英文标题：
《Using Deep Learning for price prediction by exploiting stationary limit
  order book features》
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作者：
Avraam Tsantekidis, Nikolaos Passalis, Anastasios Tefas, 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 recent surge in Deep Learning (DL) research of the past decade has successfully provided solutions to many difficult problems. The field of quantitative analysis has been slowly adapting the new methods to its problems, but due to problems such as the non-stationary nature of financial data, significant challenges must be overcome before DL is fully utilized. In this work a new method to construct stationary features, that allows DL models to be applied effectively, is proposed. These features are thoroughly tested on the task of predicting mid price movements of the Limit Order Book. Several DL models are evaluated, such as recurrent Long Short Term Memory (LSTM) networks and Convolutional Neural Networks (CNN). Finally a novel model that combines the ability of CNNs to extract useful features and the ability of LSTMs\' to analyze time series, is proposed and evaluated. The combined model is able to outperform the individual LSTM and CNN models in the prediction horizons that are tested.
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中文摘要：
近十年来，深度学习（DL）研究的激增成功地解决了许多难题。定量分析领域一直在缓慢地适应新方法以解决其问题，但由于金融数据的非平稳性等问题，在充分利用DL之前，必须克服重大挑战。本文提出了一种新的构造平稳特征的方法，该方法可以有效地应用DL模型。在预测限价指令簿的中间价格变动的任务中，对这些功能进行了彻底的测试。对几种DL模型进行了评估，如递归长短时记忆（LSTM）网络和卷积神经网络（CNN）。最后，提出并评估了一种新的模型，该模型将CNN提取有用特征的能力与LSTMs分析时间序列的能力相结合。在所测试的预测范围内，组合模型能够优于单独的LSTM和CNN模型。
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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