LOB-Impact Benchmark: Emergent Market Impact in Generative Limit Order Book Models
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 re...
| Authors | George Nigmatulin, Valentin Mohl, Sascha Frey et al. |
|---|---|
| Primary category | q-fin.TR |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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