Supervised Similarity for High-Yield Corporate Bonds with Quantum Cognition Machine Learning

ArXiv ID: 2502.01495 “View on arXiv”

Authors: Unknown

Abstract

We investigate the application of quantum cognition machine learning (QCML), a novel paradigm for both supervised and unsupervised learning tasks rooted in the mathematical formalism of quantum theory, to distance metric learning in corporate bond markets. Compared to equities, corporate bonds are relatively illiquid and both trade and quote data in these securities are relatively sparse. Thus, a measure of distance/similarity among corporate bonds is particularly useful for a variety of practical applications in the trading of illiquid bonds, including the identification of similar tradable alternatives, pricing securities with relatively few recent quotes or trades, and explaining the predictions and performance of ML models based on their training data. Previous research has explored supervised similarity learning based on classical tree-based models in this context; here, we explore the application of the QCML paradigm for supervised distance metric learning in the same context, showing that it outperforms classical tree-based models in high-yield (HY) markets, while giving comparable or better performance (depending on the evaluation metric) in investment grade (IG) markets.

Keywords: Quantum Cognition Machine Learning, Distance Metric Learning, Corporate Bonds, Illiquidity, Similarity Measures, Bonds

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper introduces advanced quantum mathematics (Hilbert spaces, operators, fidelity) for bond similarity, indicating high mathematical complexity. It includes a detailed empirical methodology with backtesting on real bond data and quantitative performance comparisons against benchmarks, demonstrating high empirical rigor.

  flowchart TD
    A["Research Goal<br/>Develop supervised similarity<br/>for illiquid corporate bonds<br/>using Quantum Cognition ML (QCML)"] --> B["Input Data<br/>High-Yield & Investment Grade<br/>Corporate Bond Data"]
    B --> C["Methodology<br/>Compare QCML vs.<br/>Classical Tree-Based Models"]
    C --> D["Process<br/>Train Models & Learn<br/>Distance Metrics"]
    D --> E["Evaluation<br/>Performance on Trading &<br/>Pricing Tasks"]
    E --> F{"Key Findings & Outcomes"}
    F --> G["QCML Outperforms<br/>Classical Models in<br/>High-Yield Markets"]
    F --> H["QCML Achieves Comparable/<br/>Better Performance in<br/>Investment Grade Markets"]