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

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 this purpose we use a powerful statistical tool, relatively new in the financial literature: the complexity-entropy causality plane. This representation space allows us 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. In particular, we detect the formation of two clusters, which correspond to the global categories of investment and speculative grades. Regarding 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.

Permutation entropy and statistical complexity analysis of turbulence in laboratory plasmas and the solar wind

“…Since Bandt and Pompe introduced their probability distribution based on ordinal patterns in arbitrary time series in 2002 [1], their methodology has found a wide variety of applications, from tracking the effects of anesthetic drugs on the brain [2][3][4] to informing economic policy [5][6][7] to various other areas [8][9][10][11][12]. In 2007, Rosso et al applied the ordinal pattern distribution of Bandt and Pompe to a time series analysis using the complexityentropy plane, or "CH plane", capable of differentiating between periodic, chaotic, and stochastic systems [13].…”

Monitoring the informational efficiency of European corporate bond markets with dynamical permutation min-entropy

“…A rolling window of 1024 datapoints, that corresponds47 to a period of four years approximately, has been initially fixed. This window length is selected because it coincides with 48 the political cycles in most countries.…”

Permutation Entropy and Statistical Complexity Analysis of Brazilian Agricultural Commodities

“…The complexity–entropy causality plane (CECP) was introduced by Rosso et al [ 43 ] as a tool to jointly quantify both information content and structural complexity in a temporal series. It was shown that CECP is useful for distinguishing between stochastic noise and deterministic chaotic behavior [ 43 ], leading to many applications in data analysis such as in physiology [ 44 ], physics [ 45 , 46 ], oceanography [ 47 ], ecology [ 48 ], hydrology [ 49 , 50 , 51 ], and finances [ 27 , 28 , 29 , 30 ]. In CECP representation, the horizontal axis is the Permutation entropy, and the vertical axis is a statistical complexity measure, also calculated using Bandt–Pompe probability distribution .…”

“…In this work, we analyze predictability of Brazilian agricultural commodity prices during the period after 2007/2008 food crisis. We use information theory based method of Permutation Entropy/Statistical complexity with its representation space called Complexity/Entropy causality plane, which was shown to be successful in the analysis of market efficiency and predictability [ 10 , 27 , 28 , 29 , 30 ].…”