analiza aiqbench
This paper uses autoregressive neural models trained for next-token prediction as market simulators, and it proposes a benchmark for testing whether they reproduce realistic price impact of large orders. The reported exponents (0.63 to 1.21) sit near the square-root law, but the models showed no measurable impact decay after execution, which the authors attribute to short context windows. For ML readers, the useful point is the evaluation method: counterfactual injection compared against a blind control, a test that goes beyond distributional similarity to historical data.
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 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.
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.
9 pages, 4 figures, 3 tables. Accepted at ICAIF '26 (7th ACM International Conference on AI in Finance), Milan, Italy. Code: this https URL
Trading and Market Microstructure (q-fin.TR); Machine Learning (cs.LG)
arXiv:2610.27100 [q-fin.TR]
(or arXiv:2610.27100v1 [q-fin.TR] for this version)
https://doi.org/10.48550/arXiv.2610.27100 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.1145/3858063.3858909 Focus to learn more DOI(s) linking to related resources
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