Financial Bond Similarity Search Using Representation Learning
Statistical Finance
2026-02-10 v1 Machine Learning
Computational Finance
Portfolio Management
Abstract
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.
Keywords
Cite
@article{arxiv.2602.07020,
title = {Financial Bond Similarity Search Using Representation Learning},
author = {Amin Haeri and Mahdi Ghelichi and Nishant Agrawal and David Li and Catalina Gomez Sanchez},
journal= {arXiv preprint arXiv:2602.07020},
year = {2026}
}
Comments
22 pages, 18 figures, 1 table