用路径签名统一预测与投资决策，提升资产配置鲁棒性。
Signature-Informed Transformer for Asset Allocation
- 用路径签名捕捉资产价格复杂依赖关系
- 直接优化风险价值，训练目标贴合金融目标
- 适合量化投资、金融工程研究者参考
当前资产配置的深度学习方法通常将预测与优化分离，导致最小化预测误差无法带来稳健投资组合。本文提出签名感知变换器（Signature-Informed Transformer），将特征提取与决策过程整合为单一策略。模型利用路径签名编码复杂的路径依赖关系，并引入专为几何资产关系设计的注意力机制。通过直接最小化条件风险价值（Conditional Value at Risk），确保训练目标与金融目标一致。理论证明注意力模块能严格放大由签名生成的信号。在多个股票市场中进行实验，结果表明该方法显著优于传统策略及先进预测基线。代码已公开于：https://anonymous.4open.science/r/Signature-Informed-Transformer-For-Asset-Allocation-DB88。
原文摘要 · Abstract (English)
Modern deep learning for asset allocation typically separates forecasting from optimization. We argue this creates a fundamental mismatch where minimizing prediction errors fails to yield robust portfolios. We propose the Signature Informed Transformer to address this by unifying feature extraction and decision making into a single policy. Our model employs path signatures to encode complex path dependencies and introduces a specialized attention mechanism that targets geometric asset relationships. By directly minimizing the Conditional Value at Risk we ensure the training objective aligns with financial goals. We prove that our attention module rigorously amplifies signature derived signals. Experiments across diverse equity universes show our approach significantly outperforms both traditional strategies and advanced forecasting baselines. The code is available at: https://anonymous.4open.science/r/Signature-Informed-Transformer-For-Asset-Allocation-DB88
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