Deep Learning Modeling of the Limit Order Book: A Comparative Perspective

Briola, Antonio; Turiel, Jeremy; Aste, Tomaso

doi:10.2139/ssrn.3714230

Cited by 17 publications

(12 citation statements)

References 18 publications

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“…Deep Learning (Goodfellow et al 2016) algorithms have been heavily used for predicting high-frequency microstructure data (Tsantekidis et al 2017;Sirignano and Cont 2019;Briola, Turiel, and Aste 2020;Wallbridge 2020). In particular, Roberts (2018, 2019a,b) apply convolutional neural networks and LSTMs to model the dynamics of LOB and demonstrate accuracy improvements over linear models.…”

Section: Literaturementioning

confidence: 99%

“…Propelled by the publication of the benchmark dataset (Ntakaris et al 2018) of high-frequency limit order book (LOB) data, there has been a growing interest in research studying LOB data. Recent works by Tsantekidis et al (2017); Sirignano and Cont (2019); Zhang, Zohren, and Roberts (2019a); Briola, Turiel, and Aste (2020) demonstrate that strong predictive performance can be obtained from modelling high-frequency LOB data and with resulting predictions finding applications in market-making and trade execution which have short holding periods.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning for Market by Order Data

Zhang¹,

Lim²,

Zohren³

2021

Preprint

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Market by order (MBO) data -a detailed feed of individual trade instructions for a given stock on an exchange -is arguably one of the most granular sources of microstructure information. While limit order books (LOBs) are implicitly derived from it, MBO data is largely neglected by current academic literature which focuses primarily on LOB modelling. In this paper, we demonstrate the utility of MBO data for forecasting high-frequency price movements, providing an orthogonal source of information to LOB snapshots. We provide the first predictive analysis on MBO data by carefully introducing the data structure and presenting a specific normalisation scheme to consider level information in order books and to allow model training with multiple instruments. Through forecasting experiments using deep neural networks, we show that while MBO-driven and LOB-driven models individually provide similar performance, ensembles of the two can lead to improvements in forecasting accuracy -indicating that MBO data is additive to LOB-based features.

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Section: Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Learning for Market by Order Data

Zhang¹,

Lim²,

Zohren³

2021

Preprint

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“…It is also one of the cornerstones of modern quantitative finance. At the finest granular level, in finance, modeling the levels of limit order book is a multivariate problem for high-frequency trading with the aim of mid-price prediction [7,8]. For longer-term investment like portfolio management [45,74], prices of assets in a portfolio are usually multivariate time-series.…”

Section: Introductionmentioning

confidence: 99%

Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-series

Wang¹,

Aste²

2022

Preprint

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We propose an end-to-end architecture for multivariate time-series prediction that integrates a spatial-temporal graph neural network with a matrix filtering module. This module generates filtered (inverse) correlation graphs from multivariate time series before inputting them into a GNN. In contrast with existing sparsification methods adopted in graph neural network, our model explicitly leverage time-series filtering to overcome the low signal-to-noise ratio typical of complex systems data. We present a set of experiments, where we predict future sales from a synthetic time-series sales dataset. The proposed spatial-temporal graph neural network displays superior performances with respect to baseline approaches, with no graphical information, and with fully connected, disconnected graphs and unfiltered graphs. CCS CONCEPTS• Computing methodologies → Machine learning approaches.

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“…Currently, modern signal processing and machine learning community have also applied data-driven modelling to return and volatility prediction (Glosten et al (1993); Zhong & Enke (2019); Briola et al (2020Briola et al ( , 2021). However, it is well understood that these models tend to be prone to overfitting and large noise in the low signal-to-noise ratio financial environment.…”

Section: Introductionmentioning

confidence: 99%