Machine Learning-based Relative Valuation of Municipal Bonds

TL;DR

This paper introduces a machine learning model using CatBoost to assess the relative value of municipal bonds by learning from large datasets, outperforming traditional rule-based methods.

Contribution

The paper presents a novel supervised similarity framework for muni bonds that leverages machine learning to improve relative valuation accuracy.

Findings

The ML-based method outperforms traditional heuristics in back-testing.

The model effectively captures complex relationships between bond features.

The approach provides a scalable solution for bond valuation.

Abstract

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.