Deep Learning Techniques for predicting High-Frequency returns using Order Book data

金融市场是愿意购买或出售某种金融证券（如股票、债券或衍生品）的市场代理人的集合。金融市场的自然基础设施是一个交易所，一个将买卖双方聚集在一起的实体或虚拟场所，促进交易的发生。今天，大多数交易所都是电子的，允许交易者访问实时订单信息，即给定证券的所有有效订单的集合。市场参与者有不同的技术基础设施，以不同的延迟接收数据和提交订单。在过去的几十年里，高频交易一直在为零延迟进行激烈的竞争，付出了巨大的经济努力来减少延迟，仅仅是几微秒。在我们的研究中，我们的目标是探索这样一场比赛首先发生的可能原因之一。具体来说，我们的目标是分析订单数据的预测价值，即，在多大程度上可以直接访问订单的交易者预测市场的未来方向？实证研究表明，价格形成动态，即下一个中间价格走势，是可预测的。在我们的研究中，我们调查了这种可预测性是否在更长时间内持续存在。为此，我们采用深度学习架构，利用它们学习复杂数据依赖关系的能力。到目前为止，我们对纳斯达克一年多的数据进行了广泛的实证实验，以回答以下问题：1。高频回报表现出可预测性吗？如果是这样，我们能预测多久？哪种订单表表现最好？我们能在多个视界中使用单一模型吗？我们可以在多个股票中使用单一模型吗？为了回答这些问题，我们使用了模型置信度集，这是一种特别适合这个问题的结构化统计程序。我们还想进一步探讨一些问题。首先，我们想了解订单簿的结构是否可以完全解释回报的可预测性，或者重复的交易模式是否发挥了相关作用。此外，我们想了解这种可预测性是否可交易，或者它是否对一些市场参与者有用，例如，帮助做市商判断市场方向并相应地调整报价。虽然图/网络时间序列有各种离散时间模型，但我们的研究项目将重点放在连续时间设置上，以允许跨时间尺度的一致建模，并考虑不规则的观测结果。该项目旨在扩展之前关于Graph-Ornstein Uhlenbeck （GrOU）流程的工作。我们希望考虑多种研究途径。首先，我们希望允许更灵活的自相关结构，可能显示长记忆。在这种情况下，我们可以考虑将现有的GrOU过程理论与多元CARMA过程理论合并和推进。或者，我们可以考虑定义一个Graph supOU过程并开发合适的推理技术。其次，我们希望探索具有更复杂拓扑的图。例如，我们可能希望允许在每个节点上进行多变量观察和/或考虑具有自然群体结构的网络。在应用中，经常出现图的邻接矩阵的维数（大大）大于时间序列观测的数量的情况。在这种情况下，一个重要的问题是我们是否可以一致地估计邻接矩阵。如果是这样，人们可能想要检测这种网络中的稀疏性。可能的真实世界数据集与时间演化图结构股票的已实现波动率和汇率对。该项目属于EPSRC的以下研究领域：人工智能技术、数字信号处理、统计与应用概率。

A financial market is an ensemble of market agents willing to buy or sell a certain financial security, such as a stock, bond, or derivative. The natural infrastructure for a financial market is an exchange, a physical or virtual place that brings together buyers and sellers, facilitating the occurrence of transactions. Today most exchanges are electronic, allowing traders to access live order book information, i.e., the collection of all standing orders for a given security. Market participants have different technological infrastructures, receiving data and submitting orders at different latencies. Over the past few decades, HFTs have engaged in a fierce race to zero latency, making vast economic efforts to reduce their latency by just a few microseconds. What we aim to explore in our research is one of the possible reasons why such a race happened in the first place. Specifically, we aim to analyse the predictive value of order book data, i.e., to what extent can a trader with immediate access to the order book predict the market's future direction?Empirical studies have shown that price formation dynamics, i.e., next mid-price moves, are predictable. In our research, we investigate whether such predictability persists at longer horizons. To do so, we employ deep learning architectures, leveraging their ability to learn complex data dependencies. So far, we have conducted an extensive empirical experiment on over one year of Nasdaq data to answer the following questions:1. Do high-frequency returns display predictability? If so, how far ahead can we predict?2. Which order book representations perform the best?3. Can we use a single model across multiple horizons?4. Can we use a single model across multiple stocks?To answer these questions, we used model confidence sets, a structured statistical procedure particularly well suited for the problem.There are some further questions we wish to explore. First, we would like to understand whether the structure of the order book can completely explain the predictability in returns or if recurring trading patterns play a relevant role. Moreover, we would like to understand whether such predictability is tradeable or if it might be useful for some market players, for example, helping market makers gauge the market's direction and adjust their quotes accordingly.Graph supOU processesWhile there are various discrete-time models for graph/network time series, our research project will focus on the continuous-time setting to allow for consistent modelling across time scales and account for irregular observations.The project aims to extend previous work on Graph-Ornstein Uhlenbeck (GrOU) processes. There are various research avenues we wish to consider. First, we would like to allow for a more flexible autocorrelation structure, possibly displaying long memory. In this context, we could consider merging and advancing the existing theory of GrOU processes with that of multivariate CARMA processes. Alternatively, we could consider defining a Graph supOU process and developing suitable inference techniques. Second, we wish to explore graphs with more complex topologies. For example, we may want to allow multivariate observations on each node and/or consider networks with natural group structures. In applications, it is often the case that the dimension of the graph's adjacency matrix is (much) larger than the number of time series observations. In this setting, an important question is whether we can estimate the adjacency matrix consistently. If so, one might want to detect sparsity in such networks. Possible real-world data sets with time-evolving graph structures stocks' realized volatilities and exchange rate pairs.This project falls within the following EPSRC research areas: Artificial Intelligence Technologies, Digital Signal Processing, and Statistics and Applied Probability.