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This study introduces a novel bandwidth-free field constructed from firms' language-model article embedding distributions, utilizing target-anchored Wasserstein barycentric reconstruction to interpret feedback in spatial return models. By addressing the limitations of traditional interaction matrices, the authors derive a peer-misalignment penalty ratio of 3.46 for 52 firms, demonstrating superior performance compared to equal-weighted peer support and RBF weighting methods. The findings suggest that the proposed method provides a more accurate representation of firm interactions and feedback dynamics over time, with implications for forecasting and strategic decision-making.
A new approach reveals a peer-misalignment penalty ratio of 3.46, outperforming traditional methods in capturing firm interaction dynamics.
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.