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

使用订单簿数据预测高频回报的深度学习技术

• 批准号: 2602120
• 金额: --
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2602120
• 关键词: Deep; Learning; Techniques; predicting; Frequency

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.

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）的市场主体的集合。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的物理或虚拟场所，促进交易的发生。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，收集给定证券的所有常规指令。市场参与者有不同的技术基础设施，在不同的时间接收数据和提交订单。在过去的几十年里，高频技术参与了一场激烈的零延迟竞赛，付出了巨大的经济努力来将其延迟减少几微秒。我们在研究中的目标是探索为什么会发生这样的比赛的可能原因之一。具体来说，我们的目标是分析订单数据的预测价值，即，一个能直接看到订单簿的交易者能在多大程度上预测市场的未来走向？实证研究表明，价格形成动态，即，接下来的中间价走势，是可以预测的。在我们的研究中，我们调查这种可预测性是否在更长的时间内持续存在。为此，我们采用了深度学习架构，利用它们学习复杂数据依赖关系的能力。到目前为止，我们已经进行了一个广泛的实证实验，超过一年的纳斯达克数据回答以下问题：1。高频率回报显示出可预测性吗？如果是这样，我们能预测多远？2.哪种订单帐簿表示法性能最好？3.我们可以在多个领域使用单一模型吗？4.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集，这是一种结构化的统计程序，特别适合于这个问题。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起着相关的作用。此外，我们想了解这种可预测性是否可以交易，或者它是否对某些市场参与者有用，例如，帮助做市商衡量市场的方向并相应地调整他们的报价。图形支持过程虽然有各种离散时间模型用于图形/网络时间序列，我们的研究项目将集中在持续的-时间设置，以允许跨时间尺度的一致建模，并考虑到不规则的观测。该项目旨在扩展以前在Graph上的工作，Ornstein Uhlenbeck（格鲁）过程。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，可能显示长记忆。在这种情况下，我们可以考虑合并和推进现有的格鲁过程与多变量CARMA过程的理论。或者，我们可以考虑定义一个图支持过程，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，我们可能希望在每个节点上允许多变量观测和/或考虑具有自然组结构的网络。在实际应用中，经常会出现图的邻接矩阵的维数比时间序列观测值的个数大得多的情况。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与时间演变的图形结构股票的实现波动率和汇率对。该项目福尔斯以下EPSRC研究领域：人工智能技术，数字信号处理，统计和应用概率。