M3: A State-Event Generative Foundation Model for Market Microstructure Dynamics
Not on the map. Why?
Abstract (arXiv)
Market microstructure simulation aims to model how liquidity, prices, and order flow evolve in electronic financial markets. Since market data reveal only one realized trajectory, many important questions are inherently counterfactual and require realistic trajectory-level simulation. Existing financial generative models, however, often model order events and market states, such as the LOB, in isolation, overlooking the dynamic interaction between order flow and liquidity in market microstructure. We propose the M3 (underlineMarket underlineMicrostructure underlineModel), a state-event generative foundation model for market microstructure dynamics. M3 learns to generate future order-flow trajectories, while accounting for the evolving interaction between order events and limit-order-book liquidity. Trained on large-scale order-level real stock market data, M3 exhibits predictable scaling behavior, reproduces key market stylized facts, and enables practical simulation-based applications including forecasting, stress testing, and market-impact analysis. These results suggest a scalable foundation-model paradigm for counterfactual market simulation at the microstructure level.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 39 references that are in the map. First posted on arXiv 29 Jul 2026.
Builds on
19 of 39 references.
References · 19
- 2017Attention Is All You Needcited by 60,361
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
- 2020Scaling Laws for Neural Language Modelscited by 6,586
- 2017Neural Discrete Representation Learningcited by 4,712
- 2021RoFormer: Enhanced Transformer with Rotary Position Embeddingcited by 4,703
- 2021Zero-Shot Text-to-Image Generationcited by 4,288
- 2020Taming Transformers for High-Resolution Image Synthesiscited by 3,253
- 2020GLU Variants Improve Transformercited by 1,804
- 2018World Modelscited by 1,513
- 2019Root Mean Square Layer Normalizationcited by 1,480
- 2019Generating Diverse High-Fidelity Images with VQ-VAE-2cited by 1,387
- 2021VideoGPT: Video Generation using VQ-VAE and Transformerscited by 658
7 more
- 2019ABIDES: Towards High-Fidelity Market Simulation for AI Researchcited by 32
- 2024MarS: a Financial Market Simulation Engine Powered by Generative Foundation Modelcited by 28
- 2022Learning to simulate realistic limit order book markets from data as a World Agentcited by 27
- 2025Kronos: A Foundation Model for the Language of Financial Marketscited by 26
- 2024Simulating Financial Market via Large Language Model based Agentscited by 25
- 2023Generative AI for End-to-End Limit Order Book Modelling: A Token-Level Autoregressive Generative Model of Message Flow Using a Deep State Space Networkcited by 20
- 2026TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructurecited by 6
Built on by
Not cited yet.