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: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: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...
arXiv:2608. 19227v1 Announce Type: cross Abstract: Market microstructure simulation aims to model how liquidity, prices, and order flow evolve in electronic financial markets.
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.
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.
arXiv:2609.06085v1 Announce Type: cross Abstract: Understanding the joint dynamics of prices and trades is central to market microstructure, where returns and order flow interact through nonlinear an...
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.
The paper proposes an adversarial reinforcement‑learning framework for market making that incorporates Hawkes‑process driven order arrivals and trade‑induced price impact, addressing limitations of prior Poisson‑based models. An LSTM module captures temporal dependencies in recent observations to handle increased non‑stationarity, and the authors analyze equilibrium properties and introduce a robustness evaluation protocol focused on the left tail of returns. Experiments across diverse market regimes demonstrate that the method improves left‑tail performance, especially under strong Hawkes excitation and moderate price impact, without relying on a terminal inventory bias.
arXiv:2609.13825v1 Announce Type: new Abstract: Reinforcement learning trading systems published in the academic literature overwhelmingly rely on price-aggregate state representations (OHLCV bars) o...
arXiv:2607. 09230v1 Announce Type: cross Abstract: Building event-conditioned market models requires separating macro-event labels from persistent microstructure state.
arXiv:2607. 12248v1 Announce Type: cross Abstract: Large pretrained time-series models such as TimesFM are attractive for financial forecasting, but raw directional accuracy is a misleading scoreboard in equity markets.
arXiv:2610.10323v1 Announce Type: new Abstract: Short-horizon realized volatility forecasting requires the integration of market information that evolves at incompatible temporal resolutions, from se...