用嵌入模型捕捉债券非财务属性相似性，提升风险建模效果
Financial Bond Similarity Search Using Representation Learning
- 通过嵌入模型学习债券的非财务属性语义相似性
- 在稀疏发行人增强下，显著提升曲线构建与风险建模性能
- 适合固定收益分析、信用风险建模方向的研究者
固定收益分析中寻找相似债券仍具挑战，因数值型金融属性常掩盖类别型非财务属性（如发行人行业、注册地）。本文表明，这些类别属性对利差曲线预测具有主导作用，并提出嵌入模型以捕捉其语义相似性，相比独热编码及其他基线方法表现更优。通过稀疏发行人增强评估，该方法有效提升了风险建模与曲线构建效果。
原文摘要 · Abstract (English)
Finding similar bonds remains challenging in fixed-income analytics, as numerical financial attributes often overshadow categorical non-financial ones such as issuer sector and domicile. This paper shows that these categorical attributes dominate the predictability of spread curves and proposes embedding models to capture their semantic similarities, outperforming one-hot and many other baselines. Evaluated via sparse-issuer augmentation, the approach improves risk modeling and curve construction.
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