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的市场主体的集合，如股票、债券或衍生品。金融市场的自然基础设施是交易所，一个将买家和卖家聚集在一起、促进交易发生的实体或虚拟场所。今天，大多数交易所都是电子化的，允许交易员访问实时订单簿信息，即收集某一证券的所有常规订单。市场参与者拥有不同的技术基础设施，接收数据和提交订单的延迟不同。在过去的几十年里，高频交易一直在进行一场激烈的零延迟竞赛，在经济上做出了巨大的努力，将其延迟缩短了几微秒。在我们的研究中，我们的目标是探索这种竞赛发生的可能原因之一。具体地说，我们的目标是分析订单簿数据的预测价值，即，能够立即接触到订单簿的交易员在多大程度上能够预测市场的未来走向？实证研究表明，价格形成动态，即下一步的中间价变动是可以预测的。在我们的研究中，我们调查了这种可预测性是否在更长期的视野中持续存在。为此，我们采用深度学习体系结构，利用它们学习复杂数据依赖关系的能力。到目前为止，我们已经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.高频回报是否具有可预测性？如果是这样的话，我们能预测多远？2.哪种订单簿表示法表现最好？3.我们可以在多个视野中使用一个模型吗？4.我们可以在多个股票上使用一个模型吗？为了回答这些问题，我们使用了模型置信集，这是一种特别适合这个问题的结构化统计程序。还有一些我们希望探索的进一步问题。首先，我们希望了解订单簿的结构是否能够完全解释回报的可预测性，或者经常性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以交易，或者它是否对一些市场参与者有用，例如，帮助做市商衡量市场方向并相应调整他们的报价。Graph supOU过程虽然有各种用于图/网络时间序列的离散时间模型，但我们的研究项目将专注于连续时间设置，以允许跨时间尺度进行一致的建模并考虑不规则观测。该项目旨在扩展先前关于Graph-Ornstein Uhlenbeck(Grouu)过程的工作。我们希望考虑各种研究途径。首先，我们希望允许更灵活的自相关结构，可能会显示长记忆。在此背景下，我们可以考虑合并和发展现有的一般过程理论和多元CARMA过程的理论。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/或考虑具有自然群结构的网络。在应用中，经常会出现图的邻接矩阵的维度比时间序列观测值的数量大得多的情况。在这种情况下，一个重要的问题是我们是否能够一致地估计邻接矩阵。如果是这样的话，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，具有随时间演变的图结构股票的已实现波动性和汇率对。本项目属于EPSRC的以下研究领域：人工智能技术、数字信号处理和统计与应用概率。

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.