用于空间因子模型的Wasserstein重心相互作用场：来自语言模型表示的证据
Wasserstein-Barycentric Interaction Fields for Spatial Factor Models: Evidence from Language-Model Representations
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- University of Cape Town（开普敦大学）
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中文总结 AI 辅助
该研究针对空间收益模型未解释反馈的问题，提出目标锚定的Wasserstein重心相互作用场，经52家企业数据验证，其2023-2026年惩罚比率表现优于基准方法，联合检验拒绝排除假设。
中文摘要 AI 辅助
空间收益模型将交互矩阵视为给定条件，未对反馈进行解释。我们利用目标锚定的Wasserstein重心重构，从企业的语言模型文章嵌入分布中构建了一个无带宽的场。二次曝光调整问题将反馈映射为同行失配惩罚比率。针对52家企业，该场基于2018-2022年新闻冻结，得出2023-2026年惩罚比率为3.46（95%置信区间[2.89, 4.17]），且比等权同行支持或相同距离的RBF加权具有更高的条件拟似然。重心场与新闻共同提及场的联合惩罚比率分别为2.33和0.86，经边界校准检验后拒绝两者的排除假设。
英文摘要
Spatial asset-pricing models take the structure of inter-firm interaction as given. We infer that structure from firms' information environments using language-model representations. Each firm is represented as a distribution of news-article embeddings, and a target-anchored Wasserstein barycentric reconstruction selects, for every firm, the weighted combination of other firms whose information footprints jointly reconstruct its own. The resulting directed peer field enters a quadratic exposure-adjustment model in which the spatial coefficient indexes alignment with information peers relative to stand-alone exposure. Using fields built from 2018-2022 news and frozen before 2023-2026 returns, we find that the constructed field organizes cross-sectional return dependence beyond the Fama-French five factors and momentum and raises the held-out mean Gaussian quasi-log score relative to a matched factor-only model. Because factor betas are unchanged, the gain lies in residual covariance. The field outperforms pairwise distance weighting and equal weighting of the same peers, and remains incrementally informative beside persistent news co-mentions under the primary factor-conditioned specification. Linear and quadratic transport generate nearly identical peer-return signals and equivalent held-out predictive performance. The barycentric-proximity ordering persists across alternative embedding models, and a pre-period encoder preserves the held-out advantage under the primary specification. Language-model representations thus serve as a measurement instrument for latent inter-firm information structure in capital markets.
