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Machine Learning-based Relative Valuation of Municipal Bonds

Author

Listed:
  • Preetha Saha
  • Jingrao Lyu
  • Dhruv Desai
  • Rishab Chauhan
  • Jerinsh Jeyapaulraj
  • Philip Sommer
  • Dhagash Mehta

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.

Suggested Citation

  • Preetha Saha & Jingrao Lyu & Dhruv Desai & Rishab Chauhan & Jerinsh Jeyapaulraj & Philip Sommer & Dhagash Mehta, 2024. "Machine Learning-based Relative Valuation of Municipal Bonds," Papers 2408.02273, arXiv.org.
    • Handle: RePEc:arx:papers:2408.02273
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    References listed on IDEAS

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