Supervised similarity learning for corporate bonds using Random Forest proximities

TL;DR

This paper introduces a supervised similarity learning framework for corporate bonds using Random Forest proximities, addressing challenges in noisy financial data and enabling more effective local and global similarity measures for portfolio management.

Contribution

It demonstrates that Random Forest can be effectively used as a similarity metric learning tool and proposes a novel similarity evaluation metric for corporate bonds.

Findings

RF outperforms other methods in similarity tasks

The framework captures both local and global similarities

A new metric for evaluating similarity is proposed

Abstract

Financial literature consists of ample research on similarity and comparison of financial assets and securities such as stocks, bonds, mutual funds, etc. However, going beyond correlations or aggregate statistics has been arduous since financial datasets are noisy, lack useful features, have missing data and often lack ground truth or annotated labels. However, though similarity extrapolated from these traditional models heuristically may work well on an aggregate level, such as risk management when looking at large portfolios, they often fail when used for portfolio construction and trading which require a local and dynamic measure of similarity on top of global measure. In this paper we propose a supervised similarity framework for corporate bonds which allows for inference based on both local and global measures. From a machine learning perspective, this paper emphasis that random forest (RF), which is usually viewed as a supervised learning algorithm, can also be used as a similarity learning (more specifically, a distance metric learning) algorithm. In addition, this framework proposes a novel metric to evaluate similarities, and analyses other metrics which further demonstrate that RF outperforms all other methods experimented with, in this work.