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Wasserstein-Barycentric Interaction Fields for Spatial Factor Models: Evidence from Language-Model Representations

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  • Marcus Gawronsky
  • Chun-Sung Huang

Abstract

Spatial return models take the interaction matrix as given and leave feedback uninterpreted. We construct a bandwidth-free field from firms' language-model article embedding distributions using target-anchored Wasserstein barycentric reconstruction. A quadratic exposure-adjustment problem maps feedback into a peer-misalignment penalty ratio. For 52 firms, the field, frozen from 2018-2022 news, yields a 2023-2026 penalty ratio of 3.46 (95% interval [2.89, 4.17]) and higher conditional quasi-likelihood than equal-weighted peer support or RBF weighting of the same distances. Joint penalty ratios for the barycentric and news co-mention fields are 2.33 and 0.86 with boundary calibrated tests which reject both exclusions.

Suggested Citation

  • Marcus Gawronsky & Chun-Sung Huang, 2026. "Wasserstein-Barycentric Interaction Fields for Spatial Factor Models: Evidence from Language-Model Representations," Papers 2608.29669, arXiv.org.
    • Handle: RePEc:arx:papers:2608.29669
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