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Transformer models repurposed for market impact analysis · arXiv cs.AI

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new framework to evaluate the market impact of counterfactual order book messages using deep learning models. This model-agnostic approach compares a forecaster's predictions before and after injecting simulated messages, defining short-horizon market impact. A Transformer-based forecaster demonstrated high accuracy, achieving a Spearman correlation of 0.99 and 97.2% directional agreement, indicating that pre-trained Limit Order Book forecasters can be repurposed for scenario-conditioned response modeling without retraining. AI

IMPACT This research demonstrates a novel method for repurposing existing deep learning models for market impact analysis, potentially improving financial forecasting and risk assessment.

RANK_REASON The cluster contains an academic paper detailing a new modeling framework and experimental results.

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Transformer models repurposed for market impact analysis · arXiv cs.AI

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Eljas Linna, Kestutis Baltakys, Derrick Manoharan, Alexandros Iosifidis, Juho Kanniainen ·

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

    arXiv:2609.16930v1 Announce Type: cross 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 frame…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    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 predicti…

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