Paper: arXiv 2510.03129

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

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 6.0/10 (capped from 7.0: transaction costs only partially modelled; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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.

Research Flowchart

  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

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2 of 63 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.