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arXiv AI By Eljas Linna, Kestutis Baltakys, Derrick Manoharan, Alexandros Iosifidis, Juho Kanniainen

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

Read the original on arXiv AI →

The paper demonstrates that deep limit order book forecasting models can be repurposed to quantify scenario-conditioned market impact without retraining. By injecting counterfactual order‑book messages into a trained Transformer forecaster, the authors compare predictive distributions before and after the injection, defining a short‑horizon model‑implied market impact. The approach achieves a Spearman correlation of 0.99 and 97.2% directional agreement with historical outcomes for non‑neutral scenarios, and captures incremental sequence‑dependent variation beyond scenario identity and pre‑event forecasts.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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