State Dependent Parallel Neural Hawkes Process for Limit Order Book Event Stream Prediction and Simulation

Zijian Shi; John P Cartlidge

doi:10.1145/3534678.3539462

State Dependent Parallel Neural Hawkes Process for Limit Order Book Event Stream Prediction and Simulation

Zijian Shi^*, John P Cartlidge

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

9 Citations (Scopus)

704 Downloads (Pure)

Abstract

The majority of trading in financial markets is executed through a limit order book (LOB). The LOB is an event-based continuously-updating system that records contemporaneous demand (“bids” to buy) and supply (“asks” to sell) for a financial asset. Following recent successes in the literature that combine stochastic point processes with neural networks to model event stream patterns, we propose a novel state-dependent parallel neural Hawkes process to predict LOB events and simulate realistic LOB data. The model is characterized by: (1) separate intensity rate modelling for each event type through a parallel structure of continuous time LSTM units; and (2) an event-state interaction mechanism that improves prediction accuracy and enables efficient sampling of the event-state stream. We first demonstrate the superiority of the proposed model over traditional stochastic or deep learning models for predicting event type and time of a real world LOB dataset. Using stochastic point sampling from a well trained model, we then develop a realistic deep learning-based LOB simulator that exhibits multiple stylized facts found in real LOB data.

Original language	English
Title of host publication	KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery (ACM)
Pages	1607-1615
Number of pages	9
ISBN (Electronic)	9781450393850
DOIs	https://doi.org/10.1145/3534678.3539462
Publication status	Published - 14 Aug 2022
Event	28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Washington DC, United States Duration: 14 Aug 2022 → 18 Aug 2022 Conference number: 28 https://kdd.org/kdd2022/

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
ISSN (Print)	2154-817X

Conference

Conference	28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Abbreviated title	KDD
Country/Territory	United States
City	Washington DC
Period	14/08/22 → 18/08/22
Internet address	https://kdd.org/kdd2022/

Bibliographical note

Funding Information:
ZS’s PhD is funded by a China Scholarship Council / University of Bristol joint-scholarship. JC is sponsored by Refinitiv.

Publisher Copyright:
© 2022 ACM.

Keywords

Time series analysis
Neural networks (NNs)
Limit Order Book

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Shi, Z., & Cartlidge, J. P. (2022). State Dependent Parallel Neural Hawkes Process for Limit Order Book Event Stream Prediction and Simulation. In KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 1607-1615). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery (ACM). https://doi.org/10.1145/3534678.3539462

Shi, Zijian ; Cartlidge, John P. / State Dependent Parallel Neural Hawkes Process for Limit Order Book Event Stream Prediction and Simulation. KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery (ACM), 2022. pp. 1607-1615 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

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abstract = "The majority of trading in financial markets is executed through a limit order book (LOB). The LOB is an event-based continuously-updating system that records contemporaneous demand (“bids” to buy) and supply (“asks” to sell) for a financial asset. Following recent successes in the literature that combine stochastic point processes with neural networks to model event stream patterns, we propose a novel state-dependent parallel neural Hawkes process to predict LOB events and simulate realistic LOB data. The model is characterized by: (1) separate intensity rate modelling for each event type through a parallel structure of continuous time LSTM units; and (2) an event-state interaction mechanism that improves prediction accuracy and enables efficient sampling of the event-state stream. We first demonstrate the superiority of the proposed model over traditional stochastic or deep learning models for predicting event type and time of a real world LOB dataset. Using stochastic point sampling from a well trained model, we then develop a realistic deep learning-based LOB simulator that exhibits multiple stylized facts found in real LOB data.",

keywords = "Time series analysis, Neural networks (NNs), Limit Order Book",

author = "Zijian Shi and Cartlidge, {John P}",

note = "Funding Information: ZS{\textquoteright}s PhD is funded by a China Scholarship Council / University of Bristol joint-scholarship. JC is sponsored by Refinitiv. Publisher Copyright: {\textcopyright} 2022 ACM.; 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ; Conference date: 14-08-2022 Through 18-08-2022",

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Shi, Z & Cartlidge, JP 2022, State Dependent Parallel Neural Hawkes Process for Limit Order Book Event Stream Prediction and Simulation. in KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery (ACM), pp. 1607-1615, 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington DC, District of Columbia, United States, 14/08/22. https://doi.org/10.1145/3534678.3539462

State Dependent Parallel Neural Hawkes Process for Limit Order Book Event Stream Prediction and Simulation. / Shi, Zijian; Cartlidge, John P.
KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery (ACM), 2022. p. 1607-1615 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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N2 - The majority of trading in financial markets is executed through a limit order book (LOB). The LOB is an event-based continuously-updating system that records contemporaneous demand (“bids” to buy) and supply (“asks” to sell) for a financial asset. Following recent successes in the literature that combine stochastic point processes with neural networks to model event stream patterns, we propose a novel state-dependent parallel neural Hawkes process to predict LOB events and simulate realistic LOB data. The model is characterized by: (1) separate intensity rate modelling for each event type through a parallel structure of continuous time LSTM units; and (2) an event-state interaction mechanism that improves prediction accuracy and enables efficient sampling of the event-state stream. We first demonstrate the superiority of the proposed model over traditional stochastic or deep learning models for predicting event type and time of a real world LOB dataset. Using stochastic point sampling from a well trained model, we then develop a realistic deep learning-based LOB simulator that exhibits multiple stylized facts found in real LOB data.

AB - The majority of trading in financial markets is executed through a limit order book (LOB). The LOB is an event-based continuously-updating system that records contemporaneous demand (“bids” to buy) and supply (“asks” to sell) for a financial asset. Following recent successes in the literature that combine stochastic point processes with neural networks to model event stream patterns, we propose a novel state-dependent parallel neural Hawkes process to predict LOB events and simulate realistic LOB data. The model is characterized by: (1) separate intensity rate modelling for each event type through a parallel structure of continuous time LSTM units; and (2) an event-state interaction mechanism that improves prediction accuracy and enables efficient sampling of the event-state stream. We first demonstrate the superiority of the proposed model over traditional stochastic or deep learning models for predicting event type and time of a real world LOB dataset. Using stochastic point sampling from a well trained model, we then develop a realistic deep learning-based LOB simulator that exhibits multiple stylized facts found in real LOB data.

KW - Time series analysis

KW - Neural networks (NNs)

KW - Limit Order Book

U2 - 10.1145/3534678.3539462

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M3 - Conference Contribution (Conference Proceeding)

T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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EP - 1615

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ER -

Shi Z, Cartlidge JP. State Dependent Parallel Neural Hawkes Process for Limit Order Book Event Stream Prediction and Simulation. In KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery (ACM). 2022. p. 1607-1615. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/3534678.3539462

State Dependent Parallel Neural Hawkes Process for Limit Order Book Event Stream Prediction and Simulation

Abstract

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