Briola, A;
Bartolucci, S;
Aste, T;
(2025)
HLOB–Information persistence and structure in limit order books.
Expert Systems with Applications
, 266
, Article 126078. 10.1016/j.eswa.2024.126078.
Preview |
PDF
1-s2.0-S0957417424029452-main.pdf - Published Version Download (3MB) | Preview |
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.
| Type: | Article |
|---|---|
| Title: | HLOB–Information persistence and structure in limit order books |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.eswa.2024.126078 |
| Publisher version: | https://doi.org/10.1016/j.eswa.2024.126078 |
| Language: | English |
| Additional information: | © 2024 The Authors. Published by Elsevier Ltd. under a Creative Commons license (http://creativecommons.org/licenses/by/4.0/). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10204213 |
Loading...
Loading...Loading...Loading...Archive Staff Only
 |
View Item |
