HLOB–Information persistence and structure in limit order books

Briola, Antonio, Bartolucci, Silvia and Aste, Tomaso (2025) HLOB–Information persistence and structure in limit order books. Expert Systems With Applications, 266. ISSN 0957-4174

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Abstract

We introduce a novel large-scale deep learning model for Limit Order Book mid-price changes forecasting, and we name it ‘HLOB’. This architecture (i) exploits the information encoded by an Information Filtering Network, namely the Triangulated Maximally Filtered Graph, to unveil deeper and non-trivial dependency structures among volume levels; and (ii) guarantees deterministic design choices to handle the complexity of the underlying system by drawing inspiration from the groundbreaking class of Homological Convolutional Neural Networks. We test our model against 9 state-of-the-art deep learning alternatives on 3 real-world Limit Order Book datasets, each including 15 stocks traded on the NASDAQ exchange, and we systematically characterize the scenarios where HLOB outperforms state-of-the-art architectures. Our approach sheds new light on the spatial distribution of information in Limit Order Books and on its degradation over increasing prediction horizons, narrowing the gap between microstructural modeling and deep learning-based forecasting in high-frequency financial markets.

Item Type:	Article
Additional Information:	© 2024 The Authors
Divisions:	Systemic Risk Centre
Subjects:	H Social Sciences > HG Finance Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited:	07 Jan 2025 15:03
Last Modified:	16 Jan 2025 11:06
URI:	http://eprints.lse.ac.uk/id/eprint/126623

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