Who Are a Firm's Economic Peers? Mapping a Latent Information Field with Language Models

Abstract

Identifying a firm's economic peers matters for benchmarking, risk measurement, and understanding return dependence, yet a diversified firm may have no single natural peer. We use language-model representations of corporate news to identify the combination of firms that jointly represents each firm's information environment. Each firm is represented by a distribution of article embeddings, and target-anchored Wasserstein barycentric reconstruction produces a directed map of economic-peer weights. We use this map as the interaction structure in a spatial factor model and ask whether return dependence follows peer relations measured from news without using returns. We construct peer weights from 2018-2022 news for 100 large-cap US traded firms and evaluate their financial relevance in subsequent returns. Relative to a matched model with the Fama-French five factors and momentum, the information field firms' unexpected returns move together after common factor exposures are accounted for. Joint reconstruction contributes substantially more than pairwise Wasserstein proximity or equal weighting of the selected peers, while centroid reconstruction retains most of the gain. Explicit news co-mentions provide complementary information. Language models thus make the interaction structure in spatial asset pricing an object of measurement: economically interpretable peer relations can be constructed from text before their financial relevance is assessed in returns.