Repurposing Deep Limit Order Book Forecasting for Scenario-Conditioned Market Impact Modeling

Abstract:Deep Limit Order Book forecasting models capture nonlinear market dynamics, but their ability to quantify the effects of counterfactual order book messages has not been systematically validated. We introduce a model-agnostic framework that compares a trained forecaster's predictive distributions before and after injecting mechanically valid counterfactual messages, defining short-horizon model-implied market impact. A Transformer-based forecaster recovered scenario rankings with a Spearman correlation of 0.99 and 97.2% directional agreement with realized historical outcomes among non-neutral scenarios. Observation-level analysis further showed that estimated impacts captured incremental sequence-dependent variation beyond scenario identity and the pre-event forecast. These results provide evidence that pretrained Limit Order Book forecasters can be repurposed for scenario-conditioned response modeling without retraining.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.16930 [cs.LG]

(or arXiv:2609.16930v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.16930 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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