Abstract
Robust asset allocation is a key challenge in quantitative finance, where deep-learning forecasters often fail due to objective mismatch and error amplification. We introduce the Signature-Informed Transformer (SIT), a novel framework that learns end-to-end allocation policies by directly optimizing a risk-aware financial objective. SIT's core innovations include path signatures for a rich geometric representation of asset dynamics and a signature-augmented attention mechanism embedding financial inductive biases, like lead-lag effects, into the model. Evaluated on daily S\&P 100 equity data, SIT decisively outperforms traditional and deep-learning baselines, especially when compared to predict-then-optimize models. These results indicate that portfolio-aware objectives and geometry-aware inductive biases are essential for risk-aware capital allocation in machine-learning systems. The code is available at: this https URL
Abstract (translated)
稳健的资产配置是量化金融中的一个关键挑战，深度学习预测器常常由于目标不匹配和错误放大而失效。我们引入了签名信息Transformer（SIT），这是一种新颖的框架，通过直接优化风险意识的财务目标来端到端地学习资产配置策略。SIT的核心创新包括用于丰富几何表示资产动态路径签名以及将如领先-滞后效应等金融归纳偏差嵌入模型中的签名增强注意力机制。 在对每日S&P 100股票数据进行评估时，SIT显著优于传统的和基于深度学习的基准方法，尤其是在与预测然后优化模型相比时。这些结果表明，在机器学习系统中，针对投资组合的目标意识以及几何感知的归纳偏差对于风险认知资本配置至关重要。 代码可在以下链接获取：[此链接](this https URL)
URL
https://arxiv.org/abs/2510.03129
