Machine Learning-based Relative Valuation of Municipal Bonds

ArXiv ID: 2408.02273 “View on arXiv”

Authors: Unknown

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.

Keywords: CatBoost, Supervised similarity framework, Relative value, Back-testing, Municipal bonds, Fixed Income (Municipal Bonds)

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced machine learning mathematics, including supervised similarity learning with CatBoost, multi-output regression, and custom proximity metrics, while the methodology includes a deployed back-testing framework and specific performance comparisons against benchmarks, demonstrating high empirical rigor.

  flowchart TD
    A["Research Goal: Relative Valuation<br>of Municipal Bonds"] --> B["Data & Inputs<br>Large-scale Muni Bond Dataset"]
    
    B --> C{"Methodology: CatBoost Algorithm"}
    
    C --> D["Computational Process:<br>Learn Bond Similarities & Risk Profiles"]
    
    D --> E["Framework:<br>Supervised Similarity-based Relative Value"]
    
    E --> F["Evaluation:<br>Back-testing vs. Rule/Heuristic Methods"]
    
    F --> G{"Key Findings/Outcomes"}
    
    G --> H["Superior Performance:<br>CatBoost outperforms traditional benchmarks"]
    G --> I["Deployment:<br>Validated model for relative value assessment"]