arXiv Machine Learning
LOB-Impact Benchmark: Emergent Market Impact in Generative Limit Order Book Models
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arXiv Machine Learning
Oct 22
Understanding the Inner Workings of a State-Space Foundation Model for Limit Order Books
arXiv:2610. 28753v1 Announce Type: cross Abstract: Autoregressive foundation models offer state-of-the-art capabilities to simulate the market by generating the message flow of limit order books (LOBs).
arXiv Machine Learning
Oct 15
What Do Foundation Models for Market Microstructure Learn? A Controlled Study with Synthetic Limit Order Books
arXiv:2610.20104v1 Announce Type: new Abstract: Autoregressive transformers trained on limit order book (LOB) message streams are increasingly proposed as foundation models for market microstructure....
arXiv Machine Learning
Oct 12
Market Impact Estimation via Causal Foundation Models
The paper introduces a Causal Market Simulator using a continuous‑time causal prior‑data fitted network (PFN) to estimate market impact, addressing bias from informational flow that traditional models overlook. On a synthetic market prior with adjustable confounding, the PFN matches an unbiased oracle while a naive estimator overestimates by up to 68%. In zero‑shot tests on real limit‑order books and equity data, the model’s intervention responses align with ground‑truth scales and improve correlation with measured impact when a recurrent state‑space variant is used.
arXiv Machine Learning
Aug 21
M3: A State-Event Generative Foundation Model for Market Microstructure Dynamics
arXiv:2608. 19227v1 Announce Type: cross Abstract: Market microstructure simulation aims to model how liquidity, prices, and order flow evolve in electronic financial markets.
arXiv Machine Learning
Aug 14
FlowLOB: Efficient and Controllable Limit Order Book Generation with Flow Matching
arXiv:2608. 13096v1 Announce Type: new Abstract: Limit order book (LOB) simulators are most useful to practitioners when they combine realistic market dynamics, computationally efficient sampling, controllable scenario generation, and the ability to generalize beyond the instruments seen during training---properties that existing agent-based and deep generative simulators provide only partially.
arXiv Machine Learning
Sep 30
ReLOBGen: Replayable Limit Order Book Message Generation
arXiv:2609.35867v1 Announce Type: new Abstract: We propose ReLOBGen, a method for generating limit order book (LOB) messages that are replayable by construction. Replayability is required for closed-...