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

2026-09-15

AI Summary

研究通过引入模型无关框架，利用深度限价订单簿预测器量化假设订单信息的市场影响，验证其在不同情景下的有效性。

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.

Problem

• Deep Limit Order Book
• market impact
• counterfactual messages
• scenario-conditioned modeling

Innovation

• model-agnostic framework
• counterfactual order book messages
• scenario-conditioned market impact
• Transformer-based forecaster