mlsanityData to 14 Oct 2026

HLOB -- Information Persistence and Structure in Limit Order Books

arXiv 2405.18938 ↗First posted 29 May 2024q-fin.TR cs.LGcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 25 of its 65 references that are in the map. First posted on arXiv 29 May 2024.

Builds on

25 of 65 references.

References · 25

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  4. 2018Universal features of price formation in financial markets: perspectives from Deep Learningcited by 60
  5. 2017Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methodscited by 49
  6. 2017Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysiscited by 46
  7. 2018Facial Expression Analysis under Partial Occlusion: A Surveycited by 28
  8. 2018Using Deep Learning for price prediction by exploiting stationary limit order book featurescited by 27
  9. 2021Deep Reinforcement Learning for Active High Frequency Tradingcited by 21
  10. 2015Network Filtering for Big Data: Triangulated Maximally Filtered Graphcited by 17
  11. 2020Data Normalization for Bilinear Structures in High-Frequency Financial Time-seriescited by 16
  12. 2016Parsimonious modeling with Information Filtering Networkscited by 14
13 more
  1. 2020Deep Learning modeling of Limit Order Book: a comparative perspectivecited by 11
  2. 2019Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Datacited by 9
  3. 2023LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Studycited by 9
  4. 2021Deep Learning for Market by Order Datacited by 8
  5. 2022Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series predictioncited by 8
  6. 2020Attention-based Neural Bag-of-Features Learning for Sequence Datacited by 7
  7. 2024Deep Limit Order Book Forecastingcited by 7
  8. 2020Topological regularization with information filtering networkscited by 6
  9. 2023Homological Neural Networks: A Sparse Architecture for Multivariate Complexitycited by 5
  10. 2023Limit Order Book Dynamics and Order Size Modelling Using Compound Hawkes Processcited by 5
  11. 2022Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classificationcited by 4
  12. 2022Axial-LOB: High-Frequency Trading with Axial Attentioncited by 4
  13. 2023Homological Convolutional Neural Networkscited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 4.

8 papers.

  1. 2026DiffLOB: Diffusion Models for Counterfactual Generation in Limit Order Bookscited by 2
  2. 2025Information Filtering Networks: Theoretical Foundations, Generative Methodologies, and Real-World Applicationscited by 1
  3. 2025DiffVolume: Diffusion Models for Volume Generation in Limit Order Bookscited by 1
  4. 2026Graph Regularized PCAcited by 1
  5. 2025Deep Learning Models Meet Financial Data Modalitiescited by 0
  6. 2025An Efficient deep learning model to Predict Stock Price Movement Based on Limit Order Bookcited by 0
  7. 2026Compositional Sparsity as an Inductive Bias for Neural Architecture Designcited by 0
  8. 2026The Inference-Compute Frontier and a Latency-Efficient Architecture for Limit Order Book Predictioncited by 0