深度限价订单簿预测模型的场景条件市场冲击建模再利用

中文摘要 AI 辅助

深度限价订单簿预测模型能够捕捉非线性市场动态，但其量化反事实订单簿消息影响的能力尚未得到系统验证。我们提出了一种模型无关框架，通过比较训练好的预测器在注入机械上有效的反事实消息前后的预测分布，定义了短时域模型隐含的市场冲击。基于Transformer的预测器在非中性场景中，其场景排序与历史实际结果的Spearman相关系数达到0.99，方向一致性为97.2%。观察层面分析进一步表明，估计的冲击捕捉了超出场景身份和事件前预测的增量序列依赖变异。这些结果证明，预训练的限价订单簿预测器无需重新训练即可用于场景条件响应建模。

英文摘要

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.

发表机构

• Tampere University（坦佩雷大学）

机构由 AI 辅助整理，请以论文原文为准。