Wasserstein-Barycentric Interaction Fields for Spatial Factor Models: Evidence from Language-Model Representations
Abstract
A language-model embedding field reconstructed via Wasserstein barycenters predicts peer-misalignment penalties more accurately than conventional weighting schemes.
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.
Community
While this work explores applications in Financial Ecometrics, it's bandwith-free barycentric operator offers a more general between modern language modelling and interprettable econometric and statistical techniques, by treating an entities' documents as empirical distributions and using methods from spatial regression to measure spatial interaction between semantic clouds. For machine learning and data science researchers and practitioners, this new insights into sentiment analysis, and other forecasting applications.
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