Newest papers

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

George Nigmatulin, Valentin Mohl, Sascha Frey, Kang Li, Aramis Marti-Shahandeh, Mihai Cucuringu, Anisoara Calinescu, Jakob Foerster, Stefan Zohren · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.27100 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

如果一个模拟器误估了这个机制,代理商在模拟器中学习的策略是最佳的,但在现实市场中错误的。现有的指标仅通过与历史数据的分布比较来评估生成的流量,留下对代理人自己的订单的反事实反应在很大程度上未被探索。在这里,我们展示了市场影响在模拟器中自然产生,但在现实市场中只有为下一个代币预测而训练的神经LOB发电机。我们将执行式代码注入到五个NASDAQ股票上产生的五个自我退缩型号的发行式消息流量,并使用从实验性影响文献中估计的响应,在基线上验证了其未来的物理学。我们从可参与式窗口到可见的家庭基准评估了我们如何在

Machine translation (Meta M2M100); the English original follows.

Abstract

Realistic market impact is essential for generative limit order book (LOB) models to serve as world models for trading agents. If a simulator misprices this mechanism, agents learn policies that are optimal in the simulator but wrong in the real market. Existing metrics assess generated streams purely through distributional comparison with historical data, leaving counterfactual response to an agent's own orders largely unexplored. Here, we show that market impact emerges naturally in neural LOB generators trained solely for next-token prediction. We inject execution-style metaorders into the generated message flow of five autoregressive models across five NASDAQ stocks and measure the response using estimators from the empirical impact literature, validated on baselines whose physics is known in advance. Our benchmark evaluates any message-level generator in three phases: free generation (reading order-flow persistence against price diffusivity); metaorder injection (yielding the impact law's exponent and amplitude); and a post-execution window (measuring relaxation). In addition, comparing each visible injection to a blind, simulator-only control isolates the model's learned response from standard book mechanics. For every model on every stock, we measure how the impact responds as a function of cumulative metaorder participation. Interestingly, we observe emergent impact in generative models of market data, with exponents in the range of 0.63 to 1.21, slightly above the traditional 0.5 of the square-root law of impact. In the family of models tested, we are so far unable to observe impact decay during relaxation. We tentatively attribute this to the still-short context windows of the models. We release the benchmark as open source to validate future generative models for market data.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Counterfactual thinking, Benchmark (surveying), Context (archaeology), Market impact, Econometrics, Generative grammar

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange