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

TL;DR

This paper applies quantum cognition machine learning to improve distance metric learning in corporate bond markets, especially for high-yield bonds, outperforming classical models in certain market segments.

Contribution

It introduces the use of QCML for supervised similarity learning in bond markets, demonstrating superior performance over classical tree-based models in high-yield bonds.

Findings

QCML outperforms classical models in high-yield markets.

QCML achieves comparable or better results in investment grade markets.

Distance measures from QCML aid in bond trading and pricing.

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.