Signature-Informed Transformer for Asset Allocation

• URL: http://arxiv.org/abs/2510.03129v1
• Date: Fri, 03 Oct 2025 15:58:21 GMT
• Title: Signature-Informed Transformer for Asset Allocation
• Authors: Yoontae Hwang, Stefan Zohren,
• Abstract summary: Signature-Informed Transformer is a framework that learns end-to-end allocation policies by directly optimizing a risk-aware financial objective.<n> evaluated on daily S&P 100 equity data, SIT decisively outperforms traditional and deep-learning baselines.<n>Results indicate that portfolio-aware objectives and geometry-aware inductive biases are essential for risk-aware capital allocation in machine-learning systems.
• Score: 9.290367832033063
• License: http://creativecommons.org/licenses/by/4.0/
• 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: https://github.com/Yoontae6719/Signature-Informed-Transformer-For-Asset-Allocation

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