HLOB -- Information Persistence and Structure in Limit Order Books

• Main Authors: Briola, Antonio, Bartolucci, Silvia, Aste, Tomaso
• Format: Journal Article
• Language: English
• Published: 29-05-2024
• Subjects: Computer Science - Learning; Quantitative Finance - Trading and Microstructure
• DOI: 10.48550/arxiv.2405.18938

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.