Paper: arXiv 2408.02273

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.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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.

Research Flowchart

  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"]

Cited References (45)

  1. Can an unsupervised clustering algorithm reproduce a categorization system? — → 2408.10340
  2. Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals — → 2408.06679
  3. Open Set Recognition for Random Forest (2024) — link
  4. Company Similarity Using Large Language Models — → 2308.08031
  5. Quantifying Outlierness of Funds from their Categories using Supervised Similarity — → 2308.06882
  6. Do Municipal Bond Dealers Give their Customers “Fair and Reasonable” Pricing? (2023) — link
  7. How Trading Analytics and Data Science Can Improve Investment Outcomes (2022) — link
  8. Supervised similarity learning for corporate bonds using Random Forest proximities (2022) — link
and 37 more
  1. Geometry- and Accuracy-Preserving Random Forest Proximities (2022) — link
  2. Systematic Pricing and Trading of Municipal Bonds (2021) — link
  3. On Robustness of Mutual Funds Categorization and Distance Metric Learning (2021) — link
  4. Fast TreeSHAP: Accelerating SHAP Value Computation for Trees (2021) — link
  5. Fund2Vec: mutual funds similarity using graph learning (2021) — link
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  7. Municipal Bond Markets (2019) — link
  8. Optuna: A Next-generation Hyperparameter Optimization Framework (2019) — link
  9. A Comprehensive Analysis of Traditional Clustering Algorithms On Corporate Bond Data (2018) — link
  10. CatBoost: unbiased boosting with categorical features (2017)
  11. A Unified Approach to Interpreting Model Predictions (2017) — link
  12. Fundamental Analysis Works (2015) — link
  13. The Municipal Securities Rulemaking Board (2015) — link
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  34. ICAIF ’24, November 14–17, 2024, Brooklyn, NY, USA
  35. Machine Learning-based Relative Valuation of Municipal Bonds — → 2408.02273
    1. Towards Enhanced Local Explainability of Random Forests: A Proximity-Based Approach
  37. Relativevaluationwithmachinelearning

5 of 45 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.