  The trading ecosystem of the Municipal (muni) bond is complex and unique.
With nearly 2\% of securities from over a million securities outstanding
trading daily, determining the value or relative value of a bond among its
peers is challenging. Traditionally, relative value calculation has been done
using rule-based or heuristics-driven approaches, which may introduce human
biases and often fail to account for complex relationships between the bond
characteristics. We propose a data-driven model to develop a supervised
similarity framework for the muni bond market based on CatBoost algorithm. This
algorithm learns from a large-scale dataset to identify bonds that are similar
to each other based on their risk profiles. This allows us to evaluate the
price of a muni bond relative to a cohort of bonds with a similar risk profile.
We propose and deploy a back-testing methodology to compare various benchmarks
and the proposed methods and show that the similarity-based method outperforms
both rule-based and heuristic-based methods.
