超越共动：基于暴露的局部性支持用于投资组合多元化的联合因子-图框架
Beyond Co-Movement: Locality by Exposures Enables a Joint Factor-Graph Framework for Portfolio Diversification
- Imperial College London（帝国理工学院）
- Beaufort Bridges（博福特桥公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究针对现有投资组合构建方法的缺陷，提出MINGLE框架，通过ADMM联合学习因子表示与图拓扑，构建的投资组合表现优于传统方法。
AI中文摘要：
当前投资组合构建方法要么对异质冲击的影响不敏感（标准因子模型），要么对驱动系统性收益的潜在数据结构不敏感（近期基于图的方法）。这为结合因子领域和图领域捕捉的互补市场方面提供了机会，使资产配置能够直接作用于潜在市场结构，而非其观测到的共动或有限样本伪影。本研究中，我们引入了互信息图-局部性与暴露框架（MINGLE），该框架通过系统性因子暴露剖面而非观测到的共动来重新定义图局部性，从而对因子领域和图领域进行相互正则化。这通过统一的交替方向乘子法（ADMM）框架实现，该框架直接从市场收益中联合学习潜在因子表示及其诱导的图拓扑结构。由此产生的暴露相似性图与传统的基于相关性的图相比，与既定经济部门的对齐更紧密。结果表明，由该表示构建的投资组合在一系列波动制度和交易成本水平下，始终优于基于相关性的对应投资组合。为确保严谨性，配对统计检验证实，这些收益源于图领域与因子领域的协调。
英文摘要：
Current portfolio construction methods are either agnostic to the effects of idiosyncratic shocks (standard factor models) or to the latent data structure driving systematic returns (recent graph-based approaches). This presents an opportunity to combine the complementary market aspects captured by the factor and graph domains, allowing asset allocations to operate directly on the underlying market structure, rather than on its observed co-movement or its finite-sample artefacts. In this work, we introduce the Mutually-INformed Graph-Locality and Exposures framework (MINGLE), which mutually regularises the factor and graph domains by redefining graph locality through systematic factor exposure profiles, rather than via observed co-movements. This is formalised through a unified Alternating Direction Method of Multipliers (ADMM) framework that jointly learns a latent factor representation and its induced graph topology directly from market returns. The resulting exposure-similarity graph aligns more closely with established economic sectors than conventional correlation-based graphs. Portfolios constructed from this representation are shown to consistently outperform their correlation-based counterparts across a range of volatility regimes and transaction cost levels. For rigour, paired statistical testing confirms that these gains stem from the reconciliation of the graph and factor domains.
