Efficiency and credit ratings: a permutation-information-theory analysis

TL;DR

This study uses permutation-information-theory to analyze the relationship between credit ratings and informational efficiency of corporate bonds, revealing a strong correspondence with Moody's ratings and identifying distinct efficiency clusters.

Contribution

It introduces the complexity-entropy causality plane as a novel tool to classify bonds by informational efficiency, aligning with credit ratings and uncovering new efficiency subgroups.

Findings

Classification aligns with Moody's ratings

Two main clusters: investment and speculative grades

Efficiency does not correlate with firm characteristics

Abstract

The role of credit rating agencies has been under severe scrutiny after the subprime crisis. In this paper we explore the relationship between credit ratings and informational efficiency of a sample of thirty nine corporate bonds of US oil and energy companies from April 2008 to November 2012. For that purpose, we use a powerful statistical tool relatively new in the financial literature: the complexity-entropy causality plane. This representation space allows to graphically classify the different bonds according to their degree of informational efficiency. We find that this classification agrees with the credit ratings assigned by Moody's. Particularly, we detect the formation of two clusters, that correspond to the global categories of investment and speculative grades. Regarding to the latter cluster, two subgroups reflect distinct levels of efficiency. Additionally, we also find an intriguing absence of correlation between informational efficiency and firm characteristics. This allows us to conclude that the proposed permutation-information-theory approach provides an alternative practical way to justify bond classification.