明该模型具有提取通用特征的能力。为了更好地理解这些特征并超越“黑箱”模型，我们进行了敏感性分析，以了解模型预测背后的基本原理，并揭示最相关的LOB组成部分。提取能够很好地转换到其他工具的鲁棒特征是我们的模型的一个重要特性，该模型具有许多其他应用。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Computational Finance 计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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