英文标题：
《Feature Engineering for Mid-Price Prediction with Deep Learning》
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作者：
Adamantios Ntakaris, Giorgio Mirone, Juho Kanniainen, Moncef Gabbouj,
  Alexandros Iosifidis
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最新提交年份：
2019
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英文摘要：
  Mid-price movement prediction based on limit order book (LOB) data is a challenging task due to the complexity and dynamics of the LOB. So far, there have been very limited attempts for extracting relevant features based on LOB data. In this paper, we address this problem by designing a new set of handcrafted features and performing an extensive experimental evaluation on both liquid and illiquid stocks. More specifically, we implement a new set of econometrical features that capture statistical properties of the underlying securities for the task of mid-price prediction. Moreover, we develop a new experimental protocol for online learning that treats the task as a multi-objective optimization problem and predicts i) the direction of the next price movement and ii) the number of order book events that occur until the change takes place. In order to predict the mid-price movement, the features are fed into nine different deep learning models based on multi-layer perceptrons (MLP), convolutional neural networks (CNN) and long short-term memory (LSTM) neural networks. The performance of the proposed method is then evaluated on liquid and illiquid stocks, which are based on TotalView-ITCH US and Nordic stocks, respectively. For some stocks, results suggest that the correct choice of a feature set and a model can lead to the successful prediction of how long it takes to have a stock price movement.
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中文摘要：
由于LOB的复杂性和动态性，基于LOB数据的中间价变动预测是一项具有挑战性的任务。到目前为止，基于LOB数据提取相关特征的尝试非常有限。在本文中，我们通过设计一组新的手工特征并对流动和非流动股票进行广泛的实验评估来解决这个问题。更具体地说，我们实现了一组新的计量经济学特征，这些特征捕获了基础证券的统计特性，用于中期价格预测任务。此外，我们还开发了一个新的在线学习实验协议，该协议将任务视为一个多目标优化问题，并预测i）下一次价格变动的方向，以及ii）在发生变化之前发生的订单事件的数量。为了预测中期价格变动，将这些特征输入到九种不同的深度学习模型中，这些模型基于多层感知器（MLP）、卷积神经网络（CNN）和长-短期记忆（LSTM）神经网络。然后，分别以TotalView ITCH美国和北欧股票为基础，对流动性股票和非流动性股票的绩效进行评估。对于一些股票，结果表明，正确选择特征集和模型可以成功预测股价波动所需的时间。
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分类信息：

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：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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