用深度限价单簿预测模型做市场情景影响建模
Repurposing Deep Limit Order Book Forecasting for Scenario-Conditioned Market Impact Modeling
这个研究挺有意思，作者把深度限价单簿预测模型改了个用法，用来做市场情景下的响应建模，效果还挺好，相关系数和方向一致率都特别高。
这篇论文提出了一种框架，通过在训练好的深度限价单簿预测模型中注入机械有效的反事实订单信息，来比较预测分布的变化，从而定义短期的模型隐含市场影响。一个基于Transformer的预测器在非中性情景下，其情景排名的斯皮尔曼相关系数达到0.99，与历史结果的预测方向一致率达到97.2%。观察层面的分析还表明，估计的影响捕捉到了增量序列依赖性变化，超越了情景身份和事件前预测。
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 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.