HLOB–Information persistence and structure in limit order books

Antonio Briola, Silvia Bartolucci and Tomaso Aste

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: deep learning; eEconophysics; High frequency trading; limit order book; market microstructure (search for similar items in EconPapers)
JEL-codes: C45 C53 D50 D51 D53 (search for similar items in EconPapers)
Pages: 16 pages
Date: 2025-03-25
New Economics Papers: this item is included in nep-big, nep-cmp and nep-mst
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Published in Expert Systems With Applications, 25, March, 2025, 266. ISSN: 0957-4174

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