Limit Order Book Modeling

Latest papers

20 papers8 papers in the last four weeks, up 100% on the four weeks before. 0.0% of all new papers.

Understanding the Inner Workings of a State-Space Foundation Model for Limit Order Books
Oct 20, 2026Valentin Mohl, Alexandre Bismuth, Aramis Marti-Shahandeh +6Limit Order Book Modeling State Representation Learning
British Open-ended Learning and Discovery Lab (BOLD), Department of Computer Science, University of Oxford, Oxford, United Kingdom · Harvard University, Cambridge, MA, United States · British Open-ended Learning and Discovery Lab (BOLD), Department of Engineering Science, University of Oxford, Oxford, United Kingdom +3
LOB-Impact Benchmark: Emergent Market Impact in Generative Limit Order Book Models
Oct 19, 2026George Nigmatulin, Valentin Mohl, Sascha Frey +6Limit Order Book Modeling Generative Modeling
British Open-ended Learning and Discovery Lab (BOLD), Department of Engineering Science, University of Oxford, Oxford, United Kingdom · British Open-ended Learning and Discovery Lab (BOLD), Department of Computer Science, University of Oxford, Oxford, United Kingdom · British Open-ended Learning and Discovery Lab (BOLD), Department of Statistics, University of Oxford, Oxford, United Kingdom +2
Adversarial Post-Training of Foundation Models for High-Frequency Trading
Oct 19, 2026Alexandre Bismuth, Kang Li, Aramis Marti-Shahandeh +3Limit Order Book Modeling Adversarial Training
British Open-ended Learning and Discovery Lab (BOLD), Department of Computer Science, University of Oxford, Oxford, United Kingdom · Harvard University, Cambridge, MA, United States · British Open-ended Learning and Discovery Lab (BOLD), Department of Statistics, University of Oxford, Oxford, United Kingdom +1
Probabilistic Limit Order Book Forecasting with Conditional Diffusion Models
Oct 18, 2026Junyu Chen, Franklin Liu, Moshu Xu +1Limit Order Book Forecasting Limit Order Book Modeling
Department of Statistics and Data Science, Tsinghua University, Beijing, China
OrderFlowDiT: Transport-Regularized Diffusion Transformer for Probabilistic Limit-Order Supply Forecasting
Oct 15, 2026Jung Hun Phee, Tucker BalchLimit Order Book Forecasting Limit Order Book Modeling
Georgia Institute of Technology, Atlanta, Georgia, USA · Emory University, Atlanta, Georgia, USA
StochLOB: Heavy-Tailed Score Matching for Robust Limit Order Book Trend Prediction
Oct 15, 2026Arshia Abolghasemi, Seyed Reza Tavakoli, Parsa Naderi +7Limit Order Book Forecasting Score Matching
Dataism Laboratory for Quantitative Finance · Sharif University of Technology · Amirkabir University of Technology +3
What Do Foundation Models for Market Microstructure Learn? A Controlled Study with Synthetic Limit Order Books
Oct 14, 2026Fernando AceroLimit Order Book Modeling Time-Series Foundation Models
J.P. Morgan QTR AI Research, United Kingdom
Optimal Regret for Online Market Making with Limit Order Book
Oct 7, 2026Maria Elena Vischi, Francesco Emanuele Stradi, Alberto MarchesiAlgorithmic Trading Limit Order Book Modeling
Politecnico di Milano
ReLOBGen: Replayable Limit Order Book Message Generation
Sep 26, 2026Junoh Kang, Kiseop Lee, Bohyung HanSynthetic Data Generation Limit Order Book Modeling
ECE & 3IPAI, Seoul National University · Department of Statistics, Purdue University · 3IPAI, Seoul National University
UQ-LOB: Uncertainty-Aware Limit Order Book Mid-Price Forecasting
Sep 25, 2026Derrick Gilchrist Edward Manoharan, Eljas Linna, Kestutis Baltakys +2Limit Order Book Forecasting Uncertainty Quantification
Data Science Research Centre Tampere University Tampere, Finland
Agentic Limit Order Books: Phase Transitions and Market Impact
Sep 25, 2026Jan RosenzweigLimit Order Book Modeling Phase Transitions
Repurposing Deep Limit Order Book Forecasting for Scenario-Conditioned Market Impact Modeling
Sep 15, 2026Eljas Linna, Kestutis Baltakys, Derrick Manoharan +2Limit Order Book Forecasting Financial Forecasting
Data Science Research Center, Tampere University, Finland
Do LLMs Understand Limit Order Book Dynamics?
Aug 24, 2026Junxiao Chen, Paul GlassermanLimit Order Book Modeling Latent World Models
Columbia Business School
LOB-ID: Evaluating Synthetic Market Data by Inception Distances
Aug 13, 2026Andreea Bacalum, Zhuohan Wang, Ollie Olby +2Quantitative Finance Limit Order Book Modeling
Simudyne · King's College London, United Kingdom
Volatility-Aware Extreme Event Detection in High-Frequency Financial Markets
Jul 20, 2026Maorufa Zaman, Haris Md SahedQuantitative Finance Limit Order Book Modeling
Department of Digital Business & Innovation
When Does Order Flow Matter? State-Dependent L2 Liquidity-State Transitions in Crypto Futures
Jul 10, 2026Joohyoung JeonQuantitative Finance Limit Order Book Modeling
Korea University · Seoul, South Korea
The Inference-Compute Frontier and a Latency-Efficient Architecture for Limit Order Book Prediction
Jun 24, 2026C. Evans HedgesLimit Order Book Forecasting Efficient Neural Network Inference
Independent Researcher New York, USA
When Quotes Crumble: Detecting Transient Mechanical Liquidity Erosion in Limit Order Books
Apr 23, 2026Haohan Xu, Jason Bohne, Pawel Polak +5Quantitative Finance Algorithmic Trading
Stony Brook University · Bloomberg
Early Detection of Latent Microstructure Regimes in Limit Order Books
Apr 22, 2026Prakul Sunil Hiremath, Vruksha Arun HiremathQuantitative Finance Latent Dynamics Modeling
Department of Computer Science and Engineering Visvesvaraya Technological University, Belagavi · Department of Business Administration K.L.E. Society’s College of Business Administration
LOBERT: Generative AI Foundation Model for Limit Order Book Messages
Nov 16, 2025Eljas Linna, Kestutis Baltakys, Alexandros Iosifidis +1Limit Order Book Forecasting Quantitative Finance
Data Science Research Centre Tampere University

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Understanding the Inner Workings of a State-Space Foundation Model for Limit Order Books

Valentin Mohl, Alexandre Bismuth, Aramis Marti-Shahandeh, George Nigmatulin+5

British Open-ended Learning and Discovery Lab (BOLD), Department of Computer Science, University of Oxford, Oxford, United Kingdom · Harvard University, Cambridge, MA, United States · British Open-ended Learning and Discovery Lab (BOLD), Department of Engineering Science, University of Oxford, Oxford, United Kingdom+3

Limit Order Book Modeling State Representation Learning Time-Series Foundation Models Mechanistic Interpretability State Space Models Quantitative Finance

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Autoregressive foundation models offer state-of-the-art capabilities to simulate the market by generating the message flow of limit order books (LOBs). Yet, without understanding their inner workings, research directions are hard to narrow and practical use in real-world trading environments remains uncertain. This study therefore asks what these models actually learn about market microstructure. We find that, similarly to games where sequence models build an internal representation of the board, an 8.2M-parameter Mamba-3 state-space model trained autoregressively on the tokenized message stream of eight large-cap US equities learns an internal representation of the market and its dynamics. In particular, order-flow imbalance, a central driver of short-horizon price moves, is decodable at

R^2 \approx 0.59

, far above controls and baselines. This representation extends to unseen stocks, suggesting that these models learn general representations rather than ticker-specific patterns. The model also generalizes across metrics beyond order-flow imbalance: results hold across traded volume and volatility. Additionally, the representation is causally steerable: pushing the state along a feature's axis shifts the model's next-message beliefs as that feature implies, monotonically in dose, consistent with the emergence of a world model.