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arXiv · 2609.16930

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

Eljas Linna·Kestutis Baltakys·Derrick Manoharan·Alexandros Iosifidis·Juho Kanniainen
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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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Eljas Linna, Kestutis Baltakys, Derrick Manoharan, Alexandros Iosifidis, Juho Kanniainen. 2026-09-15. Repurposing Deep Limit Order Book Forecasting for Scenario-Conditioned Market Impact Modeling. https://arxiv.org/abs/2609.16930

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