arXiv Machine Learning By Valentin Mohl, Alexandre Bismuth, Aramis Marti-Shahandeh, George Nigmatulin, Kang Li, Stefan Zohren, Jakob Foerster, Mihai Cucuringu, Anisoara Calinescu

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

Read the original on arXiv Machine Learning →

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).

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arXiv Machine Learning
Oct 21

LOB-Impact Benchmark: Emergent Market Impact in Generative Limit Order Book Models

arXiv:2610.27100v1 Announce Type: cross Abstract: Realistic market impact is essential for generative limit order book (LOB) models to serve as world models for trading agents. If a simulator mispric...

By George Nigmatulin, Valentin Mohl, Sascha Frey, Kang Li, Aramis Marti-Shahandeh, Mihai Cucuringu, Anisoara Calinescu, Jakob Foerster, Stefan Zohren
arXiv AI
Sep 28

UQ-LOB: Uncertainty-Aware Limit Order Book Mid-Price Forecasting

UQ-LOB is a lightweight, encoder‑agnostic module that adds uncertainty quantification to any pretrained limit order book (LOB) encoder. It offers two variants: UQ‑regression, which outputs a calibrated Gaussian over future tick displacement, and UQ‑classification, which outputs a categorical distribution over down/up/stationary. On 5.2 billion LOB events across seven cryptocurrency assets, UQ‑regression achieves near‑nominal 68 % interval coverage, and selecting the top 10 % most confident predictions boosts directional macro F1 by 0.11–0.15 for regression and 0.05–0.11 for classification, reaching F1 scores of 0.88 (down) and 0.83 (up) at a 5‑second horizon.

By Derrick Gilchrist Edward Manoharan, Eljas Linna, Kestutis Baltakys, Hao Dong, Juho Kanniainen
arXiv AI
Aug 26

Do LLMs Understand Limit Order Book Dynamics?

A large language model trained on synthetic limit order book data can generate valid sequences of LOB events with near‑perfect accuracy, yet its internal world model does not capture the true state of the book. This shortfall results in biased estimates and misleading predictability when the model is used to forecast future LOB events. The study introduces new tests for an LLM’s world model, extending previous deterministic analyses to the stochastic dynamics inherent in limit order books.

By Junxiao Chen, Paul Glasserman