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Repurposing Deep Limit Order Book Forecasting for Scenario-Conditioned Market Impact Modeling
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 s
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- arXiv · AI, language, vision and robotics · 2026-09-15T10:06:03.000Z
First collected: 2026-09-20T08:40:59.508Z. This is not the publication date.