论文标题
收益订单市场的回报的短期可预测性：深度学习观点
The Short-Term Predictability of Returns in Order Book Markets: a Deep Learning Perspective
论文作者
论文摘要
在本文中，我们通过利用深度学习技术来对高频回报中的订单驱动可预测性进行系统的大规模分析。首先，我们介绍了订单书《卷代表》的新的稳健代表。接下来，我们进行了广泛的经验实验，以解决有关可预测性的各种问题。我们调查了是否有可预测性，强大的数据表示的重要性，多晶体建模的优势以及通用交易模式的存在的重要性。我们使用模型置信度集，该集合提供了一个正式的统计推理框架，特别适合回答这些问题。我们的发现表明，在高频中，中价回报中的可预测性不仅存在，而且是无处不在的。深度学习模型的性能在很大程度上取决于订单书表示的选择，在这方面，卷表示似乎具有多个实际优势。
In this paper, we conduct a systematic large-scale analysis of order book-driven predictability in high-frequency returns by leveraging deep learning techniques. First, we introduce a new and robust representation of the order book, the volume representation. Next, we carry out an extensive empirical experiment to address various questions regarding predictability. We investigate if and how far ahead there is predictability, the importance of a robust data representation, the advantages of multi-horizon modeling, and the presence of universal trading patterns. We use model confidence sets, which provide a formalized statistical inference framework particularly well suited to answer these questions. Our findings show that at high frequencies predictability in mid-price returns is not just present, but ubiquitous. The performance of the deep learning models is strongly dependent on the choice of order book representation, and in this respect, the volume representation appears to have multiple practical advantages.
