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

• URL: http://arxiv.org/abs/2609.16930v1
• Date: Tue, 15 Sep 2026 10:06:03 GMT
• Title: Repurposing Deep Limit Order Book Forecasting for Scenario-Conditioned Market Impact Modeling
• Abstract summary: We introduce a model-agnostic framework that compares a trained forecaster's predictive distributions before and after injecting mechanically valid counterfactual messages.<n>A Transformer-based forecaster recovered scenario rankings with a Spearman correlation of 0.99 and 97.2% directional agreement with realized historical outcomes.
• Score: 49.590388401787834
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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