| title | Portfolio Risk Bounds without Cross-Asset Return Covariances: Distributional Fields from Language-Model Representations |
|---|---|
| authors | Marcus Gawronsky, Chun-Sung Huang |
| date | 2026-08-30 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.29692 |
| affiliation | University of Cape Town |
| upvotes | 2 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | Portfolio risk assessment ordinarily relies on reliable estimates of cross-asset return covariances, which are difficult to obtain in short, high-dimensional panels. We show that firm-level distribution-valued characteristics can instead provide one-sided certificates of portfolio risk. Under maintained links from characteristics to systematic exposures and from exposures to returns, multi-firm Wasserstein-2 dispersion yields a sharp upper bound on systematic portfolio variance and a corresponding bound for standardized returns. A weighted pairwise relaxation produces an objective that is convex under a checkable condition and requires marginal volatility scales but no cross-asset return covariances. With zero firm-specific slack, the common-map scale changes the certified variance reduction but not the normalized allocation, which depends only on observed information geometry. In a 52-firm panel from 2018-2022, an allocation constructed from Qwen3-Embedding-8B news representations lies between the 0.69th and 1.33rd in-sample variance percentiles across four prespecified capped portfolio populations; equal risk weighting lies between the 21.1st and 28.6th percentiles. The lower in-sample variance ranking relative to equal risk also appears across the reported frozen language-model representations. The framework therefore distribution-valued firm information into a coherent risk bound and an implementable allocation rule constructed without cross-asset return covariances. |
cite
@misc{gawronsky2026portfolio,
title={Portfolio Risk Bounds without Cross-Asset Return Covariances: Distributional Fields from Language-Model Representations},
author={Marcus Gawronsky and Chun-Sung Huang},
year={2026},
eprint={2608.29692},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.29692}
}