Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This paper introduces a novel approach to portfolio risk assessment that circumvents the need for cross-asset return covariances by leveraging firm-level distribution-valued characteristics. By utilizing multi-firm Wasserstein-2 dispersion, the authors derive sharp upper bounds on systematic portfolio variance and standardized returns, demonstrating that their method can outperform traditional equal risk weighting in terms of variance reduction. In empirical tests on a 52-firm panel, allocations based on language-model representations significantly improved in-sample variance rankings, showcasing the practical applicability of their framework.
Firm-level characteristics can yield reliable risk bounds without the notoriously difficult-to-estimate cross-asset return covariances, reshaping portfolio management strategies.
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.