Signature-Informed Transformer for Asset Allocation

ArXiv ID: 2510.03129 “View on arXiv”

Authors: Yoontae Hwang, Stefan Zohren

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

Keywords: Asset Allocation, Path Signatures, Reinforcement Learning, Transformer, Portfolio Optimization, Equities

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematics including Rough Path Theory and path signatures, and integrates a custom attention mechanism with financial inductive biases, giving a high math score. It reports out-of-sample backtests on S&P 100 data, provides code, and optimizes a direct financial objective (CVaR), indicating strong empirical grounding.

  flowchart TD
    subgraph A ["Research Goal"]
        goal["Robust Asset Allocation via<br>End-to-End Policy Learning"]
    end

    subgraph B ["Methodology"]
        sig["Path Signatures<br>Geometric Feature Extraction"]
        att["Signature-Augmented Attention<br>Lead-Lag Inductive Bias"]
        sit["Signature-Informed Transformer SIT"]
    end

    subgraph C ["Data & Experiment"]
        data["S&P 100 Daily Equity Data"]
        obj["Risk-Aware Financial Objective"]
    end

    subgraph D ["Outcomes"]
        res["Decisive Outperformance<br>vs. Predict-Then-Optimize & Baselines"]
        conc["Portfolio-Aware Objectives<br>& Geometry Biases are Essential"]
    end

    goal --> sig
    sig --> sit
    att --> sit
    sit --> obj
    data --> obj
    obj --> res
    res --> conc