Limit Order Book Forecasting

Latest papers

10 papers4 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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
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
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
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
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
TIP-Search: Time-Predictable Inference Scheduling for Market Prediction under Uncertain Load
May 30, 2025Xibai WangLimit Order Book Forecasting Online Convex Optimization
NeuroQuant Labs Limited Hong Kong SAR, China

Oct 18, 2026J/K move · Enter open · S save

Probabilistic Limit Order Book Forecasting with Conditional Diffusion Models

Junyu Chen, Franklin Liu, Moshu Xu, Lijian Yang

Department of Statistics and Data Science, Tsinghua University, Beijing, China

Limit Order Book Forecasting Limit Order Book Modeling Time Series Forecasting Probabilistic Time Series Forecasting Conditional Diffusion Models

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Modeling limit order book (LOB) dynamics is central to market forecasting and execution-strategy evaluation. While diffusion models achieve strong empirical performance, existing LOB snapshot models typically lack a learned recurrent encoder and formal statistical guarantees. We introduce LOBGrad, a conditional DDPM framework for probabilistic LOB forecasting that combines an autoregressive RNN encoder with a diffusion-based generator. In particular, we establish an excess-risk oracle inequality for the one-step DDPM noise-prediction objective under exponential mixing and regularity assumptions. We further show that, under contractive dynamics, the dependence on the conditioning-window length can be reduced to a dependence on the latent-state dimension. Experiments on BTC/USDT, GOOGL, and META show that LOBGrad improves on its encoder-free diffusion ablation across all reported queue metrics and obtains competitive results against other baselines. It also reproduces the key stylized facts of LOB microstructure, and yields reasonably calibrated queue-based predictive intervals. A stylized downstream task illustrates the potential value of the generated forecasts.